Surgical systems often incorporate an imaging system, which can allow the clinician(s) to view the surgical site and/or one or more portions thereof on one or more displays such as a monitor, for example. The display(s) can be local and/or remote to a surgical theater. An imaging system can include a scope with a camera that views the surgical site and transmits the view to a display that is viewable by a clinician. Scopes include, but are not limited to, arthroscopes, angioscopes, bronchoscopes, choledochoscopes, colonoscopes, cytoscopes, duodenoscopes, enteroscopes, esophagogastro-duodenoscopes (gastroscopes), endoscopes, laryngoscopes, nasopharyngo-neproscopes, sigmoidoscopes, thoracoscopes, ureteroscopes, and exoscopes. Imaging systems can be limited by the information that they are able to recognize and/or convey to the clinician(s). For example, certain concealed structures, physical contours, and/or dimensions within a three-dimensional space may be unrecognizable intraoperatively by certain imaging systems. Additionally, certain imaging systems may be incapable of communicating and/or conveying certain information to the clinician(s) intraoperatively.
A surgical visualization system can comprise an emitter configured to emit a plurality of tissue-penetrating waveforms, a receiver configured to detect the plurality of tissue-penetrating waveforms, an imaging system comprising a display and a control circuit in signal communication with the receiver. The control circuit can be configured to receive data from the receiver representative of an image of a hidden portion of a surgical device and provide the image of the hidden portion of the surgical device to the display.
A surgical visualization system can comprise a hyperspectral camera comprising an emitter configured to emit a plurality of tissue-penetrating waveforms, and an image sensor configured to detect the plurality of tissue-penetrating waveforms. The surgical visualization system can further comprise a control circuit in signal communication with the hyperspectral camera, wherein the control circuit is configured to receive data representative of a position of a first critical structure from the plurality of tissue-penetrating waveforms detected by the image sensor, receive data representative of a position of a second critical structure from the plurality of tissue-penetrating waveforms detected by the image sensor, and determine a distance between the first critical structure and the second critical structure.
A non-transitory computer readable medium can store computer readable instructions which, when executed, causes a machine to receive data from an image sensor representative of a first image of a first hidden structure, provide the first image of the first hidden structure to a display, receive data from the image sensor representative of a second image of a second hidden structure, provide the second image of the second hidden structure to the display, and determine a distance between the first hidden structure and the second hidden structure.
The novel features of the various aspects are set forth with particularity in the appended claims. The described aspects, however, both as to organization and methods of operation, may be best understood by reference to the following description, taken in conjunction with the accompanying drawings in which:
Applicant of the present application also owns the following U.S. patent Applications, filed on Sep. 11, 2018, each of which is herein incorporated by reference in its entirety:
U.S. patent application Ser. No. 16/128,179, titled SURGICAL VISUALIZATION PLATFORM, now U.S. Patent Application Publication No. 2020/0015923;
U.S. patent application Ser. No. 16/128,191, titled SURGICAL VISUALIZATION CONTROLS, now U.S. Patent Application Publication No. 2020/0015904;
U.S. patent application Ser. No. 16/128,180, titled CONTROLLING AN EMITTER ASSEMBLY PULSE SEQUENCE, now U.S. Patent Application Publication No. 2020/0015900;
U.S. patent application Ser. No. 16/128,198, titled COMBINATION EMITTER AND CAMERA ASSEMBLY, now U.S. Patent Application Publication No. 2020/0015668;
U.S. patent application Ser. No. 16/128,207, titled SINGULAR EMR SOURCE WITH DUAL OUTPUT EMITTER ASSEMBLY, now U.S. Patent Application Publication No. 2020/0015925;
U.S. patent application Ser. No. 16/128,176, titled SURGICAL VISUALIZATION WITH PROXIMITY TRACKING FEATURES, now U.S. Patent Application Publication No. 2020/0015899;
U.S. patent application Ser. No. 16/128,192, titled VISUALIZATION OF SURGICAL DEVICES, now U.S. Patent Application Publication No. 2020/0015905;
U.S. patent application Ser. No. 16/128,163, titled OPERATIVE COMMUNICATION OF LIGHT, now U.S. Patent Application Publication No. 2020/0015897;
U.S. patent application Ser. No. 16/128,197, titled ROBOTIC LIGHT PROJECTION TOOLS, now U.S. Patent Application Publication No. 2020/0015924;
U.S. patent application Ser. No. 16/128,164, titled SURGICAL VISUALIZATION FEEDBACK SYSTEM, now U.S. Patent Application Publication No. 2020/0015898;
U.S. patent application Ser. No. 16/128,193, titled SURGICAL VISUALIZATION AND MONITORING, now U.S. Patent Application Publication No. 2020/0015906;
U.S. patent application Ser. No. 16/128,195, titled INTEGRATION OF IMAGING DATA, now U.S. Patent Application Publication No. 2020/0015907;
U.S. patent application Ser. No. 16/128,170, titled ROBOTICALLY-ASSISTED SURGICAL SUTURING SYSTEMS, now U.S. Patent Application Publication No. 2020/0015806;
U.S. patent application Ser. No. 16/128,183, titled SAFETY LOGIC FOR SURGICAL SUTURING SYSTEMS, now U.S. Patent Application Publication No. 2020/0015901;
U.S. patent application Ser. No. 16/128,172, titled ROBOTIC SYSTEM WITH SEPARATE PHOTOACOUSTIC RECEIVER, now U.S. Patent Application Publication No. 2020/0015914; and
U.S. patent application Ser. No. 16/128,185, titled FORCE SENSOR THROUGH STRUCTURED LIGHT DEFLECTION, now U.S. Patent Application Publication No. 2020/0015902.
Applicant of the present application also owns U.S. Pat. No. 9,072,535, titled SURGICAL STAPLING INSTRUMENTS WITH ROTATABLE STAPLE DEPLOYMENT ARRANGEMENTS, issued Jul. 7, 2015, which is incorporated by reference herein in its entirety.
Applicant of the present application also owns U.S. Provisional Patent Application No. 62/611,339, titled ROBOT ASSISTED SURGICAL PLATFORM, filed Dec. 28, 2017, which is incorporated by reference herein in its entirety.
Applicant of the present application also owns the following U.S. Patent Applications, filed on Mar. 29, 2018, each of which is herein incorporated by reference in its entirety:
Before explaining various aspects of a surgical visualization platform in detail, it should be noted that the illustrative examples are not limited in application or use to the details of construction and arrangement of parts illustrated in the accompanying drawings and description. The illustrative examples may be implemented or incorporated in other aspects, variations, and modifications, and may be practiced or carried out in various ways. Further, unless otherwise indicated, the terms and expressions employed herein have been chosen for the purpose of describing the illustrative examples for the convenience of the reader and are not for the purpose of limitation thereof. Also, it will be appreciated that one or more of the following-described aspects, expressions of aspects, and/or examples, can be combined with any one or more of the other following-described aspects, expressions of aspects, and/or examples.
The present disclosure is directed to a surgical visualization platform that leverages “digital surgery” to obtain additional information about a patient's anatomy and/or a surgical procedure. The surgical visualization platform is further configured to convey data and/or information to one or more clinicians in a helpful manner. For example, various aspects of the present disclosure provide improved visualization of the patient's anatomy and/or the surgical procedure.
“Digital surgery” can embrace robotic systems, advanced imaging, advanced instrumentation, artificial intelligence, machine learning, data analytics for performance tracking and benchmarking, connectivity both inside and outside of the operating room (OR), and more. Although various surgical visualization platforms described herein can be used in combination with a robotic surgical system, surgical visualization platforms are not limited to use with a robotic surgical system. In certain instances, advanced surgical visualization can occur without robotics and/or with limited and/or optional robotic assistance. Similarly, digital surgery can occur without robotics and/or with limited and/or optional robotic assistance.
In certain instances, a surgical system that incorporates a surgical visualization platform may enable smart dissection in order to identify and avoid critical structures. Critical structures include anatomical structures such as a ureter, an artery such as a superior mesenteric artery, a vein such as a portal vein, a nerve such as a phrenic nerve, and/or a tumor, among other anatomical structures. In other instances, a critical structure can be a foreign structure in the anatomical field, such as a surgical device, surgical fastener, clip, tack, bougie, band, and/or plate, for example. Critical structures can be determined on a patient-by-patient and/or a procedure-by-procedure basis. Example critical structures are further described herein. Smart dissection technology may provide improved intraoperative guidance for dissection and/or can enable smarter decisions with critical anatomy detection and avoidance technology, for example.
A surgical system incorporating a surgical visualization platform may also enable smart anastomosis technologies that provide more consistent anastomoses at optimal location(s) with improved workflow. Cancer localization technologies may also be improved with the various surgical visualization platforms and procedures described herein. For example, cancer localization technologies can identify and track a cancer location, orientation, and its margins. In certain instances, the cancer localizations technologies may compensate for movement of a tool, a patient, and/or the patient's anatomy during a surgical procedure in order to provide guidance back to the point of interest for the clinician.
In certain aspects of the present disclosure, a surgical visualization platform may provide improved tissue characterization and/or lymph node diagnostics and mapping. For example, tissue characterization technologies may characterize tissue type and health without the need for physical haptics, especially when dissecting and/or placing stapling devices within the tissue. Certain tissue characterization technologies described herein may be utilized without ionizing radiation and/or contrast agents. With respect to lymph node diagnostics and mapping, a surgical visualization platform may preoperatively locate, map, and ideally diagnose the lymph system and/or lymph nodes involved in cancerous diagnosis and staging, for example.
These and other related topics are described herein and/or in the aforementioned contemporaneously-filed U.S. Patent Applications, which are incorporated by reference herein in their respective entireties.
During a surgical procedure, the information available to the clinician via the “naked eye” and/or an imaging system may provide an incomplete view of the surgical site. For example, certain structures, such as structures embedded or buried within an organ, can be at least partially concealed or hidden from view. Additionally, certain dimensions and/or relative distances can be difficult to ascertain with existing sensor systems and/or difficult for the “naked eye” to perceive. Moreover, certain structures can move preoperatively (e.g. before a surgical procedure but after a preoperative scan) and/or intraoperatively. In such instances, the clinician can be unable to accurately determine the location of a critical structure intraoperatively.
When the position of a critical structure is uncertain and/or when the proximity between the critical structure and a surgical tool is unknown, a clinician's decision-making process can be inhibited. For example, a clinician may avoid certain areas in order to avoid inadvertent dissection of a critical structure; however, the avoided area may be unnecessarily large and/or at least partially misplaced. Due to uncertainty and/or overly/excessive exercises in caution, the clinician may not access certain desired regions. For example, excess caution may cause a clinician to leave a portion of a tumor and/or other undesirable tissue in an effort to avoid a critical structure even if the critical structure is not in the particular area and/or would not be negatively impacted by the clinician working in that particular area. In certain instances, surgical results can be improved with increased knowledge and/or certainty, which can allow a surgeon to be more accurate and, in certain instances, less conservative/more aggressive with respect to particular anatomical areas.
In various aspects, the present disclosure provides a surgical visualization system for intraoperative identification and avoidance of critical structures. In one aspect, the present disclosure provides a surgical visualization system that enables enhanced intraoperative decision making and improved surgical outcomes. In various aspects, the disclosed surgical visualization system provides advanced visualization capabilities beyond what a clinician sees with the “naked eye” and/or beyond what an imaging system can recognize and/or convey to the clinician. The various surgical visualization systems can augment and enhance what a clinician is able to know prior to tissue treatment (e.g. dissection) and, thus, may improve outcomes in various instances.
For example, a visualization system can include a first light emitter configured to emit a plurality of spectral waves, a second light emitter configured to emit a light pattern, and one or more receivers, or sensors, configured to detect visible light, molecular responses to the spectral waves (spectral imaging), and/or the light pattern. The surgical visualization system can also include an imaging system and a control circuit in signal communication with the receiver(s) and the imaging system. Based on output from the receiver(s), the control circuit can determine a geometric surface map, i.e. three-dimensional surface topography, of the visible surfaces at the surgical site and one or more distances with respect to the surgical site. In certain instances, the control circuit can determine one more distances to an at least partially concealed structure. Moreover, the imaging system can convey the geometric surface map and the one or more distances to a clinician. In such instances, an augmented view of the surgical site provided to the clinician can provide a representation of the concealed structure within the relevant context of the surgical site. For example, the imaging system can virtually augment the concealed structure on the geometric surface map of the concealing and/or obstructing tissue similar to a line drawn on the ground to indicate a utility line below the surface. Additionally or alternatively, the imaging system can convey the proximity of one or more surgical tools to the visible and obstructing tissue and/or to the at least partially concealed structure and/or the depth of the concealed structure below the visible surface of the obstructing tissue. For example, the visualization system can determine a distance with respect to the augmented line on the surface of the visible tissue and convey the distance to the imaging system.
In various aspects of the present disclosure, a surgical visualization system is disclosed for intraoperative identification and avoidance of critical structures. Such a surgical visualization system can provide valuable information to a clinician during a surgical procedure. As a result, the clinician can confidently maintain momentum throughout the surgical procedure knowing that the surgical visualization system is tracking a critical structure such as a ureter, specific nerves, and/or critical blood vessels, for example, which may be approached during dissection, for example. In one aspect, the surgical visualization system can provide an indication to the clinician in sufficient time for the clinician to pause and/or slow down the surgical procedure and evaluate the proximity to the critical structure to prevent inadvertent damage thereto. The surgical visualization system can provide an ideal, optimized, and/or customizable amount of information to the clinician to allow the clinician to move confidently and/or quickly through tissue while avoiding inadvertent damage to healthy tissue and/or critical structure(s) and, thus, to minimize the risk of harm resulting from the surgical procedure.
The surgical visualization system 100 incorporates tissue identification and geometric surface mapping in combination with a distance sensor system 104. In combination, these features of the surgical visualization system 100 can determine a position of a critical structure 101 within the anatomical field and/or the proximity of a surgical device 102 to the surface 105 of the visible tissue and/or to the critical structure 101. Moreover, the surgical visualization system 100 includes an imaging system that includes an imaging device 120, such as a camera, for example, configured to provide real-time views of the surgical site. In various instances, the imaging device 120 is a spectral camera (e.g. a hyperspectral camera, multispectral camera, or selective spectral camera), which is configured to detect reflected spectral waveforms and generate a spectral cube of images based on the molecular response to the different wavelengths. Views from the imaging device 120 can be provided to a clinician and, in various aspects of the present disclosure, can be augmented with additional information based on the tissue identification, landscape mapping, and the distance sensor system 104. In such instances, the surgical visualization system 100 includes a plurality of subsystems—an imaging subsystem, a surface mapping subsystem, a tissue identification subsystem, and/or a distance determining subsystem. These subsystems can cooperate to intraoperatively provide advanced data synthesis and integrated information to the clinician(s).
The imaging device can include a camera or imaging sensor that is configured to detect visible light, spectral light waves (visible or invisible), and a structured light pattern (visible or invisible), for example. In various aspects of the present disclosure, the imaging system can include an imaging device such as an endoscope, for example. Additionally or alternatively, the imaging system can include an imaging device such as an arthroscope, angioscope, bronchoscope, choledochoscope, colonoscope, cytoscope, duodenoscope, enteroscope, esophagogastro-duodenoscope (gastroscope), laryngoscope, nasopharyngo-neproscope, sigmoidoscope, thoracoscope, ureteroscope, or exoscope, for example. In other instances, such as in open surgery applications, the imaging system may not include a scope.
In various aspects of the present disclosure, the tissue identification subsystem can be achieved with a spectral imaging system. The spectral imaging system can rely on hyperspectral imaging, multispectral imaging, or selective spectral imaging, for example. Hyperspectral imaging of tissue is further described in U.S. Pat. No. 9,274,047, titled SYSTEM AND METHOD FOR GROSS ANATOMIC PATHOLOGY USING HYPERSPECTRAL IMAGING, issued Mar. 1, 2016, which is incorporated by reference herein in its entirety.
In various aspect of the present disclosure, the surface mapping subsystem can be achieved with a light pattern system, as further described herein. The use of a light pattern (or structured light) for surface mapping is known. Known surface mapping techniques can be utilized in the surgical visualization systems described herein.
Structured light is the process of projecting a known pattern (often a grid or horizontal bars) on to a surface. U.S. Patent Application Publication No. 2017/0055819, titled SET COMPRISING A SURGICAL INSTRUMENT, published Mar. 2, 2017, and U.S. Patent Application Publication No. 2017/0251900, titled DEPICTION SYSTEM, published Sep. 7, 2017, disclose a surgical system comprising a light source and a projector for projecting a light pattern. U.S. Patent Application Publication No. 2017/0055819, titled SET COMPRISING A SURGICAL INSTRUMENT, published Mar. 2, 2017, and U.S. Patent Application Publication No. 2017/0251900, titled DEPICTION SYSTEM, published Sep. 7, 2017, are incorporated by reference herein in their respective entireties.
In various aspects of the present disclosure, the distance determining system can be incorporated into the surface mapping system. For example, structured light can be utilized to generate a three-dimensional virtual model of the visible surface and determine various distances with respect to the visible surface. Additionally or alternatively, the distance determining system can rely on time-of-flight measurements to determine one or more distances to the identified tissue (or other structures) at the surgical site.
In various aspects, the heart of the camera 144 is the image sensor 135. Generally, modern image sensors 135 are solid-state electronic devices containing up to millions of discrete photodetector sites called pixels. The image sensor 135 technology falls into one of two categories: Charge-Coupled Device (CCD) and Complementary Metal Oxide Semiconductor (CMOS) imagers and more recently, short-wave infrared (SWIR) is an emerging technology in imaging. Another type of image sensor 135 employs a hybrid CCD/CMOS architecture (sold under the name “sCMOS”) and consists of CMOS readout integrated circuits (ROICs) that are bump bonded to a CCD imaging substrate. CCD and CMOS image sensors 135 are sensitive to wavelengths from approximately 350-1050 nm, although the range is usually given from 400-1000 nm. CMOS sensors are, in general, more sensitive to IR wavelengths than CCD sensors. Solid state image sensors 135 are based on the photoelectric effect and, as a result, cannot distinguish between colors. Accordingly, there are two types of color CCD cameras: single chip and three-chip. Single chip color CCD cameras offer a common, low-cost imaging solution and use a mosaic (e.g. Bayer) optical filter to separate incoming light into a series of colors and employ an interpolation algorithm to resolve full color images. Each color is, then, directed to a different set of pixels. Three-chip color CCD cameras provide higher resolution by employing a prism to direct each section of the incident spectrum to a different chip. More accurate color reproduction is possible, as each point in space of the object has separate RGB intensity values, rather than using an algorithm to determine the color. Three-chip cameras offer extremely high resolutions.
The control system 133 also includes a spectral light source 150 and a structured light source 152. In certain instances, a single source can be pulsed to emit wavelengths of light in the spectral light source 150 range and wavelengths of light in the structured light source 152 range. Alternatively, a single light source can be pulsed to provide light in the invisible spectrum (e.g. infrared spectral light) and wavelengths of light on the visible spectrum. The spectral light source 150 can be a hyperspectral light source, a multispectral light source, and/or a selective spectral light source, for example. In various instances, the tissue identification logic 140 can identify critical structure(s) via data from the spectral light source 150 received by the image sensor 135 portion of the camera 144. The surface mapping logic 136 can determine the surface contours of the visible tissue based on reflected structured light. With time-of-flight measurements, the distance determining logic 141 can determine one or more distance(s) to the visible tissue and/or the critical structure 101. One or more outputs from the surface mapping logic 136, the tissue identification logic 140, and the distance determining logic 141, can be provided to the imaging logic 138, and combined, blended, and/or overlaid to be conveyed to a clinician via the display 146 of the imaging system 142.
The description now turns briefly to
Referring again to the surgical visualization system 100 in
In one aspect, the critical structure 101 may be embedded in tissue 103. Stated differently, the critical structure 101 may be positioned below the surface 105 of the tissue 103. In such instances, the tissue 103 conceals the critical structure 101 from the clinician's view. The critical structure 101 is also obscured from the view of the imaging device 120 by the tissue 103. The tissue 103 can be fat, connective tissue, adhesions, and/or organs, for example. In other instances, the critical structure 101 can be partially obscured from view.
The imaging device 120 of the surgical visualization system 100 is configured to detect light at various wavelengths, such as, for example, visible light, spectral light waves (visible or invisible), and a structured light pattern (visible or invisible). The imaging device 120 may include a plurality of lenses, sensors, and/or receivers for detecting the different signals. For example, the imaging device 120 can be a hyperspectral, multispectral, or selective spectral camera, as further described herein. The imaging device 120 can also include a waveform sensor 122 (such as a spectral image sensor, detector, and/or three-dimensional camera lens). For example, the imaging device 120 can include a right-side lens and a left-side lens used together to record two two-dimensional images at the same time and, thus, generate a three-dimensional image of the surgical site, render a three-dimensional image of the surgical site, and/or determine one or more distances at the surgical site. Additionally or alternatively, the imaging device 120 can be configured to receive images indicative of the topography of the visible tissue and the identification and position of hidden critical structures, as further described herein. For example, the field of view of the imaging device 120 can overlap with a pattern of light (structured light) on the surface 105 of the tissue, as shown in
In one aspect, the surgical visualization system 100 may be incorporated into a robotic system 110. For example, the robotic system 110 may include a first robotic arm 112 and a second robotic arm 114. The robotic arms 112, 114 include rigid structural members 116 and joints 118, which can include servomotor controls. The first robotic arm 112 is configured to maneuver the surgical device 102, and the second robotic arm 114 is configured to maneuver the imaging device 120. A robotic control unit can be configured to issue control motions to the robotic arms 112, 114, which can affect the surgical device 102 and the imaging device 120, for example.
The surgical visualization system 100 also includes an emitter 106, which is configured to emit a pattern of light, such as stripes, grid lines, and/or dots, to enable the determination of the topography or landscape of the surface 105. For example, projected light arrays 130 can be used for three-dimensional scanning and registration on the surface 105. The projected light arrays 130 can be emitted from the emitter 106 located on the surgical device 102 and/or one of the robotic arms 112, 114 and/or the imaging device 120, for example. In one aspect, the projected light array 130 is employed to determine the shape defined by the surface 105 of the tissue 103 and/or the motion of the surface 105 intraoperatively. The imaging device 120 is configured to detect the projected light arrays 130 reflected from the surface 105 to determine the topography of the surface 105 and various distances with respect to the surface 105.
In one aspect, the imaging device 120 also may include an optical waveform emitter 123 that is configured to emit electromagnetic radiation 124 (NIR photons) that can penetrate the surface 105 of the tissue 103 and reach the critical structure 101. The imaging device 120 and the optical waveform emitter 123 thereon can be positionable by the robotic arm 114. A corresponding waveform sensor 122 (an image sensor, spectrometer, or vibrational sensor, for example) on the imaging device 120 is configured to detect the effect of the electromagnetic radiation received by the waveform sensor 122. The wavelengths of the electromagnetic radiation 124 emitted by the optical waveform emitter 123 can be configured to enable the identification of the type of anatomical and/or physical structure, such as the critical structure 101. The identification of the critical structure 101 can be accomplished through spectral analysis, photo-acoustics, and/or ultrasound, for example. In one aspect, the wavelengths of the electromagnetic radiation 124 may be variable. The waveform sensor 122 and optical waveform emitter 123 may be inclusive of a multispectral imaging system and/or a selective spectral imaging system, for example. In other instances, the waveform sensor 122 and optical waveform emitter 123 may be inclusive of a photoacoustic imaging system, for example. In other instances, the optical waveform emitter 123 can be positioned on a separate surgical device from the imaging device 120.
The surgical visualization system 100 also may include the distance sensor system 104 configured to determine one or more distances at the surgical site. In one aspect, the time-of-flight distance sensor system 104 may be a time-of-flight distance sensor system that includes an emitter, such as the emitter 106, and a receiver 108, which can be positioned on the surgical device 102. In other instances, the time-of-flight emitter can be separate from the structured light emitter. In one general aspect, the emitter 106 portion of the time-of-flight distance sensor system 104 may include a very tiny laser source and the receiver 108 portion of the time-of-flight distance sensor system 104 may include a matching sensor. The time-of-flight distance sensor system 104 can detect the “time of flight,” or how long the laser light emitted by the emitter 106 has taken to bounce back to the sensor portion of the receiver 108. Use of a very narrow light source in the emitter 106 enables the distance sensor system 104 to determining the distance to the surface 105 of the tissue 103 directly in front of the distance sensor system 104. Referring still to
In various instances, the receiver 108 for the time-of-flight distance sensor system 104 can be mounted on a separate surgical device instead of the surgical device 102. For example, the receiver 108 can be mounted on a cannula or trocar through which the surgical device 102 extends to reach the surgical site. In still other instances, the receiver 108 for the time-of-flight distance sensor system 104 can be mounted on a separate robotically-controlled arm (e.g. the robotic arm 114), on a movable arm that is operated by another robot, and/or to an operating room (OR) table or fixture. In certain instances, the imaging device 120 includes the time-of-flight receiver 108 to determine the distance from the emitter 106 to the surface 105 of the tissue 103 using a line between the emitter 106 on the surgical device 102 and the imaging device 120. For example, the distance de can be triangulated based on known positions of the emitter 106 (on the surgical device 102) and the receiver 108 (on the imaging device 120) of the time-of-flight distance sensor system 104. The three-dimensional position of the receiver 108 can be known and/or registered to the robot coordinate plane intraoperatively.
In certain instances, the position of the emitter 106 of the time-of-flight distance sensor system 104 can be controlled by the first robotic arm 112 and the position of the receiver 108 of the time-of-flight distance sensor system 104 can be controlled by the second robotic arm 114. In other instances, the surgical visualization system 100 can be utilized apart from a robotic system. In such instances, the distance sensor system 104 can be independent of the robotic system.
In certain instances, one or more of the robotic arms 112, 114 may be separate from a main robotic system used in the surgical procedure. At least one of the robotic arms 112, 114 can be positioned and registered to a particular coordinate system without a servomotor control. For example, a closed-loop control system and/or a plurality of sensors for the robotic arms 110 can control and/or register the position of the robotic arm(s) 112, 114 relative to the particular coordinate system. Similarly, the position of the surgical device 102 and the imaging device 120 can be registered relative to a particular coordinate system.
Referring still to
Additionally or alternatively, time-of-flight from the optical waveform emitter 123 can be configured to determine the distance from the optical waveform emitter 123 to the surface 105 of the tissue 103. For example, a first waveform (or range of waveforms) can be utilized to determine the camera-to-critical structure distance dw and a second waveform (or range of waveforms) can be utilized to determine the distance to the surface 105 of the tissue 103. In such instances, the different waveforms can be utilized to determine the depth of the critical structure 101 below the surface 105 of the tissue 103.
Additionally or alternatively, in certain instances, the distance dA can be determined from an ultrasound, a registered magnetic resonance imaging (MRI) or computerized tomography (CT) scan. In still other instances, the distance dA can be determined with spectral imaging because the detection signal received by the imaging device can vary based on the type of material. For example, fat can decrease the detection signal in a first way, or a first amount, and collagen can decrease the detection signal in a different, second way, or a second amount.
Referring now to a surgical visualization system 160 in
dA=dw−dt.
As disclosed herein, various information regarding visible tissue, embedded critical structures, and surgical devices can be determined by utilizing a combination approach that incorporates one or more time-of-flight distance sensors, spectral imaging, and/or structured light arrays in combination with an image sensor configured to detect the spectral wavelengths and the structured light arrays. Moreover, the image sensor can be configured to receive visible light and, thus, provide images of the surgical site to an imaging system. Logic or algorithms are employed to discern the information received from the time-of-flight sensors, spectral wavelengths, structured light, and visible light and render three-dimensional images of the surface tissue and underlying anatomical structures. In various instances, the imaging device 120 can include multiple image sensors.
The camera-to-critical structure distance dw can also be detected in one or more alternative ways. In one aspect, a fluoroscopy visualization technology, such as fluorescent indosciedine green (ICG), for example, can be utilized to illuminate a critical structure 201, as shown in
In still other aspects, the surgical visualization system 100 may employ two separate waveform receivers (i.e. cameras/image sensors) to determine dw. Referring now to
In another aspect, referring now to
The surgical visualization system 500 is configured to determine an emitter-to-tissue distance de from an emitter 506 on the surgical device 502 to a surface 505 of the uterus 503 via structured light. The surgical visualization system 500 is configured to extrapolate a device-to-tissue distance dt from the surgical device 502 to the surface 505 of the uterus 503 based on the emitter-to-tissue distance de. The surgical visualization system 500 is also configured to determine a tissue-to-ureter distance dA from the ureter 501a to the surface 505 and a camera-to ureter distance dw from the imaging device 520 to the ureter 501a. As described herein with respect to
Referring now to
The control system 600 is configured for implementing a hyperspectral imaging and visualization system in which a molecular response is utilized to detect and identify anatomy in a surgical field of view. The control system 600 includes a conversion logic circuit 648 to convert tissue data to surgeon usable information. For example, the variable reflectance based on wavelengths with respect to obscuring material can be utilized to identify the critical structure in the anatomy. Moreover, the control system 600 combines the identified spectral signature and the structural light data in an image. For example, the control system 600 can be employed to create of three-dimensional data set for surgical use in a system with augmentation image overlays. Techniques can be employed both intraoperatively and preoperatively using additional visual information. In various instances, the control system 600 is configured to provide warnings to a clinician when in the proximity of one or more critical structures. Various algorithms can be employed to guide robotic automation and semi-automated approaches based on the surgical procedure and proximity to the critical structure(s).
A projected array of lights is employed to determine tissue shape and motion intraoperatively. Alternatively, flash Lidar may be utilized for surface mapping of the tissue.
The control system 600 is configured to detect the critical structure(s) and provide an image overlay of the critical structure and measure the distance to the surface of the visible tissue and the distance to the embedded/buried critical structure(s). In other instances, the control system 600 can measure the distance to the surface of the visible tissue or detect the critical structure(s) and provide an image overlay of the critical structure.
The control system 600 includes a spectral control circuit 602. The spectral control circuit 602 can be a field programmable gate array (FPGA) or another suitable circuit configuration as described herein in connection with
The video input processor 606 is coupled to a camera 612 at the patient side via a patient isolation circuit 614. As previously discussed, the camera 612 includes a solid state image sensor 634. The patient isolation circuit can include a plurality of transformers so that the patient is isolated from other circuits in the system. The camera 612 receives intraoperative images through optics 632 and the image sensor 634. The image sensor 634 can include a CMOS image sensor, for example, or may include any of the image sensor technologies discussed herein in connection with
The laser pulsing control circuit 622 controls a laser light engine 624. The laser light engine 624 outputs light in a plurality of wavelengths (λ1, λ2, λ3 . . . λn) including near infrared (NIR). The laser light engine 624 can operate in a plurality of modes. In one aspect, the laser light engine 624 can operate in two modes, for example. In a first mode, e.g. a normal operating mode, the laser light engine 624 outputs an illuminating signal. In a second mode, e.g. an identification mode, the laser light engine 624 outputs RGBG and NIR light. In various instances, the laser light engine 624 can operate in a polarizing mode.
Light output 626 from the laser light engine 624 illuminates targeted anatomy in an intraoperative surgical site 627. The laser pulsing control circuit 622 also controls a laser pulse controller 628 for a laser pattern projector 630 that projects a laser light pattern 631, such as a grid or pattern of lines and/or dots, at a predetermined wavelength (λ2) on the operative tissue or organ at the surgical site 627. The camera 612 receives the patterned light as well as the reflected light output through the camera optics 632. The image sensor 634 converts the received light into a digital signal.
The color RGB fusion circuit 616 also outputs signals to the image overlay controller 610 and a video input module 636 for reading the laser light pattern 631 projected onto the targeted anatomy at the surgical site 627 by the laser pattern projector 630. A processing module 638 processes the laser light pattern 631 and outputs a first video output signal 640 representative of the distance to the visible tissue at the surgical site 627. The data is provided to the image overlay controller 610. The processing module 638 also outputs a second video signal 642 representative of a three-dimensional rendered shape of the tissue or organ of the targeted anatomy at the surgical site.
The first and second video output signals 640, 642 include data representative of the position of the critical structure on a three-dimensional surface model, which is provided to an integration module 643. In combination with data from the video out processor 608 of the spectral control circuit 602, the integration module 643 can determine the distance dA (
Preoperative data 650 from a CT or MRI scan can be employed to register or align certain three-dimensional deformable tissue in various instances. Such preoperative data 650 can be provided to the integration module 643 and ultimately to the image overlay controller 610 so that such information can be overlaid with the views from the camera 612 and provided to the video monitors 652. Registration of preoperative data is further described herein and in the aforementioned contemporaneously-filed U.S. Patent Applications, including U.S. Patent Application Ser. No. 16/128,195, titled INTEGRATION OF IMAGING DATA, for example, which are incorporated by reference herein in their respective entireties.
The video monitors 652 can output the integrated/augmented views from the image overlay controller 610. A clinician can select and/or toggle between different views on one or more monitors. On a first monitor 652a, the clinician can toggle between (A) a view in which a three-dimensional rendering of the visible tissue is depicted and (B) an augmented view in which one or more hidden critical structures are depicted over the three-dimensional rendering of the visible tissue. On a second monitor 652b, the clinician can toggle on distance measurements to one or more hidden critical structures and/or the surface of visible tissue, for example.
The control system 600 and/or various control circuits thereof can be incorporated into various surgical visualization systems disclosed herein.
In certain instances, invisible (or imperceptible) structured light can be utilized, in which the structured light is used without interfering with other computer vision tasks for which the projected pattern may be confusing. For example, infrared light or extremely fast frame rates of visible light that alternate between two exact opposite patterns can be utilized to prevent interference. Structured light is further described at en.wikipedia.org.
Referring now to
Also by way example to illustrate the concept of hyperspectral imaging,
The hyperspectral imaging concepts described in connection with
TABLE 1 specifies wavelengths and bands of absorption for various materials.
Referring now to
In various instances, hyperspectral imaging technology, as described herein for illustrative purposes with respect to terrestrial features and objects and a fried egg, can be employed to identify signatures in anatomical structures in order to differentiate a critical structure from obscurants. Hyperspectral imaging technology may provide a visualization system that can provide a way to identify critical structures such as ureters and/or blood vessels, for example, especially when those structures are obscured by fat, connective tissue, blood, or other organs, for example. The use of the difference in reflectance of different wavelengths in the infrared (IR) spectrum may be employed to determine the presence of key structures versus obscurants. Referring now to
In various instances, select wavelengths for spectral imaging can be identified and utilized based on the anticipated critical structures and/or obscurants at a surgical site (i.e. “selective spectral” imaging). By utilizing selective spectral imaging, the amount of time required to obtain the spectral image can be minimized such that the information can be obtained in real-time, or near real-time, and utilized intraoperatively. In various instances, the wavelengths can be selected by a clinician or by a control circuit based on input by the clinician. In certain instances, the wavelengths can be selected based on machine learning and/or big data accessible to the control circuit via a cloud, for example.
The foregoing application of spectral imaging to tissue can be utilized intraoperatively to measure the distance between a waveform emitter and a critical structure that is obscured by tissue. In one aspect of the present disclosure, referring now to
The waveforms 1124, 1125 are configured to penetrate obscuring tissue 1103. For example, the wavelengths of the waveforms 1124, 1125 can be in the NIR or SWIR spectrum of wavelengths. In one aspect, a spectral signal (e.g. hyperspectral, multispectral, or selective spectral) or a photoacoustic signal can be emitted from the emitter 1106 and can penetrate the tissue 1103 in which the critical structure 1101 is concealed. The emitted waveform 1124 can be reflected by the critical structure 1101. The received waveform 1125 can be delayed due to the distance d between the distal end of the surgical device 1102 and the critical structure 1101. In various instances, the waveforms 1124, 1125 can be selected to target the critical structure 1101 within the tissue 1103 based on the spectral signature of the critical structure 1101, as further described herein. In various instances, the emitter 1106 is configured to provide a binary signal on and off, as shown in
Based on the delay between the emitted wave 1124 and the received wave 1125, the time-of-flight sensor system 1104 is configured to determine the distance d (
where:
c=the speed of light;
t=length of pulse;
q1=accumulated charge while light is emitted; and
q2=accumulated charge while light is not being emitted.
As provided herein, the time-of-flight of the waveforms 1124, 1125 corresponds to the distance din
dA=dw−dt.
where:
dA=the depth of the critical structure 1101;
dw=the distance from the emitter 1106 to the critical structure 1101 (din
dt=the distance from the emitter 1106 (on the distal end of the surgical device 1102) to the surface 1105 of the obscuring tissue 1103.
In one aspect of the present disclosure, referring now to
The different emitted waves 1224a, 1224b, 1224c are configured to target different types of material at the surgical site. For example, the wave 1224a targets the obscuring tissue 1203, the wave 1224b targets a first critical structure 1201a (e.g. a vessel), and the wave 1224c targets a second critical structure 1201b (e.g. a cancerous tumor). The wavelengths of the waves 1224a, 1224b, 1224c can be in the visible light, NIR, or SWIR spectrum of wavelengths. For example, visible light can be reflected off a surface 1205 of the tissue 1203 and NIR and/or SWIR waveforms can be configured to penetrate the surface 1205 of the tissue 1203. In various aspects, as described herein, a spectral signal (e.g. hyperspectral, multispectral, or selective spectral) or a photoacoustic signal can be emitted from the emitter 1206. In various instances, the waves 1224b, 1224c can be selected to target the critical structures 1201a, 1201b within the tissue 1203 based on the spectral signature of the critical structure 1201a, 1201b, as further described herein. Photoacoustic imaging is further described herein and in the aforementioned contemporaneously-filed U.S. Patent Applications, which are incorporated by reference herein in their respective entireties.
The emitted waves 1224a, 1224b, 1224c can be reflected off the targeted material (i.e. the surface 1205, the first critical structure 1201a, and the second structure 1201b, respectively). The received waveforms 1225a, 1225b, 1225c can be delayed due to the distances d1a, d2a, d3a, d1b, d2b, d2c indicated in
In the time-of-flight sensor system 1204, in which the emitter 1206 and the receiver 1208 are independently positionable (e.g., on separate surgical devices 1202a, 1202b and/or controlled by separate robotic arms), the various distances d1a, d2a, d3a, d1b, d2b, d2c can be calculated from the known position of the emitter 1206 and the receiver 1208. For example, the positions can be known when the surgical devices 1202a, 1202b are robotically-controlled. Knowledge of the positions of the emitter 1206 and the receiver 1208, as well as the time of the photon stream to target a certain tissue and the information received by the receiver 1208 of that particular response can allow a determination of the distances d1a, d2a, d3a, d1b, d2b, d2c. In one aspect, the distance to the obscured critical structures 1201a, 1201b can be triangulated using penetrating wavelengths. Because the speed of light is constant for any wavelength of visible or invisible light, the time-of-flight sensor system 1204 can determine the various distances.
Referring still to
In various instances, the receiver 1208 can be mounted on a trocar or cannula, such as the trocar 1210b, for example, through which the surgical device 1202b is positioned. In other instances, the receiver 1208 can be mounted on a separate robotic arm for which the three-dimensional position is known. In various instances, the receiver 1208 can be mounted on a movable arm that is separate from the robot that controls the surgical device 1202a or can be mounted to an operating room (OR) table that is intraoperatively registerable to the robot coordinate plane. In such instances, the position of the emitter 1206 and the receiver 1208 can be registerable to the same coordinate plane such that the distances can be triangulated from outputs from the time-of-flight sensor system 1204.
Combining time-of-flight sensor systems and near-infrared spectroscopy (NIRS), termed TOF-NIRS, which is capable of measuring the time-resolved profiles of NIR light with nanosecond resolution can be found in the article titled TIME-OF-FLIGHT NEAR-INFRARED SPECTROSCOPY FOR NONDESTRUCTIVE MEASUREMENT OF INTERNAL QUALITY IN GRAPEFRUIT, in the Journal of the American Society for Horticultural Science, May 2013 vol. 138 no. 3 225-228, which is incorporated by reference herein in its entirety, and is accessible at journal.ashspublications.org.
In various instances, time-of-flight spectral waveforms are configured to determine the depth of the critical structure and/or the proximity of a surgical device to the critical structure. Moreover, the various surgical visualization systems disclosed herein include surface mapping logic that is configured to create three-dimensional rendering of the surface of the visible tissue. In such instances, even when the visible tissue obstructs a critical structure, the clinician can be aware of the proximity (or lack thereof) of a surgical device to the critical structure. In one instances, the topography of the surgical site is provided on a monitor by the surface mapping logic. If the critical structure is close to the surface of the tissue, spectral imaging can convey the position of the critical structure to the clinician. For example, spectral imaging may detect structures within 5 or 10 mm of the surface. In other instances, spectral imaging may detect structures 10 or 20 mm below the surface of the tissue. Based on the known limits of the spectral imaging system, the system is configured to convey that a critical structure is out-of-range if it is simply not detected by the spectral imaging system. Therefore, the clinician can continue to move the surgical device and/or manipulate the tissue. When the critical structure moves into range of the spectral imaging system, the system can identify the structure and, thus, communicate that the structure is within range. In such instances, an alert can be provided when a structure is initially identified and/or moved further within a predefined proximity zone. In such instances, even non-identification of a critical structure by a spectral imaging system with known bounds/ranges can provide proximity information (i.e. the lack of proximity) to the clinician.
Various surgical visualization systems disclosed herein can be configured to identify intraoperatively the presence of and/or proximity to critical structure(s) and to alert a clinician prior to damaging the critical structure(s) by inadvertent dissection and/or transection. In various aspects, the surgical visualization systems are configured to identify one or more of the following critical structures: ureters, bowel, rectum, nerves (including the phrenic nerve, recurrent laryngeal nerve [RLN], promontory facial nerve, vagus nerve, and branches thereof), vessels (including the pulmonary and lobar arteries and veins, inferior mesenteric artery [IMA] and branches thereof, superior rectal artery, sigmoidal arteries, and left colic artery), superior mesenteric artery (SMA) and branches thereof (including middle colic artery, right colic artery, ilecolic artery), hepatic artery and branches thereof, portal vein and branches thereof, splenic artery/vein and branches thereof, external and internal (hypogastric) ileac vessels, short gastric arteries, uterine arteries, middle sacral vessels, and lymph nodes, for example. Moreover, the surgical visualization systems are configured to indicate proximity of surgical device(s) to the critical structure(s) and/or warn the clinician when surgical device(s) are getting close to the critical structure(s).
Various aspects of the present disclosure provide intraoperative critical structure identification (e.g., identification of ureters, nerves, and/or vessels) and instrument proximity monitoring. For example, various surgical visualization systems disclosed herein can include spectral imaging and surgical instrument tracking, which enable the visualization of critical structures below the surface of the tissue, such as 1.0-1.5 cm below the surface of the tissue, for example. In other instances, the surgical visualization system can identify structures less than 1.0 cm or more the 1.5 cm below the surface of the tissue. For example, even a surgical visualization system that can identify structures only within 0.2 mm of the surface, for example, can be valuable if the structure cannot otherwise be seen due to the depth. In various aspects, the surgical visualization system can augment the clinician's view with a virtual depiction of the critical structure as a visible white-light image overlay on the surface of visible tissue, for example. The surgical visualization system can provide real-time, three-dimensional spatial tracking of the distal tip of surgical instruments and can provide a proximity alert when the distal tip of a surgical instrument moves within a certain range of the critical structure, such as within 1.0 cm of the critical structure, for example.
Various surgical visualization systems disclosed herein can identify when dissection is too close to a critical structure. Dissection may be “too close” to a critical structure based on the temperature (i.e. too hot within a proximity of the critical structure that may risk damaging/heating/melting the critical structure) and/or based on tension (i.e. too much tension within a proximity of the critical structure that may risk damaging/tearing/pulling the critical structure). Such a surgical visualization system can facilitate dissection around vessels when skeletonizing the vessels prior to ligation, for example. In various instances, a thermal imaging camera can be utilized to read the heat at the surgical site and provide a warning to the clinician that is based on the detected heat and the distance from a tool to the structure. For example, if the temperature of the tool is over a predefined threshold (such as 120 degrees F., for example), an alert can be provided to the clinician at a first distance (such as 10 mm, for example), and if the temperature of the tool is less than or equal to the predefined threshold, the alert can be provided to the clinician at a second distance (such as 5 mm, for example). The predefined thresholds and/or warning distances can be default settings and/or programmable by the clinician. Additionally or alternatively, a proximity alert can be linked to thermal measurements made by the tool itself, such as a thermocouple that measures the heat in a distal jaw of a monopolar or bipolar dissector or vessel sealer, for example.
Various surgical visualization systems disclosed herein can provide adequate sensitivity with respect to a critical structure and specificity to enable a clinician to proceed with confidence in a quick but safe dissection based on the standard of care and/or device safety data. The system can function intraoperatively and in real-time during a surgical procedure with minimal ionizing radiation risk to a patient or a clinician and, in various instances, no risk of ionizing radiation risk to the patient or the clinician. Conversely, in a fluoroscopy procedure, the patient and clinician(s) may be exposed to ionizing radiation via an X-ray beam, for example, that is utilized to view the anatomical structures in real-time.
Various surgical visualization system disclosed herein can be configured to detect and identify one or more desired types of critical structures in a forward path of a surgical device, such as when the path of the surgical device is robotically controlled, for example. Additionally or alternatively, the surgical visualization system can be configured to detect and identify one or more types of critical structures in a surrounding area of the surgical device and/or in multiple planes/dimensions, for example.
Various surgical visualization systems disclosed herein can be easy to operate and/or interpret. Moreover, various surgical visualization systems can incorporate an “override” feature that allows the clinician to override a default setting and/or operation. For example, a clinician can selectively turn off alerts from the surgical visualization system and/or get closer to a critical structure than suggested by the surgical visualization system such as when the risk to the critical structure is less than risk of avoiding the area (e.g. when removing cancer around a critical structure the risk of leaving the cancerous tissue can be greater than the risk of damage to the critical structure).
Various surgical visualization systems disclosed herein can be incorporated into a surgical system and/or used during a surgical procedure with limited impact to the workflow. In other words, implementation of the surgical visualization system may not change the way the surgical procedure is implemented. Moreover, the surgical visualization system can be economical in comparison to the costs of an inadvertent transection. Data indicates the reduction in inadvertent damage to a critical structure can drive incremental reimbursement.
Various surgical visualization systems disclosed herein can operate in real-time, or near real-time, and far enough in advance to enable a clinician to anticipate critical structure(s). For example, a surgical visualization system can provide enough time to “slow down, evaluate, and avoid” in order to maximize efficiency of the surgical procedure.
Various surgical visualization systems disclosed herein may not require a contrast agent, or dye, that is injected into tissue. For example, spectral imaging is configured to visualize hidden structures intraoperatively without the use of a contrast agent or dye. In other instances, the contrast agent can be easier to inject into the proper layer(s) of tissue than other visualization systems. The time between injection of the contrast agent and visualization of the critical structure can be less than two hours, for example.
Various surgical visualization systems disclosed herein can be linked with clinical data and/or device data. For example, data can provide boundaries for how close energy-enabled surgical devices (or other potentially damaging devices) should be from tissue that the surgeon does not want to damage. Any data modules that interface with the surgical visualization systems disclosed herein can be provided integrally or separately from a robot to enable use with stand-alone surgical devices in open or laparoscopic procedures, for example. The surgical visualization systems can be compatible with robotic surgical systems in various instances. For example, the visualization images/information can be displayed in a robotic console.
In various instances, clinicians may not know the location of a critical structure with respect to a surgical tool. For example, when a critical structure is embedded in tissue, the clinician may be unable to ascertain the location of the critical structure. In certain instances, a clinician may want to keep a surgical device outside a range of positions surrounding the critical structure and/or away from the visible tissue covering the hidden critical structure. When the location of a concealed critical structure is unknown, the clinician may risk moving too close to the critical structure, which can result in inadvertent trauma and/or dissection of the critical structure and/or too much energy, heat, and/or tension in proximity of the critical structure. Alternatively, the clinician may stay too far away from a suspected location of the critical structure and risk affecting tissue at a less desirable location in an effort to avoid the critical structure.
A surgical visualization system is provided that presents surgical device tracking with respect to one or more critical structures. For example, the surgical visualization system can track the proximity of a surgical device with respect to a critical structure. Such tracking can occur intraoperatively, in real-time, and/or in near real-time. In various instances, the tracking data can be provided to the clinicians via a display screen (e.g. a monitor) of an imaging system.
In one aspect of the present disclosure, a surgical visualization system includes a surgical device comprising an emitter configured to emit a structured light pattern onto a visible surface, an imaging system comprising a camera configured to detect an embedded structure and the structured light pattern on the visible surface, and a control circuit in signal communication with the camera and the imaging system, wherein the control circuit is configured to determine a distance from the surgical device to the embedded structure and provide a signal to the imaging system indicative of the distance. For example, the distance can be determined by computing a distance from the camera to the critical structure that is illuminated with fluoroscopy technology and based on a three-dimensional view of the illuminated structure provided by images from multiple lenses (e.g. a left-side lens and a right-side lens) of the camera. The distance from the surgical device to the critical structure can be triangulated based on the known positions of the surgical device and the camera, for example. Alternative means for determining the distance to an embedded critical structure are further described herein. For example, NIR time-of-flight distance sensors can be employed. Additionally or alternatively, the surgical visualization system can determine a distance to visible tissue overlying/covering an embedded critical structure. For example, the surgical visualization system can identify a hidden critical structure and augment a view of the hidden critical structure by depicting a schematic of the hidden critical structure on the visible structure, such as a line on the surface of the visible tissue. The surgical visualization system can further determine the distance to the augmented line on the visible tissue.
By providing the clinician with up-to-date information regarding the proximity of the surgical device to the concealed critical structure and/or visible structure, as provided by the various surgical visualization systems disclosed herein, the clinician can make more informed decisions regarding the placement of the surgical device relative to the concealed critical structure. For example, the clinician can view the distance between the surgical device and the critical structure in real-time/intraoperatively and, in certain instances, an alert and/or warning can be provided by the imaging system when the surgical device is moved within a predefined proximity and/or zone of the critical structure. In certain instances, the alert and/or warning can be provided when the trajectory of the surgical device indicates a likely collision with a “no-fly” zone in the proximity of the critical structure (e.g. within 1 mm, 2 mm, 5 mm, 10 mm, 20 mm or more of the critical structure). In such instances, the clinician can maintain momentum throughout the surgical procedure without requiring the clinician to monitor a suspected location of the critical structure and the surgical device's proximity thereto. As a result, certain surgical procedures can be performed more quickly, with fewer pauses/interruptions, and/or with improved accuracy and/or certainty, for example. In one aspect, the surgical visualization system can be utilized to detect tissue variability, such as the variability of tissue within an organ to differentiate tumors/cancerous tissue/unhealthy tissue from healthy tissue. Such a surgical visualization system can maximize the removal of the unhealthy tissue while minimizing the removal of the healthy tissue.
Referring now to
The surgical device 1402 includes an emitter 1406 configured to emit a pattern of structured light. The pattern is configured to reflect off a surface, and the reflected light can be detected by a camera or sensor, such as the camera 1420. Based on the reflected pattern, a control circuit is configured to determine the contours of the surface 1405 and one of more distances with respect to the surface 1405. For example, the control circuit can generate a three-dimensional model of the surface. Various control circuits are further described herein (e.g. see
The camera 1420 is a three-dimensional camera. For example, the camera 1420 includes a two-dimensional left-side lens, or sensor, 1408a, and a two-dimensional right-side lens, or sensor, 1408b. The lenses 1408a, 1408b are configured to detect one or more critical structures, such as the critical structure 1401, which is embedded within the tissue 1403. In various instances, the critical structure 1401 can be illuminated with a contrast agent, such as ICG, for example, and imaged via fluoroscopy techniques, for example. Furthermore, the left-side lens 1408a is configured to detect a two-dimensional view of the illuminated critical structure 1401, which is shown in
The two-dimensional views from the lenses 1408a, 1408b can be integrated or combined to generate a three-dimensional view of the illuminated critical structure 1401 embedded in the anatomical target tissue. In various instances, the distance from the surgical device 1402 to the critical structure 1401 can be determined from the images obtained by the camera 1420. For example, the distance can be determined by applying an algorithm to both of the images obtained by the lenses 1408a, 1408b. For example, the distance in the left-hand image dl (
In various instances, because the relative positions of the camera 1420 and the surgical device 1402 are known (i.e., in a registered coordinate system such as the same robotic coordinate system), the distance from the surgical device 1402 to the critical structure 1401 can be triangulated, for example, when the distance from the critical structure 1401 to the camera 1420 is known. The robot has coordinate position knowledge of the arms (e.g., the surgical device 1402 and the camera 1420) and, thus, can triangulate to determine the intersection distance. Alternative means for determining the distance d between a surgical device and a critical structure are further described herein (e.g. ultrasound, time-of-flight measurements systems, hyperspectral signal analysis, etc.).
Referring primarily to
In one instance, referring still to
In various instances, the clinician can be alerted when the distance moves from one proximity zone to another along the spectrum of the key 1454a. For example, a first alert can be provided when the distance moves from the green zone to the yellow zone, a second alert can be provided when the distance moves from yellow zone to the orange zone, and a third alert can be provided when the distance moves from the orange zone to the red zone. The type of alert can vary depending on the proximity of the surgical device 1402 and/or the preprogrammed settings selected by the clinician. Similarly, in various aspects, the imaging system can alert the clinician when the surgical device 1402 is moved away from the critical structure 1401, such as when the critical structure 1401 is a target of the surgical device 1402.
In another instance, referring to
In certain instances, the distance d from the surgical device 1402 to the critical structure 1401 can be displayed along a proximity spectrum indicator that includes a plurality of ranges and/or zones corresponding to different distances. Different types of cross-hatching can correspond to different proximity zones, and the key 1454b can explain the significance of the different types of cross-hatching with respect to distances or proximity zones. The proximity spectrum can be defined by a spectrum of colors, a range of numerical values, and/or other differentiating symbols to indicate proximity, for example.
In various instances, the surgical visualization systems disclosed herein, such as the surgical visualization system 1400, for example, can be utilized to target a specific critical structure or portion thereof. For example, referring again to
Referring now to
Similar to the surgical device 1402 (
Similar to the camera 1420 (
Moreover, in various instances, the distances d1, d2, and d3 from the surgical devices 1502a, 1502b, 1502c, respectively, to the critical structure 1501 can be determined and/or approximated from the three-dimensional imaging of the surgical site, as further described herein. For example, the distance from each surgical device 1502a, 1502b, 1502c to the critical structure 1501 can be measured in two-dimensions and averaged, or otherwise computed/triangulated from known positions and/or distances.
The distances d1, d2, and d3 are communicated to the display screen 1550 (e.g. a video monitor), which is configured to display the distances d1, d2, and d3. For example, the distances d1, d2, and d3 can be displayed in a color-coded format, as depicted in
Additionally or alternatively, the proximity spectrum indicators 1554a, 1554b, 1554c can include a range of numerical values and/or other symbols indicating proximity zones/distances. In certain instances, the proximity zones can be configurable or selectable for different critical structures and/or different surgical devices. For example, the proximity zones around a vessel can be different for an energy device and a stapler. Additionally, the proximity zone for an energy device can be different for a vein and an artery.
In various instances, the surgical visualization system 1500 is configured to provide an alert, warning, or other indication to the clinician when one of the surgical devices 1502a, 1502b, 1502c approaches a predefined range of positions/minimum distance limit/proximity zone around the critical structure 1501. The alerts can be provided at different critical distances based on the type of surgical device 1502a, 1502b, 1502c and the type of critical structure 1501.
In various instances, one or more of the surgical devices 1502a, 1502b, 1502c can be robotic tools. For example, a robotic system can control the surgical devices 1502a, 1502b, 1502c. In certain instances, a proximity zone around a critical structure can form a “keep out zone”, and the robotic system can automatically control the surgical devices 1502a, 1502b, 1502c such that they stay out of the “keep out zone.” If a clinician provides an input command to move the surgical device 1502a, 1502b, 1502c into a “keep out zone,” an automated tool control motion can prevent the surgical device 1502a, 1502b, 1502c from entering the defined “keep out zone.” In certain instances, such an automated tool control can be turned on/off, can be a default setting, and/or can be overcome by an override input by the clinician, for example.
Referring now to
The surgical device 1602 includes an emitter 1606 configured to emit light waves. For example, the emitter 1606 can be configured to emit a tissue-penetrating infrared wavelength that is configured to penetrate the tissue 1603 and reach the critical structure 1601. The emitter 1606 can include a spectral light source, for example, which can be configured to emit hyperspectral, multispectral, and/or selective spectral waveforms, for example. The emitter 1606 can further emit a structured light pattern, which is detected by the camera 1620 to generate a topographical surface map of the surface 1605.
The camera 1620 also includes an image sensor or receiver 1608 configured to detect the light waves emitted from the emitter 1606 and reflected by the critical structure 1601. Spectral imaging logic can identify the critical structure based on the reflected light waves received by the image sensor 1608, as further described herein. In various instances, the detected critical structure 1601 can be schematically depicted as a line or other symbol on the three-dimensional rendering of the surface 1605 of the visible tissue 1603. For example, an approximate position of the critical structure 1601 can be conveyed to the clinician as a line on a three-dimensional surface map of the surface 1605. In certain instances, the image sensor 1608 is also configured to detect visible light, and can selectively record live images and/or a video feed of the surgical site and convey the images/video to an imaging system and/or display screen/monitor thereof.
The surgical visualization system 1600 is configured to determine a device-to-surface distance d2 from a distal end 1612 of the surgical device 1602 to a tissue surface, a device-to-vessel distance d3 from the distal end 1612 of the surgical device 1602 to a vessel below the tissue surface, and a surface-to-vessel distance/depth d4 of the critical structure below the tissue surface, according to one or more distance determining methods and/or systems described herein. For example, the device-to-surface distance d2 can be determined from surface mapping logic and the resultant surface map that is generated from the structured light pattern. Additionally or alternatively, the device-to-surface distance d2 can be determined by a time-of-flight distance sensing system configured to detect a delay between an emitted wave and a received wave targeting the surface 1605 of the tissue 1603. In various instances, the device-to-surface distance d2 can be the distance to the portion of the surface 1605 that is overlying an identified critical structure, such as the critical structure 1601. For example, the device-to-surface distance d2 can be the distance from the distal end 1612 of the surgical device 1602 to an augmented line on the three-dimensional rendering of the surface 1605 nearest to the surgical device 1602. The device-to-vessel distance d3 can be determined from NIR time-of-flight sensing of spectral waves received by the camera 1620 and/or one or more image sensors 1608. In other instances, the device-to-vessel distance d3 can be determined from triangulation, three-dimensional cameras, and fluoroscopy illumination. In various instances, the surface-to-vessel depth d1 can be computed and/or triangulated from real-time distances and/or dimensions obtained with spectral imaging, three-dimensional imaging and/or surface mapping data, determined by ultrasound, and/or dimensions obtained in preoperative scans, for example. Alternative distance determining systems are further disclosed herein.
The surgical visualization system 1600 also includes an imaging system, which can include the camera 1620 and a display screen 1650 (
Referring now to
In
The critical structure 1601, which is concealed to the naked eye, is identified with spectral imaging and schematically depicted via the augmented second view 1652b. In various instances, the augmented schematic of the critical structure 1601 can be toggled on and off and/or, as shown in
In
The critical structure 1601, which is concealed to the naked eye, is schematically depicted via the augmented third view 1652c. In various instances, an augmented schematic of the surface 1605 of the tissue 1603 can be toggled on and off and/or, as shown in
The views and/or portions thereof in
When a surgical device is being manipulated around a surgical site, the clinician may want to know the position of the surgical device and/or “see” the surgical device relative to one of more other structures (e.g., surface tissue, hidden critical structures, other surgical devices etc.). However, the surgical device or a portion thereof can be obstructed from view during a surgical procedure. For example, tissue or another anatomical structure can be positioned between the clinician's view point (e.g. the camera) and the surgical device or a portion of the surgical device, such as an articulation joint and/or jaw, for example, which can block the clinician's view. In such instances, the clinician may be unable to visualize the surgical device or a portion thereof and may risk moving the surgical device too close to a critical structure. For example, a clinician may unintentionally maneuver a surgical stapler, dissector, energy device, and/or needle too close to the critical structure and risk dissecting or otherwise harming the critical structure.
In various instances, a surgical visualization system can allow a clinician to visualize a an obstructed/partially obstructed surgical device relative to a critical structure. For example, the surgical visualization system can be configured to identify the surgical device. In various instances, spectral imaging, such as hyperspectral, multispectral, or selective spectral imaging, for example, can be utilized to identify the surgical device. Other detection modalities include ultrasound, registered magnetic resonance imaging (MRI), and computerized tomography (CT) scans, for example. The surgical visualization system can also include an imaging system including a display that is operably configured to depict the identified surgical device with respect to anatomical structures and/or other surgical devices.
In such instances, the clinician can track the position of the surgical device relative to the critical structure even when the surgical device and/or the critical structure is obscured (or partially obscured) from view. Visualization of a hidden surgical device relative to a critical structure can allow the clinician to carefully and quickly maneuver the surgical device in the desired proximity of the critical structure. For example, a clinician can ensure a dissector stays a sufficient distance away from the critical structure (e.g. from an artery). In another example, a clinician can ensure a biopsy needle reaches one or more suitable locations within the critical structure (e.g. within a tumor).
Referring now to
Identification of hidden anatomical structures, such as a nerve, a vessel, or a ureter, for example, is further described herein. Moreover, in addition to identifying a hidden anatomical structure, spectral imaging can be configured to detect metal, such as the metal portion of an end effector, a metal shaft, staples, and/or a metal band or plate, for example. In such instances, the surgical visualization system can further detect hidden surgical devices. For example, the receiver 1808 on the hyperspectral camera 1820 is configured to identify critical structures including surgical devices, such as a first device 1802a positioned through a first trocar 1810a and a second device 1802b positioned through a second trocar 1810b. The first device 1802a is a robotic grasper tool including an end effector 1812a, which is entirely hidden from the clinician's view by the tissue 1803a, which is a portion of the colon in this example, under which the end effector 1812a is positioned. The second device 1802b is a robotic stapling tool including an end effector 1812b, which is partially hidden from the clinician's view by the tissue 1803a. For example, the end effector 1812b is positioned to clamp the tissue 1803a and, thus, a first jaw is positioned above the tissue 1803a, and a second jaw is positioned below the tissue 1803a.
In certain instances, the receiver 1808 can identify the shaft of the devices 1802a, 1802b and/or the end effectors 1812a, 1812b, respectively, of the devices 1802a, 1802b. In one aspect, a clinician can select, or tag, the shaft or end effector 1812a, 1812b or another portion of the device 1802a, 1802b to track during a surgical procedure. For example, a clinician can select tracking of the shaft of one of the devices 1802a, 1802b to obtain information of where the side of the tool is with respect to other adjacent anatomy. Additionally or alternatively, the receiver 1808 can identify one or more anatomical structures, such as an artery 1801 embedded in tissue 1803b. Tagging of structures is further described in contemporaneously-filed U.S. patent application Ser. No. 16/128,193, titled SURGICAL VISUALIZATION AND MONITORING, for example, which is incorporated by reference herein in its entirety.
The receiver 1808 is configured to track the position of the critical structures (e.g. the end effectors 1812a, 1812b, and the artery 1801) intraoperatively. In various instances, the surgical visualization system 1800 is configured for use during a minimally invasive surgical procedure, such as the minimally invasive colorectal procedure depicted in
Referring now to
In various instances, the display 1850 can also depict a three-dimensional rendering of certain anatomical structures at the surgical site. For example, the tissue 1803a (a portion of the colon), can be depicted on the display 1850. The rendering of the colon can be obtained via structured light and surface mapping logic, as further described herein. For example, the hyperspectral camera 1820 can further include a structured light source, which can emit a structured light pattern onto the tissue 1803a and/or the tissue 1803b, for example. Moreover, the hyperspectral camera 1820 can also include a receiver configured to detect the structured light pattern. Referring to the view in
The display 1850 also includes a proximity spectrum indicator 1856, which communicates the proximity of one or both of the surgical devices 1802a, 1802b to the artery 1801. In certain instances, the proximity spectrum indicator 1856 can display the proximity of the closest surgical device 1802a, 1802b. In other instances, the proximity of the closest surgical device 1802a, 1802b can be a default mode; however, the clinician can selectively pick another surgical device and/or anatomical structure. In still other instances, the proximity spectrum indicator 1856 can alternative and/or toggle between the different surgical devices 1802a, 1802b, or can include different spectrum indicators 1856 for the different surgical devices 1802a, 1802b.
The proximity spectrum indicator 1856 can utilize colors (e.g. red, yellow, green), dimensions, and/or other symbols to communicate the proximity of the surgical device 1802a, 1802b to the artery 1801. For example, proximity zones can be defined around the artery 1801, which can be assigned a color or range of colors. As the distance changes intraoperatively, a marker 1854 can move along the proximity spectrum indicator 1856 to convey the proximity zone in real time. Referring still the
In various instances, the surgical visualization system 1800 is configured to provide an alert, warning, or other indication to the clinician when one of the surgical devices 1802a, 1802b approaches a predefined range of positions/minimum distance limit/proximity zone around the critical structure, such as the artery 1801. The alerts can be provided at different critical distances based on the type of surgical device 1802a, 1802b and the type of critical structure. In one instance, the surgical visualization system 1800 can provide a warning when the marker 1854 moves into the red zone.
The surgical visualization system 1800 is configured to determine one or more distances da and db from the end effectors 1812a and 1812b, respectively. For example, the surgical visualization system 1800 can utilize time-of-flight distance measurements to determine various distances from the camera 1820 (e.g. with targeted tissue-penetrating wavelengths to the surgical devices 1802a, 1802b and the artery 1801, as further described herein). Triangulation algorithms can then determine the relative distances between the structures, such as the distances da and db, for example.
A surgical visualization system can be utilized during a biopsy. An ultrasound-assisted biopsy procedure is shown in
In one aspect, a surgical visualization system including spectral imaging technology can be utilized during a biopsy procedure. Referring now to
The surgical visualization system 2000 includes a hyperspectral camera 2020, which includes an image sensor, as further described herein. For example, the camera 2020 includes an emitter 2006 and a receiver 2008. The emitter 2006 is configured to emit a plurality of tissue-penetrating waves. In various instances, the emitter 2006 can be configured to emit a plurality of hyperspectral, multispectral, or selective spectral waveforms, which are configured to penetrate tissue and reach one or more critical structures, such as a needle 2002 and a tumor 2001. For example, the emitter 2006 is configured to emit waveforms that penetrate a surface 2005 of a tissue 2003 such as a thyroid. Given the different molecular composition of the needle 2002, which is usually comprised of metal and/or plastic, from tissue, spectral imaging can distinguish the embedded needle 2002 from the tissue 2003 and the tumor 2001 because the spectral signatures of these materials will be different. Moreover, as further described herein, spectral imaging can distinguish the different types of tissue (i.e., the thyroid 2003 and the tumor 2001 embedded therein). The surgical visualization system 2000 is configured to convey and/or determine the proximity of the aspirating needle 2002 to tissue (e.g. the tissue of the thyroid 2003) and/or a critical structure (e.g. the tumor 2001). For example, the surgical visualization system 2000 can determine the depth of the aspirating needle 2002 relative to tissue 2003 and a targeted critical structure (i.e., the tumor 2001) using spectral signature differences between the aspirating needle 2002, the tissue 2003, and the targeted tumor 2001. As such, the surgical visualization system 2000 can be utilized instead of the ultrasound device 1920 (
The surgical visualization system 2000 also includes a structured light source and surface mapping logic, as further described herein. In one aspect, the camera 2020 includes the structured light source. For example, the emitter 2006 can be configured to selectively pulse between the spectral imaging waveforms and the structured light pattern. In certain aspects, the camera 2020 is also configured to detect the pattern of structured light on the surface 2005 of the tissue 2003. The surface mapping logic is configured to generate a three-dimensional model or rendering of the tissue surface 2005, which can be provided to an imaging system and overlaid with data from the spectral imaging system, as further described herein.
In
The display 2050 is a monitor that is configured to display a video feed of the surgical site in real-time. For example, images from the camera 2020 can be conveyed to the display 2050 intraoperatively to provide a live view of the surgical site to the clinician. The view of the surgical site can be augmented with additional information including hidden critical structures and/or distances. For example, the display 2050 shows the embedded tumor 2001 and the needle 2002, including the portion of the needle 2002 that is hidden from the clinician's view by tissue.
In various instances, the display 2050 also depicts a three-dimensional rendering of certain anatomical structures at the surgical site. For example, the tissue 2003 is shown on the display 2050. The rendering of the anatomical target tissue 2003 can be obtained via structured light and surface mapping logic, as further described herein. For example, the hyperspectral camera 2020 can further include a structured light source, which can emit a structured light pattern onto the tissue 2003, for example. Moreover, the hyperspectral camera 2020 can include a receiver configured to detect the structured light pattern. Referring to the view in
The display 2050 also includes proximity spectrum indicators 2056a, 2056b, which communicate the proximity of the needle 2002 to the surface 2005 of the tissue 2003 (distance d1) and to the surface of the embedded tumor 2001 (distance d2). For example, the proximity spectrum indicators 2056a, 2056b can define distance in positive and negative values from the surfaces. In
In various aspects, the proximity spectrum indicators 2056a, 2056b can include colors, numerical values or ranges, and/or other symbols to detect proximity. In certain instances, a single proximity spectrum indicator can alternate and/or toggle between the different distances d1 and d2 and/or a clinician can select one or more distances to display.
In various instances, the surgical visualization system 2000 is configured to provide an alert, warning, or other indication to the clinician when the needle 2002 approaches a predefined range of positions, a minimum distance limit, and/or a proximity zone around the tumor 2001 and/or a depth within the tissue 2003.
The surgical visualization system 2000 is configured to determine one or more distances with time-of-flight sensor systems (e.g. with targeted wavelengths to the surface 2005, the needle 2002, and the tumor 2001, as further described herein). Furthermore, triangulation algorithms can determine the relative distances between the structures, such as the distances d1 and d2, for example.
In certain instances, the biopsy procedure shown in
Various surgical visualization systems disclosed herein are configured to identify one or more critical structures embedded in tissue or otherwise hidden from view, and determine one or more distances with respect to the visible tissue and/or the critical structure(s). In certain instances, the critical structure can be a surgical device, such as a grasper, a dissector, a stapler, a staple, a staple row/line, a circular stapler, a circular stapler anvil, a gastric bougie, or a hernia tack/clip, for example. During a surgical procedure, it can be desirable to track the position of these critical structures. For example, a subsequent step in a surgical procedure can depend on the position of a critical structure that was implanted and/or moved during an earlier step.
As one example, during a lower anterior resection (LAR) of the colon, the clinician may want to identify and track the staple lines along the ends of the colon before effecting anastomosis of the ends. Referring to the exemplary LAR procedure in
A circular stapler 2202 can be utilized to resect the first and second colon portions 2272 and 2274. As shown in
Proper alignment of the circular stapler 2202 and the anvil 2204 is important for sealing the first colon portion 2272 and the second colon portion 2274. Therefore, it can be helpful to visualize the circular stapler 2202 and/or the anvil 2204 during the procedure. It can also be helpful to visualize the position of the staple lines and determine one or more distances relative thereto. For example, a clinician may want to track the proximity of the circular stapler 2202, the anvil 2204, and one or more staple lines (and, thus, the severed ends of the colon 2270) during the procedure to facilitate alignment and/or positioning of the devices. A surgical visualization system, as disclosed herein, can be utilized to image, visualize, and track the positions of the circular stapler 2202, the trocar 2206, and the anvil 2204, as well as the position of the trocar 2206 with respect to a desired exit point of the colon 2270, for example.
Referring now to
Identification of hidden anatomical structures, such as a nerve, a vessel, or a ureter, for example, and surgical devices, such as a surgical end effector, shaft, or staple, for example, is further described herein. For example, the receiver 2308 on the hyperspectral camera 2320 is configured to identify critical structures including surgical devices, such as a first device 2302a and a second device 2302b. The first device 2302a is a robotic grasper tool and the second device 2302b is a robotic stapling tool. In other instances, the devices 2302a, 2302b can be handheld surgical instruments, and may be used in a laparoscopic procedure, for example. In certain instances, the receiver 2308 can identify the shaft and/or the end effectors of the devices 2302a, 2302b. Additionally, the receiver 2308 on the hyperspectral camera 2320 is configured to identify staple lines 2380a, 2380b in the colon 2303. In one aspect, a clinician can select, or tag, a portion of a critical structure (e.g. a surgical device 2302a, 2302b and/or staple lines 2380a, 2380b) to track during a surgical procedure. Tagging of structures is further described in contemporaneously-filed U.S. Patent Application Ser. No. 16/128,193, titled SURGICAL VISUALIZATION AND MONITORING, for example, which is incorporated by reference herein in its entirety.
The receiver 2308 is configured to track the position of the critical structures (e.g. the surgical devices 2302a, 2302b, and the staple lines 2380a, 2380b) intraoperatively. In various instances, the surgical visualization system 2300 is configured for use during an LAR procedure, as shown in
Referring now to
In various instances, the display 2350 also depicts a three-dimensional rendering of certain anatomical structures at the surgical site. For example, the colon 2303 can be depicted on the display 2350. The rendering of the colon 2303 and movement thereof can be obtained via structured light and surface mapping logic, as further described herein. For example, the hyperspectral camera 2320 can further include the structured light source, which can emit a structured light pattern onto the colon 2303, for example. Moreover, the hyperspectral camera 2320 can also include a receiver configured to detect the structured light pattern. In other instances, a separate device can emit and/or detect the structured light pattern. Referring to the view in
The display 2350 also includes a proximity spectrum indicator 2356, which communicates the proximity of one or both of the surgical devices 2302a, 2302b to one or both of the staple lines 2380a, 2380b. In various instances, the clinician can selectively pick a staple line 2380a, 2380b and a surgical device 2302a, 2302b to track and monitor on the proximity spectrum indicator 2356 and/or show on the display 2350. In other instances, the proximity spectrum indicator 2356 can alternate and/or toggle between the different surgical devices 2302a, 2302b and/or staple lines 2380a, 2380b or can include different spectrum indicators 2356 for the different surgical devices 2302a, 2302b and staple lines 2380a, 2380b. In certain instances a tagged staple line 2380a, 2380b can be tracked by the camera 2320 and remain visible on the display 2350, for example.
The proximity spectrum indicator 2356 can utilize colors (e.g. red, yellow, green), dimensions, and/or other symbols to communicate the proximity of the surgical device 2302a, 2302b to the staple line 2380a, 2380b. For example, proximity zones can be defined around the staple lines 2380a, 2380b, which can be assigned a color or range of colors. As the distance changes intraoperatively, a marker 2354 can move along the proximity spectrum indicator 2356 to convey the proximity zone in real time. Referring still to
In various instances, the surgical visualization system 2300 is configured to provide an alert, warning, or other indication to the clinician when one of the surgical devices 2302a, 2302b approaches a predefined range of positions, a minimum distance limit, and/or a proximity zone around the staple line 2380a, 2380b. The alerts can be provided at different critical distances based on the type of surgical device 2302a, 2302b. In one instance, the surgical visualization system 2300 can provide a warning when the marker 2354 moves into the red zone.
The surgical visualization system 2300 is configured to determine one or more distances from the surgical devices 2302a and 2302b, respectively. For example, the surgical visualization system 2300 can utilize time-of-flight distance measurements to determine various distances from the camera 2320 (e.g. with targeted wavelengths to the surgical devices 2302a, 2302b and the staple lines 2380a, 2380b, as further described herein). Triangulation algorithms can then determine the relative distances between the structures, for example.
In certain instances, a robotic system can be configured to autonomously track to the tagged staple line 2380a, 2380b as if to grasp it and position the colon for a subsequent surgical step, such as anastomosis. For example, the robotic system can automatically move to a position a preset distance away from the staple line 2380a, 2380b and/or above the staple line 2380a, 2380b. Upon automatically moving into proximity with the staple lines 2380a, 2380b, the a clinician at the robotic console can further position the surgical tool via control at a command station/console.
Anastomosis of the colon 2303 is shown in
Referring now to
The surgical visualization system 2300 can be employed to see the surgical device 2302 (i.e., the circular stapler 2302c and anvil 2302d) within the colon 2303 when viewed from a laparoscope. In various instances, the clinician can toggle visibility of the surgical device 2302 or other surgical devices on or off. When the surgical device 2302 is visualized, the clinician is able to see the position of the circular stapler 2302c, the trocar 2312, and the anvil 2302d. Additionally, the clinician can visualize the position of the trocar 2312 with respect to the desired exit point from the colon 2303.
Referring now to
Referring now to
Referring now to
The surgical visualization system 2600 includes a hyperspectral camera 2620 including an image sensor, as further described herein. For example, the camera 2620 can include an emitter 2606 and a receiver 2608. The camera 2620 can be utilized during a laparoscopic procedure to image a surgical site. The emitter 2606 is configured to emit a plurality of tissue-penetrating waves. In various instances, the emitter 2606 can be configured to emit a plurality of hyperspectral, multispectral, or selective spectral waveforms, which are configured to penetrate tissue and reach one or more critical structures, such as a surgical device or another anatomical structure. For example, the emitter 2606 is configured to emit waveforms that penetrate the stomach 2603. The surgical visualization system 2600 can also include a structured light source and receiver/image sensor 2608, which can be configured to determine the surface topography of the stomach 2603.
Identification of hidden anatomical structures, such as a nerve, a vessel, or a ureter, for example, and surgical devices, such as a surgical end effector, shaft, or staple, for example, is further described herein. For example, the receiver 2608 on the hyperspectral camera 2620 is configured to identify and track critical structures including surgical devices, such as the bougie 2612, the body 2614, and the bands 2616, as well as surgical tools, such as a linear stapler 2602.
The surgical visualization system 2600 also includes an imaging system, which includes the camera 2620 and a display 2650 (
Referring now to
In various instances, the display 2650 also depicts a three-dimensional rendering of certain anatomical structures at the surgical site. For example, the stomach 2603 can be depicted on the display 2650. The rendering of the stomach 2603 can be obtained via structured light and surface mapping logic, as further described herein. Referring to the view in
The surgical visualization system 2600 is configured to determine one or more distances from the linear stapler 2602 and/or the bougie 2612 to a critical structure, such as a pylorus, i.e. the opening from the stomach 2603 into the small intestines. The surgical visualization system 2600 can utilize time-of-flight distance measurements to determine various distances from the camera 2620 (e.g. with targeted wavelengths to the surgical devices and/or anatomical structures, as further described herein). Triangulation algorithms can then determine the relative distances between the structures, for example. In various instances, an alert can be provided when the distance meets a threshold value and/or range, as further described herein.
In various instances, the different materials of the bougie 2612 can aid in positioning of the linear stapler 2602. For example, in certain gastric sleeve procedures, haptics can provide feedback to the clinician regarding the placement of the linear stapler 2602. For example, a clinician can palpate the stomach 2603 to determine the position of the bougie 2612. However, in robotic applications, a clinician may be unable to sufficiently evaluate the position of the bougie 6212 based on haptics/tactile feedback. Visualization of the bougie 2612 can provide a replacement and/or supplement to haptics. For example, the surgical visualization system 2600 is configured to determine the distance from the distal end of the linear stapler 2602 to the bougie 2612, which can assist the robotic system in positioning the various medical devices. Moreover, the distance(s) and/or position(s) can be conveyed to the clinician by an imaging system, as further described herein.
In various aspects, a surgical visualization system can be configured to identify a metallic fastener, such a clip, for example. For example, a surgical clip can be embedded in tissue (e.g. placed on a vein or artery to occlude it) during a surgical procedure. Firing staples against a surgical clip can affect the resultant firing motion. For example, a surgical clip between the jaws of an end effector can prevent the end effector from uniformly clamping the tissue and/or can jam a closure beam, firing member, and/or cutting element. Additionally, if staples are fired against the surgical clip, the staples can misfire and/or become deformed. In such instances, the staple line may result in an imperfect seal. The various surgical visualization systems disclosed herein can identify a surgical clip and, in various instances, notify a clinician when the surgical clip is positioned too close to a surgical device (e.g. within or near a transection location).
Referring now to
The surgical visualization system 2700 includes a hyperspectral camera 2720 including an image sensor, as further described herein. For example, the camera 2720 includes an emitter 2706 and a receiver 2708. The camera 2720 can be utilized during a laparoscopic procedure to image a surgical site. The emitter 2706 is configured to emit a plurality of tissue-penetrating waves. In various instances, the emitter 2706 can be configured to emit a plurality of hyperspectral, multispectral, or selective spectral waveforms, which are configured to penetrate tissue and reach one or more critical structures, such as a surgical device or another anatomical structure. For example, the emitter 2706 is configured to emit waveforms that penetrate the tissue 2703. The surgical visualization system 2700 also includes a structured light source, which can be configured to determine the surface topography of the tissue 2703.
Identification of hidden anatomical structures, such as a nerve, a vessel, or a ureter, for example, and surgical devices, such as a surgical end effector, shaft, or staple, for example, is further described herein. For example, the receiver 2708 on the hyperspectral camera 2720 is configured to identify and track critical structures including anatomical structures, such as the vessel 2701, for example, surgical devices, such as the clip 2712, for example, as well as surgical tools, such as a linear stapler 2702a and a grasper 2702b.
The surgical visualization system 2700 also includes an imaging system, which includes the camera 2720 and a display 2750 (
Referring now to
In various instances, the display 2750 also depicts a three-dimensional rendering of certain anatomical structures at the surgical site. For example, the tissue 2703 can be depicted on the display 2750. The rendering of the tissue 2703 can be obtained via structured light and surface mapping logic, as further described herein. Referring to the view on the display 2750 in
The surgical visualization system 2600 is configured to determine one or more distances from the critical structures. For example, the proximity of the surgical devices 2702a, 2702b relative to the clip 2712 can be tracked. The surgical visualization system 2700 can utilize time-of-flight distance measurements to determine various distances from the camera 2720 (e.g. with targeted wavelengths to the surgical devices and/or anatomical structures, as further described herein). Triangulation algorithms can then determine the relative distances between the structures, for example.
As shown in
Similarly, the surgical visualization system 2700 can be configured to detect additional fasteners, such as another clip, tack, or staple, for example, and to track the fastener relative to a surgical device or portion thereof, such as the end effector or shaft of the surgical device, for example. Referring now to
In various instances, a surgical visualization system can be configured to identify the surgical tacks 2812 to ensure the tacks 2812 do not damage a critical structure and/or are not positioned within a critical proximity zone relative to the critical structure. Referring again to
Various surgical visualization systems disclosed herein may be employed in one or more of the following clinical applications. The following clinical applications are non-exhaustive and merely illustrative applications for one or more of the various surgical visualization systems disclosed herein.
A surgical visualization system, as disclosed herein, can be employed in a number of different types of procedures for different medical specialties, such as urology, gynecology, oncology, colorectal, thoracic, bariatric/gastric, and hepato-pancreato-biliary (HPB), for example. In urological procedures, such as a prostatectomy, for example, the ureter may be detected in fat or connective tissue and/or nerves may be detected in fat, for example. In gynecological oncology procedures, such as a hysterectomy, for example, and in colorectal procedures, such as a low anterior resection (LAR) procedure, for example, the ureter may be detected in fat and/or in connective tissue, for example. In thoracic procedures, such as a lobectomy, for example, a vessel may be detected in the lung or in connective tissue and/or a nerve may be detected in connective tissue (e.g., an esophagostomy). In bariatric procedures, a vessel may be detected in fat. In HPB procedures, such as a hepatectomy or pancreatectomy, for example, a vessel may be detected in fat (extrahepatic), in connective tissue (extrahepatic), and the bile duct may be detected in parenchyma (liver or pancreas) tissue.
In one example, a clinician may want to remove an endometrial myoma. From a preoperative magnetic resonance imaging (MRI) scan, the clinician may know that the endometrial myoma is located on the surface of the bowel. Therefore, the clinician may want to know, intraoperatively, what tissue constitute a portion of the bowel and what tissue constitutes a portion of the rectum. In such instances, a surgical visualization system, as disclosed herein, can indicate the different types of tissue (bowel versus rectum) and convey that information to a clinician via an imaging system. Moreover, the imaging system can determine and communicate the proximity of a surgical device to the select tissue. In such instances, the surgical visualization system can provide increased procedural efficiency without critical complications.
In another example, a clinician (e.g. a gynecologist) may stay away from certain anatomic regions to avoid getting too close to critical structures and, thus, the clinician may not remove all of the endometriosis, for example. A surgical visualization system, as disclosed herein, can enable the gynecologist to mitigate the risk of getting too close to the critical structure such that the gynecologist can get close enough with the surgical device to remove all the endometriosis, which can improve the patient outcomes (democratizing surgery). Such a system can enable the surgeon to “keep moving” during the surgical procedure instead of repeatedly stopping and restarting in order to identify areas to avoid, especially during the application of therapeutic energy such as ultrasonic or electrosurgical energy, for example. In gynecological applications, uterine arteries and ureters are important critical structures and the system may be particularly useful for hysterectomy and endometriosis procedures given the presentation and/or thickness of tissue involved.
In another example, a clinician may risk dissection of a vessel at a location that is too proximal and, thus, which can affect blood supply to a lobe other than the target lobe. Moreover, anatomic differences from patient to patient may lead to dissection of a vessel (e.g. a branch) that affects a different lobe based on the particular patient. A surgical visualization system, as disclosed herein, can enable the identification of the correct vessel at the desired location, which enables the clinician to dissect with appropriate anatomic certainty. For example, the system can confirm that the correct vessel is in the correct place and then the clinician can safely divide the vessel.
In another example, a clinician may make multiple dissections before dissecting at the best location due to uncertainty about the anatomy of the vessel. However, it is desirable to dissect in the best location in the first instance because more dissection can increase the risk of bleeding. A surgical visualization system, as disclosed herein, can minimize the number of dissections by indicating the correct vessel and the best location for dissection. Ureters and cardinal ligaments, for example, are dense and provide unique challenges during dissection. In such instances, it can be especially desirable to minimize the number of dissections.
In another example, a clinician (e.g. a surgical oncologist) removing cancerous tissue may want to know the identification of critical structures, localization of the cancer, staging of the cancer, and/or an evaluation of tissue health. Such information is beyond what a clinician sees with the “naked eye”. A surgical visualization system, as disclosed herein, can determine and/or convey such information to the clinician intraoperatively to enhance intraoperative decision making and improve surgical outcomes. In certain instances, the surgical visualization system can be compatible with minimally invasive surgery (MIS), open surgery, and/or robotic approaches using either an endoscope or exoscope, for example.
In another example, a clinician (e.g. a surgical oncologist) may want to turn off one or more alerts regarding the proximity of a surgical tool to one or more critical structure to avoid being overly conservative during a surgical procedure. In other instances, the clinician may want to receive certain types of alerts, such as haptic feedback (e.g. vibrations/buzzing) to indicate proximity and/or or “no fly zones” to stay sufficiently far away from one or more critical structures. A surgical visualization system, as disclosed herein, can provide flexibility based on the experience of the clinician and/or desired aggressiveness of the procedure, for example. In such instances, the system provides a balance between “knowing too much” and “knowing enough” to anticipate and avoid critical structures. The surgical visualization system can assist in planning the next step(s) during a surgical procedure.
Various aspects of the subject matter described herein are set out in the following numbered examples.
Example 1—A surgical visualization system comprising a display screen, a surgical device configured to emit a structured light pattern onto a surface, an image sensor configured to identify a structure embedded below the surface, and a control circuit in signal communication with the image sensor. The control circuit is configured to receive imaging data indicative of the structured light pattern on the surface, generate a three-dimensional digital representation of the surface based on the imaging data, obtain an image of the structure and the surgical device from the image sensor, overlay the image of the structure and the surgical device with the three-dimensional digital representation of the surface on the display screen, and determine a distance from the surgical device to the structure from the image.
Example 2—The surgical visualization system of Example 1, further comprising an emitter configured to emit spectral light in a plurality of wavelengths capable of penetrating the surface and reaching the structure, wherein the image sensor is configured to detect reflected spectral light, and wherein the control circuit is further configured to identify a position of the structure below the surface based on the reflected spectral light.
Example 3—The surgical visualization system of Examples 1 or 2, further comprising a three-dimensional camera comprising the image sensor, and wherein the image comprises a three-dimensional image.
Example 4—The surgical visualization system of Examples 1, 2, or 3, wherein the display screen comprises a digital proximity spectrum, and wherein the control circuit is further configured to display the distance from the surgical device to the structure on the digital proximity spectrum.
Example 5—The surgical visualization system of Example 4, wherein the digital proximity spectrum comprises a plurality of colors.
Example 6—The surgical visualization system of Example 4, wherein the digital proximity spectrum comprises a range of numerical values.
Example 7—The surgical visualization system of Example 4, wherein the digital proximity spectrum comprises a plurality of cross-hatching patterns corresponding to a range of distances.
Example 8—The surgical visualization system of Examples 1, 2, 3, 4, 5, 6, or 7, wherein the three-dimensional digital representation of the surface and a position of the structure are updated on the display screen in real time.
Example 9—The surgical visualization system of Examples 1, 2, 3, 4, 5, 6, 7, or 8, further comprising a robotic control unit in signal communication with the control circuit, wherein the surgical device is operably controlled by the robotic control unit, and wherein the robotic control unit is configured to adjust an operation of the surgical device when the distance from the surgical device to the structure is reduced to less than a minimum distance.
Example 10—The surgical visualization system of Examples 1, 2, 3, 4, 5, 6, 7, 8, or 9, further comprising a contrast agent in the structure, wherein the contrast agent is configured to illuminate the structure, and wherein the image sensor is configured to detect visible light reflected from the illuminated structure.
Example 11—The surgical visualization system of Examples 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10, further comprising a second surgical device. The control circuit is further configured to determine a second distance from the second surgical device to the structure from the image, and provide the second distance to the image system.
Example 12—The surgical visualization system of Example 11, wherein the display screen is further configured to display the second surgical device and the second distance on a second proximity spectrum indicator.
Example 13—The surgical visualization system of Examples 11 or 12, wherein the control circuit is further configured to display a first alert when the distance from the surgical device to the structure is reduced to less than a first minimum distance, and display a second alert when the second distance from the second surgical device to the structure is reduced to less than a second minimum distance. The second minimum distance is different than the first minimum distance.
Example 14—The surgical visualization system of Examples 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13, wherein the control circuit is configured to triangulate the distance from known positions of the surgical device and the image sensor.
Example 15—A surgical visualization system comprising a processor and a memory communicatively coupled to the processor. The memory stores instructions executable by the processor to receive imaging data indicative of a structured light pattern on a surface, generate a three-dimensional digital representation of the surface based on the imaging data, and obtain an image of an embedded structure and a surgical device from an image sensor, overlay the image of the embedded structure and the surgical device with the three-dimensional digital representation of the surface on a display screen, and determine a distance from the surgical device to a portion of the surface covering the embedded structure.
Example 16—The surgical visualization system of Example 15, wherein a position of the embedded structure is identified with reflected spectral light capable of penetrating the surface and reaching the embedded structure.
Example 17—A non-transitory computer readable medium storing computer readable instructions which, when executed, cause a machine to receive imaging data indicative of a structured light pattern on a surface, generate a three-dimensional digital representation of the surface based on the imaging data, obtain a three-dimensional image of an embedded structure and a surgical device from an image sensor, overlay the image of the embedded structure and the surgical device with the three-dimensional digital representation of the surface on a display screen, and determine a distance from the surgical device to the embedded structure from the three-dimensional image.
Example 18—The non-transitory computer readable medium of Example 17, wherein the computer readable instructions, when executed, further cause the machine to provide a signal to the display screen indicative of the distance, and issue a warning signal when the distance meets a predefined threshold distance.
Example 19—A surgical visualization system comprising a display and a first robotic tool comprising a three-dimensional camera. The three-dimensional camera comprises an image sensor. The surgical visualization system further comprises a second robotic tool comprising a spectral light emitter configured to emit spectral light in a plurality of wavelengths capable of penetrating a surface and reaching a structure below the surface. The image sensor is configured to detect reflected visible light and reflected spectral light in the plurality of wavelengths. The surgical visualization system further comprises a control circuit in signal communication with the image sensor and the display. The control circuit is configured to obtain a three-dimensional image of the structure and the second robotic tool from the image sensor, determine a distance from the second robotic tool to the structure from the three-dimensional image, and provide a signal to the display indicative of the distance.
Example 20—The surgical visualization system of Example 19, wherein the second robotic tool further comprises a structured light emitter. The control circuit is further configured to receive imaging data indicative of a structured light pattern on a surface, generate a three-dimensional digital representation of the surface based on the imaging data, and provide a video signal to the display in which the three-dimensional image of the structure is integrated with the three-dimensional digital representation of the surface.
Example 21—A surgical visualization system comprising an emitter configured to emit a plurality of tissue-penetrating waveforms, a receiver configured to detect the plurality of tissue-penetrating waveforms, an imaging system comprising a display, and a control circuit in signal communication with the receiver. The control circuit is configured to receive data from the receiver representative of an image of a hidden portion of a surgical device, and provide the image of the hidden portion of the surgical device to the display.
Example 22—The surgical visualization system of Example 21, further comprising a hyperspectral camera comprising the emitter and the receiver.
Example 23—The surgical visualization system of Examples 21 or 22, further comprising a tissue surface mapping system comprising a structured light source. The control circuit is further configured to receive data from the tissue surface mapping system representative of a three-dimensional representation of a tissue surface, provide the three-dimensional representation of the tissue surface to the display, and overlay, on the display, the image of the hidden portion of the surgical device over the three-dimensional representation of the tissue surface.
Example 24—The surgical visualization system of Examples 21, 22, or 23, wherein the surgical device comprises a robotic surgical tool.
Example 25—The surgical visualization system of Examples 21, 22, or 23, wherein the surgical device comprises an aspirating needle.
Example 26—The surgical visualization system of Examples 21, 22, 23, 24, or 25, wherein the plurality of tissue-penetrating waveforms comprise a first waveform configured to target the hidden portion of the surgical device, and a second waveform configured to target an anatomical structure.
Example 27—The surgical visualization system of Example 26, wherein the control circuit is further configured to identify a first spectral signature corresponding to the hidden portion of the surgical device and a second spectral signature corresponding to the anatomical structure.
Example 28—The surgical visualization system of Examples 26 or 27, wherein the control circuit is further configured to determine a distance between the hidden portion of the surgical device and the anatomical structure.
Example 29—The surgical visualization system of Example 28, wherein the display is configured to convey the distance between the hidden portion of the surgical device and the anatomical structure.
Example 30—The surgical visualization system of Examples 28 or 29, wherein the control circuit is further configured to issue an alert when the distance between the hidden portion of the surgical device and the anatomical structure reaches a threshold minimum distance.
Example 31—The surgical visualization system of Examples 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30, wherein the control circuit comprises a processor and a memory communicatively coupled to the processor. The memory stores instructions executable by the processor to receive data from the receiver representative of the image of the hidden portion of the surgical device, and provide the image of the hidden portion of the surgical device to the display.
Example 32—A surgical visualization system comprising a hyperspectral camera and a control circuit in signal communication with the hyperspectral camera. The hyperspectral camera comprises an emitter and an image sensor. The emitter is configured to emit a plurality of tissue-penetrating waveforms. The image sensor is configured to detect the plurality of tissue-penetrating waveforms. The control circuit is configured to receive data representative of a position of a first critical structure from the plurality of tissue-penetrating waveforms detected by the image sensor, receive data representative of a position of a second critical structure from the plurality of tissue-penetrating waveforms detected by the image sensor, and determine a distance between the first critical structure and the second critical structure.
Example 33—The surgical visualization system of Example 32, wherein the first critical structure comprises one of a surgical device and an anatomical structure.
Example 34—The surgical visualization system of Examples 32 or 33, wherein the second critical structure comprises one of a surgical device and an anatomical structure.
Example 35—The surgical visualization system of Examples 32, 33, or 34, wherein the control circuit comprises a processor and a memory communicatively coupled to the processor. The memory stores instructions executable by the processor to identify the first critical structure from the plurality of tissue-penetrating waveforms detected by the image sensor, identify the second critical structure from the plurality of tissue-penetrating waveforms detected by the image sensor, and determine the distance between the first critical structure and the second critical structure.
Example 36—The surgical visualization system of Examples 32, 33, 34, or 35, further comprising a video monitor, wherein the control circuit is further configured to schematically depict the first critical structure and the second critical structure on the video monitor in real time.
Example 37—The surgical visualization system of Examples 32, 33, 34, 35, or 36, wherein the emitter is further configured to emit a structured light pattern configured to reach a surface. The image sensor is further configured to detect the structured light pattern. The control circuit is further configured to receive data representative of a three-dimensional representation of the surface from the structured light pattern detected by the image sensor.
Example 38—The surgical visualization system of Example 37, wherein the control circuit is further configured to generate an image based on the three-dimensional representation of the surface, and overlay the schematic depiction of the first critical structure and the second critical structure on the image.
Example 39—A non-transitory computer readable medium storing computer readable instructions which, when executed, causes a machine to receive data from an image sensor representative of a first image of a first hidden structure, provide the first image of the first hidden structure to a display, receive data from the image sensor representative of a second image of a second hidden structure, provide the second image of the second hidden structure to the display, and determine a distance between the first hidden structure and the second hidden structure.
Example 40—The non-transitory computer readable medium of Example 39, wherein the computer readable instructions, when executed, further cause the machine to receive data from a receiver configured to detect a structured light pattern on a surface, generate a three-dimensional rendering of the surface from the data, provide the three-dimensional rendering of the surface to the display, and overlay, on the display, the first image of the first hidden structure and the second image on the second hidden structure over the three-dimensional rendering of the surface.
Example 41—A surgical visualization system comprising a first projector, a second projection, and a control circuit. The first projector is configured to emit a structured light pattern on a surface of an anatomical structure. The second projector is configured to emit spectral light in a plurality of wavelengths capable of penetrating the anatomical structure and reaching a staple line. The control circuit is in signal communication with an image sensor. The control circuit is configured to receive structured light data from the image sensor indicative of the structured light pattern on the surface of the anatomical structure, calculate a three-dimensional representation of the anatomical structure from the structured light data, receive spectral light data from the image sensor indicative of a spectral image of the staple line, generate the spectral image of the staple line from the spectral light data, and determine a distance with respect to the staple line.
Example 42—The surgical visualization system of Example 41, further comprising a video monitor, wherein the control circuit is in signal communication with the video monitor. The control circuit is further configured to selectively provide a first video signal to the video monitor indicative of the three-dimensional representation of the anatomical structure in real time. The control circuit is further configured to selectively provide a second video signal to the video monitor indicative of the position of the staple line in real time.
Example 43—The surgical visualization system of Example 42, wherein the control circuit is further configured to selectively integrate the first video signal and the second video signal to generate a video depicting the position of the staple line overlaying the three-dimensional representation of the anatomical structure.
Example 44—The surgical visualization system of Examples 42 or 43, wherein the control circuit is further configured to selectively provide a first signal to the video monitor indicative of the distance relative to the staple line.
Example 45—The surgical visualization system of Examples 41, 42, 43, or 44, wherein the control circuit is further configured to receive spectral light data from the image sensor indicative of a spectral image of a surgical end effector of a robotic tool. The control circuit is further configured to generate the spectral image of the surgical end effector from the spectral light data.
Example 46—The surgical visualization system of Example 45, wherein the control circuit is further configured to triangulate the distance between the surgical end effector and the staple line from the coordinates of the image sensor and the robotic tool.
Example 47—The surgical visualization system of Examples 41, 42, 43, or 44, wherein the control circuit is further configured to receive spectral light data from the image sensor indicative of a spectral image of a trocar of a circular stapler. The control circuit is further configured to generate the spectral image of the trocar from the spectral light data.
Example 48—The surgical visualization system of Example 47, wherein the control circuit is further configured to receive spectral light data from the image sensor indicative of a spectral image of a circular stapler anvil. The control circuit is further configured to generate the spectral image of the circular stapler anvil from the spectral light data.
Example 49—The surgical visualization system of Example 48, wherein the control circuit is further configured to determine a distance between the trocar and the circular stapler anvil.
Example 50—The surgical visualization system of Example 49, wherein the control circuit is further configured to provide a first signal indicative of the distance between the circular stapler anvil and the staple line to the video monitor. The control circuit is further configured to provide a second signal indicative of the distance between the trocar and the circular stapler anvil to the video monitor.
Example 51—The surgical visualization system of Examples 42 or 43, wherein the control circuit is further configured to provide the second video signal to the video monitor based on a user selection input to track the staple line.
Example 52—The surgical visualization system of Examples 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, or 51, wherein the control circuit is in signal communication with a robotic control unit operably configured to control a robotic arm supporting a surgical device, and wherein the robotic control unit is operably configured to provide a control signal to move the robotic arm toward a portion of the anatomical structure obstructing the staple line from view.
Example 53—The surgical visualization system of Examples 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, or 52, further comprising a hyperspectral camera comprising the first projector, the second projector, and the image sensor.
Example 54—A surgical visualization system comprising a processor and a memory communicatively coupled to the processor. The memory stores instructions which, when executed by the processor receive structured light data from an image sensor indicative of a structured light pattern on a surface of an anatomical structure, calculate a three-dimensional representation of the anatomical structure from the structured light data, receive spectral light data from the image sensor indicative of a spectral image of a staple line, generate a spectral image of the staple line from the spectral light data, and determine a distance with respect to the staple line.
Example 55—The surgical visualization system of Example 54, wherein the memory stores instructions which, when executed by the processor selectively provide a first video signal to a video monitor indicative of the three-dimensional representation of the anatomical structure in real time, and selectively provide a second video signal to the video monitor indicative of the position of the staple line in real time.
Example 56—The surgical visualization system of Example 55, wherein the memory stores instructions which, when executed by the processor, selectively integrate the first video signal and the second video signal to generate a video depicting the position of the staple line overlaying the three-dimensional representation of the anatomical structure.
Example 57—The surgical visualization system of Examples 55 or 56, wherein the memory stores instructions which, when executed by the processor, selectively provide a first signal to the video monitor indicative of the distance relative to the staple line.
Example 58—A non-transitory computer readable medium storing computer readable instructions which, when executed, cause a machine to receive structured light data from an image sensor indicative of a structured light pattern on a surface of an anatomical structure, calculate a three-dimensional representation of the anatomical structure from the structured light data, receive spectral light data from the image sensor indicative of a spectral image of a staple line, generate a spectral image of the staple line from the spectral light data, and determine a distance with respect to the staple line.
Example 59—The non-transitory computer readable medium storing computer readable instructions of Example 58, which, when executed, further cause the machine to selectively provide a first video signal to a video monitor indicative of the three-dimensional representation of the anatomical structure in real time, and selectively provide a second video signal to the video monitor indicative of the position of the staple line in real time.
Example 60—The non-transitory computer readable medium storing computer readable instructions of Example 59, which, when executed, further cause the machine to selectively integrate the first video signal and the second video signal to generate a video depicting the position of the staple line overlaying the three-dimensional representation of the anatomical structure.
While several forms have been illustrated and described, it is not the intention of Applicant to restrict or limit the scope of the appended claims to such detail. Numerous modifications, variations, changes, substitutions, combinations, and equivalents to those forms may be implemented and will occur to those skilled in the art without departing from the scope of the present disclosure. Moreover, the structure of each element associated with the described forms can be alternatively described as a means for providing the function performed by the element. Also, where materials are disclosed for certain components, other materials may be used. It is therefore to be understood that the foregoing description and the appended claims are intended to cover all such modifications, combinations, and variations as falling within the scope of the disclosed forms. The appended claims are intended to cover all such modifications, variations, changes, substitutions, modifications, and equivalents.
The foregoing detailed description has set forth various forms of the devices and/or processes via the use of block diagrams, flowcharts, and/or examples. Insofar as such block diagrams, flowcharts, and/or examples contain one or more functions and/or operations, it will be understood by those within the art that each function and/or operation within such block diagrams, flowcharts, and/or examples can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or virtually any combination thereof. Those skilled in the art will recognize that some aspects of the forms disclosed herein, in whole or in part, can be equivalently implemented in integrated circuits, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or as virtually any combination thereof, and that designing the circuitry and/or writing the code for the software and or firmware would be well within the skill of one of skill in the art in light of this disclosure. In addition, those skilled in the art will appreciate that the mechanisms of the subject matter described herein are capable of being distributed as one or more program products in a variety of forms, and that an illustrative form of the subject matter described herein applies regardless of the particular type of signal bearing medium used to actually carry out the distribution.
Instructions used to program logic to perform various disclosed aspects can be stored within a memory in the system, such as dynamic random access memory (DRAM), cache, flash memory, or other storage. Furthermore, the instructions can be distributed via a network or by way of other computer readable media. Thus a machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer), but is not limited to, floppy diskettes, optical disks, compact disc, read-only memory (CD-ROMs), and magneto-optical disks, read-only memory (ROMs), random access memory (RAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic or optical cards, flash memory, or a tangible, machine-readable storage used in the transmission of information over the Internet via electrical, optical, acoustical or other forms of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.). Accordingly, the non-transitory computer-readable medium includes any type of tangible machine-readable medium suitable for storing or transmitting electronic instructions or information in a form readable by a machine (e.g., a computer).
As used in any aspect herein, the term “control circuit” may refer to, for example, hardwired circuitry, programmable circuitry (e.g., a computer processor including one or more individual instruction processing cores, processing unit, processor, microcontroller, microcontroller unit, controller, digital signal processor (DSP), programmable logic device (PLD), programmable logic array (PLA), or field programmable gate array (FPGA)), state machine circuitry, firmware that stores instructions executed by programmable circuitry, and any combination thereof. The control circuit may, collectively or individually, be embodied as circuitry that forms part of a larger system, for example, an integrated circuit (IC), an application-specific integrated circuit (ASIC), a system on-chip (SoC), desktop computers, laptop computers, tablet computers, servers, smart phones, etc. Accordingly, as used herein “control circuit” includes, but is not limited to, electrical circuitry having at least one discrete electrical circuit, electrical circuitry having at least one integrated circuit, electrical circuitry having at least one application specific integrated circuit, electrical circuitry forming a general purpose computing device configured by a computer program (e.g., a general purpose computer configured by a computer program which at least partially carries out processes and/or devices described herein, or a microprocessor configured by a computer program which at least partially carries out processes and/or devices described herein), electrical circuitry forming a memory device (e.g., forms of random access memory), and/or electrical circuitry forming a communications device (e.g., a modem, communications switch, or optical-electrical equipment). Those having skill in the art will recognize that the subject matter described herein may be implemented in an analog or digital fashion or some combination thereof.
As used in any aspect herein, the term “logic” may refer to an app, software, firmware and/or circuitry configured to perform any of the aforementioned operations. Software may be embodied as a software package, code, instructions, instruction sets and/or data recorded on non-transitory computer readable storage medium. Firmware may be embodied as code, instructions or instruction sets and/or data that are hard-coded (e.g., nonvolatile) in memory devices.
As used in any aspect herein, the terms “component,” “system,” “module” and the like can refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution.
As used in any aspect herein, an “algorithm” refers to a self-consistent sequence of steps leading to a desired result, where a “step” refers to a manipulation of physical quantities and/or logic states which may, though need not necessarily, take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It is common usage to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. These and similar terms may be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities and/or states.
A network may include a packet switched network. The communication devices may be capable of communicating with each other using a selected packet switched network communications protocol. One example communications protocol may include an Ethernet communications protocol which may be capable permitting communication using a Transmission Control Protocol/Internet Protocol (TCP/IP). The Ethernet protocol may comply or be compatible with the Ethernet standard published by the Institute of Electrical and Electronics Engineers (IEEE) titled “IEEE 802.3 Standard”, published in December, 2008 and/or later versions of this standard. Alternatively or additionally, the communication devices may be capable of communicating with each other using an X.25 communications protocol. The X.25 communications protocol may comply or be compatible with a standard promulgated by the International Telecommunication Union-Telecommunication Standardization Sector (ITU-T). Alternatively or additionally, the communication devices may be capable of communicating with each other using a frame relay communications protocol. The frame relay communications protocol may comply or be compatible with a standard promulgated by Consultative Committee for International Telegraph and Telephone (CCITT) and/or the American National Standards Institute (ANSI). Alternatively or additionally, the transceivers may be capable of communicating with each other using an Asynchronous Transfer Mode (ATM) communications protocol. The ATM communications protocol may comply or be compatible with an ATM standard published by the ATM Forum titled “ATM-MPLS Network Interworking 2.0” published August 2001, and/or later versions of this standard. Of course, different and/or after-developed connection-oriented network communication protocols are equally contemplated herein.
Unless specifically stated otherwise as apparent from the foregoing disclosure, it is appreciated that, throughout the foregoing disclosure, discussions using terms such as “processing,” “computing,” “calculating,” “determining,” “displaying,” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.
One or more components may be referred to herein as “configured to,” “configurable to,” “operable/operative to,” “adapted/adaptable,” “able to,” “conformable/conformed to,” etc. Those skilled in the art will recognize that “configured to” can generally encompass active-state components and/or inactive-state components and/or standby-state components, unless context requires otherwise.
The terms “proximal” and “distal” are used herein with reference to a clinician manipulating the handle portion of the surgical instrument. The term “proximal” refers to the portion closest to the clinician and the term “distal” refers to the portion located away from the clinician. It will be further appreciated that, for convenience and clarity, spatial terms such as “vertical”, “horizontal”, “up”, and “down” may be used herein with respect to the drawings. However, surgical instruments are used in many orientations and positions, and these terms are not intended to be limiting and/or absolute.
Those skilled in the art will recognize that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to claims containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should typically be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations.
In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, typically means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to “at least one of A, B, or C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that typically a disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms unless context dictates otherwise. For example, the phrase “A or B” will be typically understood to include the possibilities of “A” or “B” or “A and B.”
With respect to the appended claims, those skilled in the art will appreciate that recited operations therein may generally be performed in any order. Also, although various operational flow diagrams are presented in a sequence(s), it should be understood that the various operations may be performed in other orders than those which are illustrated, or may be performed concurrently. Examples of such alternate orderings may include overlapping, interleaved, interrupted, reordered, incremental, preparatory, supplemental, simultaneous, reverse, or other variant orderings, unless context dictates otherwise. Furthermore, terms like “responsive to,” “related to,” or other past-tense adjectives are generally not intended to exclude such variants, unless context dictates otherwise.
It is worthy to note that any reference to “one aspect,” “an aspect,” “an exemplification,” “one exemplification,” and the like means that a particular feature, structure, or characteristic described in connection with the aspect is included in at least one aspect. Thus, appearances of the phrases “in one aspect,” “in an aspect,” “in an exemplification,” and “in one exemplification” in various places throughout the specification are not necessarily all referring to the same aspect. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner in one or more aspects.
Any patent application, patent, non-patent publication, or other disclosure material referred to in this specification and/or listed in any Application Data Sheet is incorporated by reference herein, to the extent that the incorporated materials is not inconsistent herewith. As such, and to the extent necessary, the disclosure as explicitly set forth herein supersedes any conflicting material incorporated herein by reference. Any material, or portion thereof, that is said to be incorporated by reference herein, but which conflicts with existing definitions, statements, or other disclosure material set forth herein will only be incorporated to the extent that no conflict arises between that incorporated material and the existing disclosure material.
In summary, numerous benefits have been described which result from employing the concepts described herein. The foregoing description of the one or more forms has been presented for purposes of illustration and description. It is not intended to be exhaustive or limiting to the precise form disclosed. Modifications or variations are possible in light of the above teachings. The one or more forms were chosen and described in order to illustrate principles and practical application to thereby enable one of ordinary skill in the art to utilize the various forms and with various modifications as are suited to the particular use contemplated. It is intended that the claims submitted herewith define the overall scope.
This application claims the benefit of priority under 35 U.S.C. § 119(e) to U.S. Provisional patent application Ser. No. 62/698,625, titled DIGITAL SURGERY IMAGING/VISUALIZATION SYSTEM, filed Jul. 16, 2018, the disclosure of which is herein incorporated by reference in its entirety.
Number | Name | Date | Kind |
---|---|---|---|
4753641 | Vaslow | Jun 1988 | A |
4785180 | Dietrich et al. | Nov 1988 | A |
4986262 | Saito | Jan 1991 | A |
5460182 | Goodman et al. | Oct 1995 | A |
5609562 | Kaali | Mar 1997 | A |
6350233 | Lubowski | Feb 2002 | B1 |
6386758 | Loser | May 2002 | B2 |
6632183 | Bowman et al. | Oct 2003 | B2 |
6804012 | Gombert | Oct 2004 | B2 |
6951535 | Ghodoussi et al. | Oct 2005 | B2 |
7314048 | Couture et al. | Jan 2008 | B2 |
7477931 | Hoyt | Jan 2009 | B2 |
7516675 | Kurtz et al. | Apr 2009 | B2 |
7726171 | Langlotz et al. | Jun 2010 | B2 |
7740623 | Nayak et al. | Jun 2010 | B2 |
7766210 | Shelton, IV et al. | Aug 2010 | B2 |
7770775 | Shelton, IV et al. | Aug 2010 | B2 |
7798385 | Boyden et al. | Sep 2010 | B2 |
7810691 | Boyden et al. | Oct 2010 | B2 |
7901353 | Vayser et al. | Mar 2011 | B2 |
7905840 | Pimenta et al. | Mar 2011 | B2 |
7975894 | Boyden et al. | Jul 2011 | B2 |
8041089 | Drumm et al. | Oct 2011 | B2 |
8063883 | Senft et al. | Nov 2011 | B2 |
8073528 | Zhao et al. | Dec 2011 | B2 |
8079950 | Stern et al. | Dec 2011 | B2 |
8142450 | Harris et al. | Mar 2012 | B2 |
8161977 | Shelton, IV et al. | Apr 2012 | B2 |
8224484 | Swarup et al. | Jul 2012 | B2 |
8231639 | Bolduc et al. | Jul 2012 | B2 |
8523043 | Ullrich et al. | Sep 2013 | B2 |
8632468 | Glossop et al. | Jan 2014 | B2 |
8652148 | Zuhars | Feb 2014 | B2 |
8706211 | Dacey, Jr. et al. | Apr 2014 | B2 |
8755576 | Taerum | Jun 2014 | B2 |
8763879 | Shelton, IV et al. | Jul 2014 | B2 |
8820603 | Shelton, IV et al. | Sep 2014 | B2 |
8840616 | Wilkinson et al. | Sep 2014 | B2 |
8932208 | Kendale et al. | Jan 2015 | B2 |
8934003 | Popovic et al. | Jan 2015 | B2 |
8989528 | Udd | Mar 2015 | B2 |
8992558 | Stone et al. | Mar 2015 | B2 |
9005118 | Selover et al. | Apr 2015 | B2 |
9044230 | Morgan et al. | Jun 2015 | B2 |
9064173 | Redden | Jun 2015 | B2 |
9072501 | Menchaca et al. | Jul 2015 | B2 |
9072535 | Shelton, IV et al. | Jul 2015 | B2 |
9119670 | Yang et al. | Sep 2015 | B2 |
9141868 | Xu et al. | Sep 2015 | B2 |
9161817 | Olson et al. | Oct 2015 | B2 |
9161820 | Mark et al. | Oct 2015 | B2 |
9179822 | Kitamura et al. | Nov 2015 | B2 |
9241693 | Taylor et al. | Jan 2016 | B2 |
9254103 | Krishnaswamy et al. | Feb 2016 | B2 |
9259290 | Jenkins et al. | Feb 2016 | B2 |
9265587 | Vancamberg et al. | Feb 2016 | B2 |
9274047 | Velten et al. | Mar 2016 | B2 |
9282878 | Grey et al. | Mar 2016 | B2 |
9295773 | Prosl et al. | Mar 2016 | B2 |
9326770 | Shelton, IV et al. | May 2016 | B2 |
9345389 | Nie et al. | May 2016 | B2 |
9402726 | Linderman et al. | Aug 2016 | B2 |
9480534 | Bowling et al. | Nov 2016 | B2 |
9561082 | Yen et al. | Feb 2017 | B2 |
9597054 | Kudavelly et al. | Mar 2017 | B2 |
9649109 | Ranucci et al. | May 2017 | B2 |
9720076 | Guo et al. | Aug 2017 | B2 |
9730690 | Shanley et al. | Aug 2017 | B2 |
9788851 | Dannaher et al. | Oct 2017 | B2 |
9801685 | Nguyen et al. | Oct 2017 | B2 |
9855317 | Bright | Jan 2018 | B2 |
9857167 | Jovanovski et al. | Jan 2018 | B2 |
9883857 | Shluzas et al. | Feb 2018 | B2 |
9901409 | Yang et al. | Feb 2018 | B2 |
9918640 | Ntziachristos et al. | Mar 2018 | B2 |
9987019 | Sato | Jun 2018 | B2 |
10010326 | Sato | Jul 2018 | B2 |
10022199 | Gassner et al. | Jul 2018 | B2 |
10045763 | Sato | Aug 2018 | B2 |
10068173 | Vayser et al. | Sep 2018 | B2 |
10070929 | Tanji | Sep 2018 | B2 |
10085611 | Yabe et al. | Oct 2018 | B2 |
10123706 | Elbaz et al. | Nov 2018 | B2 |
10136959 | Mintz et al. | Nov 2018 | B2 |
10143470 | Sato | Dec 2018 | B2 |
10170205 | Curd et al. | Jan 2019 | B2 |
10194981 | Margallo Balbas et al. | Feb 2019 | B2 |
10194990 | Amanatullah et al. | Feb 2019 | B2 |
10219738 | Monty et al. | Mar 2019 | B2 |
10255723 | Thomas et al. | Apr 2019 | B2 |
10357253 | Sato | Jul 2019 | B2 |
10390835 | Williams | Aug 2019 | B2 |
10433911 | Wang et al. | Oct 2019 | B2 |
10470763 | Yates et al. | Nov 2019 | B2 |
10506991 | Govari | Dec 2019 | B2 |
10510149 | Cutu et al. | Dec 2019 | B2 |
10512518 | Vayser et al. | Dec 2019 | B2 |
10531074 | Wilson et al. | Jan 2020 | B2 |
10546423 | Jones et al. | Jan 2020 | B2 |
10548679 | Carlson et al. | Feb 2020 | B2 |
10561465 | Scholl et al. | Feb 2020 | B2 |
10576246 | Fischell et al. | Mar 2020 | B2 |
10588699 | Richmond et al. | Mar 2020 | B2 |
10666928 | Liu | May 2020 | B2 |
10687797 | Stone et al. | Jun 2020 | B2 |
10695166 | Willis et al. | Jun 2020 | B2 |
10702186 | Amies et al. | Jul 2020 | B2 |
10704093 | Deng et al. | Jul 2020 | B2 |
10743868 | Shelton, IV et al. | Aug 2020 | B2 |
10792034 | Scheib et al. | Oct 2020 | B2 |
10806518 | Amanatullah | Oct 2020 | B2 |
10813700 | Amanatullah | Oct 2020 | B2 |
10861197 | Kobayashi | Dec 2020 | B2 |
10866783 | Atarot et al. | Dec 2020 | B2 |
10881458 | Fischell et al. | Jan 2021 | B2 |
10925465 | Tully et al. | Feb 2021 | B2 |
10973519 | Weir et al. | Apr 2021 | B2 |
11006100 | Douglas | May 2021 | B1 |
11020016 | Wallace et al. | Jun 2021 | B2 |
11058501 | Tokarchuk et al. | Jul 2021 | B2 |
11100631 | Yates et al. | Aug 2021 | B2 |
11109866 | Shelton, IV et al. | Sep 2021 | B2 |
20010012327 | Loser | Aug 2001 | A1 |
20020026127 | Balbierz | Feb 2002 | A1 |
20030069591 | Carson et al. | Apr 2003 | A1 |
20040082842 | Lumba et al. | Apr 2004 | A1 |
20040106916 | Quaid et al. | Jun 2004 | A1 |
20040147926 | Iversen | Jul 2004 | A1 |
20040176751 | Weitzner et al. | Sep 2004 | A1 |
20040230199 | Jansen et al. | Nov 2004 | A1 |
20050113846 | Carson | May 2005 | A1 |
20050124975 | Law | Jun 2005 | A1 |
20050167621 | Zeng et al. | Aug 2005 | A1 |
20050177181 | Kagan et al. | Aug 2005 | A1 |
20050279354 | Deutsch et al. | Dec 2005 | A1 |
20060013454 | Flewelling et al. | Jan 2006 | A1 |
20060079841 | Duff et al. | Apr 2006 | A1 |
20060206007 | Bala | Sep 2006 | A1 |
20060224045 | Whipple et al. | Oct 2006 | A1 |
20060293557 | Chuanggui et al. | Dec 2006 | A1 |
20070014784 | Nayak et al. | Jan 2007 | A1 |
20070019781 | Haras | Jan 2007 | A1 |
20070040906 | Iketani | Feb 2007 | A1 |
20070093748 | Nayak et al. | Apr 2007 | A1 |
20070100210 | Selover et al. | May 2007 | A1 |
20070123912 | Carson | May 2007 | A1 |
20070156021 | Morse et al. | Jul 2007 | A1 |
20070172472 | Nayak | Jul 2007 | A1 |
20070175955 | Shelton et al. | Aug 2007 | A1 |
20070239149 | Lieponis | Oct 2007 | A1 |
20070265495 | Vayser | Nov 2007 | A1 |
20080001919 | Pascucci | Jan 2008 | A1 |
20080004633 | Arata et al. | Jan 2008 | A1 |
20080077158 | Haider et al. | Mar 2008 | A1 |
20080144906 | Allred et al. | Jun 2008 | A1 |
20080151233 | Blanke et al. | Jun 2008 | A1 |
20080194930 | Harris | Aug 2008 | A1 |
20080228104 | Uber et al. | Sep 2008 | A1 |
20080269596 | Revie et al. | Oct 2008 | A1 |
20080319491 | Schoenefeld | Dec 2008 | A1 |
20090000627 | Quaid et al. | Jan 2009 | A1 |
20090234223 | Onoda | Sep 2009 | A1 |
20100030231 | Revie et al. | Feb 2010 | A1 |
20100081875 | Fowler et al. | Apr 2010 | A1 |
20100087755 | Boezaart | Apr 2010 | A1 |
20100137882 | Quaid, III | Jun 2010 | A1 |
20100152539 | Ghabrial et al. | Jun 2010 | A1 |
20100280493 | Nayak | Nov 2010 | A1 |
20110014181 | Thornton | Jan 2011 | A1 |
20110071531 | Carson | Mar 2011 | A1 |
20110082369 | Mohr et al. | Apr 2011 | A1 |
20110201881 | Emch | Aug 2011 | A1 |
20110257611 | Locke et al. | Oct 2011 | A1 |
20110257661 | Choi et al. | Oct 2011 | A1 |
20110306985 | Inoue et al. | Dec 2011 | A1 |
20120004894 | Butler et al. | Jan 2012 | A1 |
20120046668 | Gantes | Feb 2012 | A1 |
20120143341 | Zipnick | Jun 2012 | A1 |
20120165837 | Belman et al. | Jun 2012 | A1 |
20120265022 | Menn | Oct 2012 | A1 |
20120300051 | Daigo et al. | Nov 2012 | A1 |
20130035583 | Park et al. | Feb 2013 | A1 |
20130100250 | Raskar et al. | Apr 2013 | A1 |
20130172902 | Lightcap et al. | Jul 2013 | A1 |
20130211244 | Nathaniel | Aug 2013 | A1 |
20130237811 | Mihailescu | Sep 2013 | A1 |
20130274596 | Azizian et al. | Oct 2013 | A1 |
20130274673 | Fischell et al. | Oct 2013 | A1 |
20130274674 | Fischell et al. | Oct 2013 | A1 |
20130296712 | Durvasula | Nov 2013 | A1 |
20140005685 | Modrow et al. | Jan 2014 | A1 |
20140024945 | Mung | Jan 2014 | A1 |
20140035762 | Shelton, IV et al. | Feb 2014 | A1 |
20140039520 | Haider et al. | Feb 2014 | A1 |
20140114180 | Jain | Apr 2014 | A1 |
20140163359 | Sholev et al. | Jun 2014 | A1 |
20140163664 | Goldsmith | Jun 2014 | A1 |
20140171793 | Lin et al. | Jun 2014 | A1 |
20140175150 | Shelton, IV et al. | Jun 2014 | A1 |
20140179997 | von Grunberg et al. | Jun 2014 | A1 |
20140336461 | Reiter | Nov 2014 | A1 |
20140378763 | Rot et al. | Dec 2014 | A1 |
20140378843 | Valdes et al. | Dec 2014 | A1 |
20150018999 | Lee et al. | Jan 2015 | A1 |
20150025548 | Franklin et al. | Jan 2015 | A1 |
20150032140 | Khouri | Jan 2015 | A1 |
20150051460 | Saxena et al. | Feb 2015 | A1 |
20150066107 | Richter et al. | Mar 2015 | A1 |
20150133909 | van der Weide et al. | May 2015 | A1 |
20150145966 | Krieger et al. | May 2015 | A1 |
20150223903 | Bell et al. | Aug 2015 | A1 |
20150238276 | Atarot et al. | Aug 2015 | A1 |
20150245878 | Jaramaz et al. | Sep 2015 | A1 |
20150297177 | Boctor et al. | Oct 2015 | A1 |
20150305650 | Hunter et al. | Oct 2015 | A1 |
20150366628 | Ingmanson | Dec 2015 | A1 |
20160000516 | Cheng et al. | Jan 2016 | A1 |
20160007827 | Frimer et al. | Jan 2016 | A1 |
20160014328 | Rokutanda | Jan 2016 | A1 |
20160022146 | Piron et al. | Jan 2016 | A1 |
20160038004 | Tanaka | Feb 2016 | A1 |
20160086380 | Vayser et al. | Mar 2016 | A1 |
20160183841 | Duindam et al. | Jun 2016 | A1 |
20160206204 | Matsuda et al. | Jul 2016 | A1 |
20160228090 | Boctor | Aug 2016 | A1 |
20160235304 | Tzoumas et al. | Aug 2016 | A1 |
20160256101 | Aharoni et al. | Sep 2016 | A1 |
20160331467 | Slamin et al. | Nov 2016 | A1 |
20160346034 | Arya et al. | Dec 2016 | A1 |
20160354166 | Popovic et al. | Dec 2016 | A1 |
20160374541 | Agrawal | Dec 2016 | A1 |
20170007350 | Popovic et al. | Jan 2017 | A1 |
20170020613 | Kang et al. | Jan 2017 | A1 |
20170055819 | Hansen et al. | Mar 2017 | A1 |
20170059408 | Korner et al. | Mar 2017 | A1 |
20170071475 | Irisawa | Mar 2017 | A1 |
20170079530 | DiMaio et al. | Mar 2017 | A1 |
20170165008 | Finley | Jun 2017 | A1 |
20170172382 | Nir et al. | Jun 2017 | A1 |
20170172662 | Panescu et al. | Jun 2017 | A1 |
20170189006 | Shluzas et al. | Jul 2017 | A1 |
20170197028 | Goldsmith | Jul 2017 | A1 |
20170202624 | Atarot et al. | Jul 2017 | A1 |
20170209050 | Fengler et al. | Jul 2017 | A1 |
20170238962 | Hansen et al. | Aug 2017 | A1 |
20170251900 | Hansen et al. | Sep 2017 | A1 |
20170258526 | Lang | Sep 2017 | A1 |
20170265943 | Sela et al. | Sep 2017 | A1 |
20170296213 | Swensgard et al. | Oct 2017 | A1 |
20170304007 | Piron et al. | Oct 2017 | A1 |
20170312032 | Amanatullah et al. | Nov 2017 | A1 |
20170366773 | Kiraly et al. | Dec 2017 | A1 |
20170367580 | DiMaio et al. | Dec 2017 | A1 |
20180014777 | Amir et al. | Jan 2018 | A1 |
20180014851 | Hansen et al. | Jan 2018 | A1 |
20180214016 | Thommen et al. | Aug 2018 | A1 |
20180247153 | Ganapati et al. | Aug 2018 | A1 |
20180271603 | Nir et al. | Sep 2018 | A1 |
20180310829 | Frangioni | Nov 2018 | A1 |
20180333210 | Nijkamp et al. | Nov 2018 | A1 |
20180343381 | Kobayashi et al. | Nov 2018 | A1 |
20180344140 | Aizenfeld | Dec 2018 | A1 |
20180368883 | Rossa et al. | Dec 2018 | A1 |
20190000478 | Messerly et al. | Jan 2019 | A1 |
20190008579 | Begg et al. | Jan 2019 | A1 |
20190022418 | Fishman | Jan 2019 | A1 |
20190046276 | Inglese et al. | Feb 2019 | A1 |
20190053691 | Hansen et al. | Feb 2019 | A1 |
20190053872 | Meglan | Feb 2019 | A1 |
20190069824 | Darty et al. | Mar 2019 | A1 |
20190076186 | Fischell et al. | Mar 2019 | A1 |
20190076187 | Fischell et al. | Mar 2019 | A1 |
20190099070 | Mark et al. | Apr 2019 | A1 |
20190099226 | Hallen | Apr 2019 | A1 |
20190104919 | Shelton, IV et al. | Apr 2019 | A1 |
20190110924 | Moreno et al. | Apr 2019 | A1 |
20190117319 | Cima et al. | Apr 2019 | A1 |
20190142524 | Hladio et al. | May 2019 | A1 |
20190175272 | Khan et al. | Jun 2019 | A1 |
20190180865 | Kashima et al. | Jun 2019 | A1 |
20190192145 | Shelton, IV et al. | Jun 2019 | A1 |
20190192150 | Widenhouse et al. | Jun 2019 | A1 |
20190192151 | Shelton, IV et al. | Jun 2019 | A1 |
20190200905 | Shelton, IV et al. | Jul 2019 | A1 |
20190200906 | Shelton, IV et al. | Jul 2019 | A1 |
20190200981 | Harris et al. | Jul 2019 | A1 |
20190201045 | Yates et al. | Jul 2019 | A1 |
20190201046 | Shelton, IV et al. | Jul 2019 | A1 |
20190201047 | Yates et al. | Jul 2019 | A1 |
20190201102 | Shelton, IV et al. | Jul 2019 | A1 |
20190201111 | Shelton, IV et al. | Jul 2019 | A1 |
20190201120 | Shelton, IV et al. | Jul 2019 | A1 |
20190201136 | Shelton, IV et al. | Jul 2019 | A1 |
20190201137 | Shelton, IV et al. | Jul 2019 | A1 |
20190201142 | Shelton, IV et al. | Jul 2019 | A1 |
20190201594 | Shelton, IV et al. | Jul 2019 | A1 |
20190205567 | Shelton, IV et al. | Jul 2019 | A1 |
20190206562 | Shelton, IV et al. | Jul 2019 | A1 |
20190206563 | Shelton, IV et al. | Jul 2019 | A1 |
20190206564 | Shelton, IV et al. | Jul 2019 | A1 |
20190206569 | Shelton, IV et al. | Jul 2019 | A1 |
20190208641 | Yates et al. | Jul 2019 | A1 |
20190247050 | Goldsmith | Aug 2019 | A1 |
20190269476 | Bowling et al. | Sep 2019 | A1 |
20190293554 | Nakao et al. | Sep 2019 | A1 |
20190307524 | Popovic | Oct 2019 | A1 |
20190311542 | Douglas et al. | Oct 2019 | A1 |
20190320117 | Wu et al. | Oct 2019 | A1 |
20190320878 | Duindam et al. | Oct 2019 | A1 |
20190321118 | Genova et al. | Oct 2019 | A1 |
20190388157 | Shameli et al. | Dec 2019 | A1 |
20200008879 | Popovic et al. | Jan 2020 | A1 |
20200015668 | Scheib | Jan 2020 | A1 |
20200015806 | Scheib et al. | Jan 2020 | A1 |
20200015897 | Scheib et al. | Jan 2020 | A1 |
20200015898 | Scheib et al. | Jan 2020 | A1 |
20200015899 | Scheib et al. | Jan 2020 | A1 |
20200015900 | Scheib et al. | Jan 2020 | A1 |
20200015901 | Scheib et al. | Jan 2020 | A1 |
20200015902 | Scheib et al. | Jan 2020 | A1 |
20200015904 | Scheib et al. | Jan 2020 | A1 |
20200015906 | Scheib et al. | Jan 2020 | A1 |
20200015907 | Scheib | Jan 2020 | A1 |
20200015914 | Scheib et al. | Jan 2020 | A1 |
20200015923 | Scheib et al. | Jan 2020 | A1 |
20200015924 | Scheib et al. | Jan 2020 | A1 |
20200015925 | Scheib | Jan 2020 | A1 |
20200018844 | Fridman et al. | Jan 2020 | A1 |
20200037858 | Pedreira de Cerqueira Filho | Feb 2020 | A1 |
20200038112 | Amanatullah et al. | Feb 2020 | A1 |
20200060725 | Sato | Feb 2020 | A1 |
20200121245 | Barclay et al. | Apr 2020 | A1 |
20200281662 | Cong et al. | Sep 2020 | A1 |
20200289205 | Scheib et al. | Sep 2020 | A1 |
20200289216 | Denlinger et al. | Sep 2020 | A1 |
20200289217 | Denlinger et al. | Sep 2020 | A1 |
20200289219 | Denlinger et al. | Sep 2020 | A1 |
20200289220 | Denlinger et al. | Sep 2020 | A1 |
20200289221 | Denlinger et al. | Sep 2020 | A1 |
20200289222 | Denlinger et al. | Sep 2020 | A1 |
20200289223 | Denlinger et al. | Sep 2020 | A1 |
20200289228 | Denlinger et al. | Sep 2020 | A1 |
20200289229 | Denlinger et al. | Sep 2020 | A1 |
20200289230 | Denlinger et al. | Sep 2020 | A1 |
20200291476 | Deng et al. | Sep 2020 | A1 |
20200315721 | Rabindran | Oct 2020 | A1 |
20200367972 | Zhang et al. | Nov 2020 | A1 |
20200397266 | Hufford | Dec 2020 | A1 |
20200405395 | Gullotti et al. | Dec 2020 | A1 |
20210068908 | Thienphrapa et al. | Mar 2021 | A1 |
20210137634 | Lang | May 2021 | A1 |
20210196098 | Shelton, IV et al. | Jul 2021 | A1 |
20210196108 | Shelton, IV | Jul 2021 | A1 |
20210196109 | Shelton, IV et al. | Jul 2021 | A1 |
20210196381 | Eckert et al. | Jul 2021 | A1 |
20210196382 | Mumaw et al. | Jul 2021 | A1 |
20210196383 | Shelton, IV et al. | Jul 2021 | A1 |
20210196384 | Shelton, IV et al. | Jul 2021 | A1 |
20210196385 | Shelton, IV et al. | Jul 2021 | A1 |
20210196386 | Shelton, IV et al. | Jul 2021 | A1 |
20210196423 | Shelton, IV et al. | Jul 2021 | A1 |
20210196424 | Shelton, IV et al. | Jul 2021 | A1 |
20210196425 | Shelton, IV et al. | Jul 2021 | A1 |
20210199557 | Shelton, IV et al. | Jul 2021 | A1 |
20210205019 | Shelton, IV et al. | Jul 2021 | A1 |
20210212792 | Shelton, IV et al. | Jul 2021 | A1 |
20210212794 | Shelton, IV et al. | Jul 2021 | A1 |
20210259660 | Bharat et al. | Aug 2021 | A1 |
20210259789 | Wright et al. | Aug 2021 | A1 |
20210275251 | Shelton, IV et al. | Sep 2021 | A1 |
20210275252 | Shelton, IV et al. | Sep 2021 | A1 |
20210282861 | Eckert et al. | Sep 2021 | A1 |
20210307835 | Shelton, IV et al. | Oct 2021 | A1 |
20210307865 | Shelton, IV et al. | Oct 2021 | A1 |
20210307866 | Shelton, IV et al. | Oct 2021 | A1 |
20210307867 | Shelton, IV et al. | Oct 2021 | A1 |
20210307868 | Shelton, IV et al. | Oct 2021 | A1 |
20210307869 | Shelton, IV et al. | Oct 2021 | A1 |
20210307870 | Shelton, IV et al. | Oct 2021 | A1 |
20220000559 | Leonard et al. | Jan 2022 | A1 |
20220000565 | Gururaj et al. | Jan 2022 | A1 |
20220047259 | Prior et al. | Feb 2022 | A1 |
20220133412 | Tolkowsky et al. | May 2022 | A1 |
Number | Date | Country |
---|---|---|
109011149 | Dec 2018 | CN |
102015115903 | Mar 2017 | DE |
2754383 | Jul 2014 | EP |
2006280591 | Oct 2006 | JP |
4106991 | Jun 2008 | JP |
20120068597 | Jun 2012 | KR |
WO-2008033133 | Mar 2008 | WO |
WO-2013093391 | Jun 2013 | WO |
WO-2013163391 | Oct 2013 | WO |
WO-2015135058 | Sep 2015 | WO |
WO-2018200767 | Nov 2018 | WO |
WO-2019130085 | Jul 2019 | WO |
WO-2020116991 | Jun 2020 | WO |
Entry |
---|
Ge, Jiawei et al., “Landmark-Guided Deformable Image Registration for Supervised Autonomous Robotic Tumor Resection,” Advances in Intelligent Data Analysis XIX, LNCS, Springer International Publishing, pp. 320-328, Oct. 10, 2019. |
Kurata et al. “Time-of-flight Near-infrared Spectroscopy for Nondestructive Measurement of Internal Quality in Grapefruit,” J. Amer. Soc. Hort. Sci. 138(3): 225-228, 2013. |
Thyroid Fine Needle Aspiration (FNA) Biopsy, retrieved from www.fairview.org/patient-education/90246 on Feb. 4, 2020. 3 pages. |
Open Technique for Low Anterior Resection, retrieved from https://abdominalkey.com/open-technique-for-low-anterior-resection/ on Feb. 4, 2020. 6 pages. |
Sukumar et al., “Robotic Partial Nephrectomy Using Robotic Bulldog Clamps,” JSLS: Journal of the Society of Laparoendoscopic Surgeons, 15(4), pp. 520-526, 2011. |
X12C4 Robotic Drop-In, retrieved from https://bkultrasound.com/transducers/x12c4-robotic-drop-in on Feb. 13, 2020. 2 pages. |
“ATM-MPLS Networking Version 2.0, af-aic-0178.001” ATM Standard, The ATM Forum Technical Committee, published Aug. 2003. |
IEEE Std 802.3-2012 (Revision of IEEE Std 802.3-2008, published Dec. 28, 2012. |
Lacy, Antonio, “Main Steps to Perform a Sleeve Gastrectomy,” retrieved from https://aischannel.com/society/main-steps-to-perform-a-sleeve-gastrectomy/ on Feb. 14, 2020. pp. 1-7, Jun. 11, 2015. |
Elhajj, et al., “Sleeve Gastrectomy Surgical Assistive Instrument for Accurate Remnant Stomach Volume,” ASME, J. Med. Devices, vol. 4, pp. 1-10, Jun. 2010. |
Brecht, Hans-Peter et al., “Whole-body three-dimensional optoacoustic tomography system for small animals,” Journal of Biomedical Optics, vol. 14, No. 6, 064007-1-064007-7 (2009). |
Number | Date | Country | |
---|---|---|---|
20200015903 A1 | Jan 2020 | US |
Number | Date | Country | |
---|---|---|---|
62698625 | Jul 2018 | US |