Systems and methods for tracking robotically controlled medical instruments

Information

  • Patent Grant
  • 11969157
  • Patent Number
    11,969,157
  • Date Filed
    Friday, April 28, 2023
    a year ago
  • Date Issued
    Tuesday, April 30, 2024
    15 days ago
Abstract
Systems and methods are described herein for tracking an elongate instrument or other medical instrument in an image.
Description
TECHNICAL FIELD

The disclosure relates generally to medical instruments, such as elongate steerable instruments for minimally-invasive intervention or diagnosis, and more particularly to methods, systems, and apparatus for controlling or tracking the location, position, orientation or shape of one or more parts of a medical instrument using registration techniques.


BACKGROUND

Currently known minimally invasive procedures for diagnosis and treatment of medical conditions use shapeable instruments, such as steerable devices, flexible catheters or more rigid arms or shafts, to approach and address various tissue structures within the body. For various reasons, it is highly valuable to be able to determine the 3-dimensional spatial position of portions of such shapeable instruments relative to other structures, such as the operating table, other instruments, or pertinent anatomical tissue structures. Such information can be used for a variety of reasons, including, but not limited to: improve device control; to improve mapping of the region; to adapt control system parameters (whether kinematic and/or solid mechanic parameters); to estimate, plan and/or control reaction forces of the device upon the anatomy; and/or to even monitor the system characteristics for determination of mechanical problems. Alternatively, or in combination, shape information can be useful to simply visualize the tool with respect to the anatomy or other regions whether real or virtual.


In many conventional systems, the catheter (or other shapeable instrument) is controlled in an open-loop manner, as described in U.S. Pat. No. 8,460,236 on Jun. 11, 2013, the contents of which are incorporated by reference in its entirety. However, at times the assumed motion of the catheter does not match the actual motion of the catheter. One such reason for this issue is the presence of unanticipated or unmodeled constraints imposed by the patient's anatomy.


Thus to perform certain desired applications, such as, for example, instinctive driving, shape feedback, and driving in a fluoroscopy view or a model, there exists a need for tool sensors to be properly registered to the patient in real time. Moreover, there remains a need to apply the information gained by spatial information or shape and applying this information to produce improved device control or improved modeling when directing a robotic or similar device. There also remains a need to apply such controls to medical procedures and equipment.


SUMMARY

A robotic system for manipulating a tool with respect to a target space is disclosed herein. The tool comprises a sensor coupled thereto. The system comprises a robotic drive system and a controller. The robotic drive system comprises at least one actuator and is configured to couple with the tool to position the tool with respect to the target space. The controller is configured to use a registration between a sensor frame and a target space frame such that the controller can direct the robotic drive system in the target space frame using the registration. In some exemplary arrangements, the controller is configured to combine a plurality of discrete registrations to produce a combined registration between the sensor frame and the target space frame.


Other and further exemplary configurations and advantages thereof will become apparent from the following detailed description when read in view of the accompanying figures.





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1A illustrates a variation of a localization system in a typical operation room set up.



FIG. 1B illustrates a 3D Model frame.



FIG. 2 illustrates an exemplary robotic surgical system.



FIG. 3 is a schematic representation of a first registration technique of correlating a sensor reference frame to selective reference frames.



FIG. 4 is a flow chart that illustrates a method of transforming a reference frame for a sensor of a surgical tool into a target reference frame.



FIG. 5 is a flow chart that illustrates a method of transforming a reference frame associated with a tool into a target reference frame.



FIG. 6 is a flow chart that illustrates a method of transforming a reference frame associated with a tool into a target reference frame utilizing medical appliances.



FIG. 7 is a flow chart that illustrates a method of using a sensor to transform a reference frame associated with a tool into a target reference frame.



FIG. 8 is a schematic illustration of a method of using an intravascular imaging sensor coupled with a shape sensor to transform a reference frame associated with a tool into a target reference frame.





DETAILED DESCRIPTION

Various localization systems and methods for tracking an elongate instrument or tool, e.g., a robotically controlled elongate instrument, in real time, in a clinical or other environment, are described herein. The term “localization” is used in the art in reference to systems for determining and/or monitoring the position of objects, such as medical instruments or tools in a reference coordinate system. Various instruments are contemplated for use in the various systems described herein. In one exemplary arrangement, elongate instruments are contemplated, such as, e.g., a catheter or vascular catheter. The various methods and systems may include integrating or registering a localization system or a localization sensor coupled to a surgical tool, with an image. A fiber optic tracking or localization system is just one, non-limiting example of a system that allows for the tracking of a location, position and/or orientation of a localization sensor placed. Various other localization sensors may be utilized, e.g., electromagnetic sensors, and other sensors for detecting or controlling the movement of medical equipment. When the localization sensor is integrated into an image, it enhances the capabilities of an instrument control or tracking system by allowing a user or doctor to easily navigate the instrument through the complex anatomy without exposing the patient to excessive radiation over a prolonged period of time.


The localization data or tracking information of a localization sensor may be registered to the desired image or model to allow for navigation of an elongate instrument through the image or model to accurately represent movement of the elongate instrument within a patient. Registration is a process that requires relating a reference frame of a sensor to another reference frame of interest. If the positions, orientations or shapes of two or more objects are known in the same reference frame, then the actual positions, orientations or shapes of each object relative to each other may be ascertained. Thus, with this information, one can drive or manipulate one of the objects relative to the other objects.


In most interventional procedures, the reference frame of interest is the visualization frame. The reference frame is the frame that the doctor is viewing, such as a patient or a live 2D/3D image such fluoroscopy, ultrasound or others. Thus, the goal of registration is to determine the relationship of a frame of a sensor integrated into a tool or element in the surgical suite within the frame of reference of the patient, as represented in a 2D/3D image.


When the tool is registered to a 3D model, the user can drive and manipulate the tool in the 3D model. This technique provides an advantage in that there is no longer a need for live fluoroscopy and radiation during a procedure. The tool is localized to the 3D model and the position, shape and orientation of the tool is visible to the user. Since the tool position, shape and orientation is updated real time by a localization sensor, an image of the tool in the virtual representation of the 3D model will be updated as it is being advanced into the patient. The sensor is localized to the reference frame of the 3D model; therefore the orientation of a tip of the tool is known relative to the 3D model. This enables driving of the tool (such as a catheter) with 3 dimensional views of the anatomy and hence improves visualization and control during a surgical procedure.


However, many localization sensors are incremental measurement sensors, where the position and orientation of a particular point is calculated and dependent on the previously calculated orientation and positions/point spacings. Thus, the localization sensor operating in any medical system needs to be registered with a coordinate system, frame or image that is useful to an operator, such as the pre-operative 3D model or a fluoroscopic image. For incremental measurement sensors, such registration is challenging because the coordinate system or frame of the sensor is not always easily related to the coordinate system of interest (i.e., the pre-operative 3D model).


Moreover, the relationship between the sensor and the coordinate system of the interest may change over time during a procedure. For example, in one exemplary robotic system, a fiber optic sensor may have its reference frame based physically in a splayer for a catheter. Thus, as the splayer is robotically driven during a surgical procedure, the position and orientation of the bases of the fiber will change with respect to other reference frames.


In addition to changing positions of reference frames, the registration process often requires information about the imaging system providing the image, such as its physical dimensions and/or the details about the imaging techniques used to acquire a particular 3D model or other image. Due to the variability in equipment used in a clinical environment, in certain situations there may be no guarantee that such information will be available or easily obtainable to an outside party.


As such, various techniques to estimate system parameters and various registration techniques may help facilitate the clinical use of localization technology.


In certain variations, a method for tracking a robotically controlled elongate instrument in real time may include displaying an image of a patient's anatomy. A localization sensor may then be coupled to the robotically controlled instrument. The localization sensor may provide localization data of the sensor and/or instrument. Moreover, different sensors may be registered to specific tools, thereby enabling tool differentiation. The localization data from the localization sensor may be registered to the image. Registering may include transforming localization data generated by the localization sensor to the coordinate system or frame of the image such that localization data of the elongate instrument, to which the localization sensor is coupled, is overlaid on the image. The coordinate system of the localization sensor may be transformed or translated to the coordinate system of the image through one or more transformations, and optionally through other coordinate systems, to register the localization data to the image. As a result, a continuously or substantially continuously updated location of at least a portion of the elongate instrument is provided in the image of the anatomy of a patient, which allows for or facilitates robotic navigation or control of the elongate instrument through the anatomy e.g., through the vasculature of a patient.


The location, position and/or orientation of the localization sensor may be continuously tracked to allow for accurate manipulation of the elongate instrument in or through the anatomy of a patient. Various types of images may be utilized in the methods and systems described herein. For example, an image may be generated by CT or 2D or 3D fluoroscopy. An image may include a 3D or 2D anatomical model or a 2D or 3D fluoroscopic image or other types of images useful for visualizing an anatomy of a patient to perform various medical procedures.


When using a fluoroscopy image, an image intensifier may be utilized. Localization data from the localization sensor may be registered to a fluoroscopy coordinate system of a fluoroscopy image coupled to the image intensifier. In order to register the localization data from the localization sensor to the fluoroscopy image, various parameters may be ascertained or known. For example, such parameters may include: a distance from an X-ray source to the image intensifier, a distance from the source to a bed, a size of the image intensifier, and/or the axis of rotation of a C-arm of the fluoroscopy system.


In certain variations, localization data can be registered to a 3D anatomical model or a fluoroscopy image. The techniques used to perform the registration vary depending on the target. Where localization data is registered to a fluoroscopy image, the 2D nature of the fluoroscopy images may require that multiple images be taken at different angles before the registration process is complete.



FIG. 1A is a schematic of a typical operation room set up for a robotic surgical system. More specifically, a typical robotic surgical system 10 includes a table 12 upon which a patient 14 will be placed, a fluoroscopy system 16, and a surgical tool, such as a catheter 18 (best seen in FIG. 2). Attached to the table 12 is a setup joint arm 20 to which a remote catheter manipulator (RCM) 22 is operatively connected. A splayer 24 may be mounted to the RCM 22. A surgical tool, such as a catheter, is operatively connected to the splayer 24. A fiber sensor 26 may be operatively connected to the surgical tool. The fluoroscopy system 16 includes a C-arm 28. A fluoroscopy panel 30 is mounted to the C-arm 28. The C-arm is selectively moveable during the procedure to permit various images of the patient to be taken by the fluoroscopy panel 30.


Additional portions of the robotic surgical system 10 may be further seen in FIG. 2. More specifically, robotic surgical system 10 may further comprise an operator control station 31, which may be remotely positioned with respect to table 12. A communication link 32 transfers signals between the operator control station 31 and the RCM 22. The operator control station 31 includes a control console 34, a computer 36, a computer interface, such as a mouse, a visual display system 38 and a master input device 40. The master input device 40 may include, but is not limited to, a multi-degree of freedom device having multiple joints and associated encoders.


Each element of the robotic surgical system 10 positioned within the operating suite may define a separate reference frame to which sensors may be localized. More specifically, separate reference frames may be defined for each of elements of the robotic surgical system 10. Such reference frames may include the following: a table reference frame TRF for the table 12, a setup joint frame SJF for the setup joint 20, an RCM reference frame RRF for the remote catheter manipulator (RCM) 22, a splayer reference frame SRF, a fluoroscopy reference frame FF. Separate reference frames may also be defined for a patient—patient reference frame PRR, a reference frame FRF for a sensor disposed within a surgical tool, and a pre-operative 3D frame AMF (best seen in FIG. 1B).


To relate a coordinate frame of a fiber optic sensor of a tool to either a fluoroscopy frame FF, or a pre-operative 3D frame AMF, a variety registration techniques is proposed herein. Generally, the techniques proposed herein fall into several categories. A first category involves using image processing or vision techniques to relate a reference frame RFR of a fiber sensor directly to an image or 3D model. This technique may be accomplished manually by a user or done automatically using image processing techniques. Another category to coordinate the reference frame FRF of a fiber optic sensor involves using knowledge about hardware, and potentially other sensors and or position of the fiber. Further discussion of these techniques is set forth below.


Registration to Fluroscopy Coordinate Frame


Referring to FIG. 3, the first category of registration techniques will now be described. The first category relates the coordinate system of the sensor reference frame FRF to a fluoroscopy reference frame FF directly. This technique utilizes fluoroscopy images taken during the surgical procedure by the fluoroscopy system 30, in real-time.


More specifically, an exemplary registration process 200 is illustrated in the flow chart of FIG. 4. The process 200 begins by inserting a tool 202 into a patient. As described above, in one exemplary configuration, the tool 202 is a catheter 18, which may be inserted by an RCM 22. Next, in step 204 an intra-operative image is taken of the tool 18.


In one exemplary arrangement, the intra-operative image is a fluoroscopy image taken by fluoroscopy system 30. Next, distinctive elements of the tool are identified in the fluoroscopy image in step 206. In one exemplary configuration, the identification step 206 may be accomplished by instructing the user to select certain marked points of a catheter 18 in the fluoroscopy image at the work station 31. Examples of marked points include, but are not limited to, physical features of the catheter 18 such as the tip of the catheter 18, certain shapes and an articulation band. In other exemplary configurations, fluoroscopy markers may be disposed on the catheter.


Once the selected points are identified in the fluoroscopy image, in the next step 208, coordinates of the selected points of the catheter 18 may be compared to corresponding measured points of elements of the catheter. In one exemplary configuration, measured points from a tool sensor operatively connected to the tool 18 may be used. More specifically, in one exemplary configuration, the tool sensor is a fiber optic sensor. Information about the fiber optic sensor will be known in relation to the features on the catheter from an in-factory calibration. This comparison can be used to determine a transformation matrix that can be used to transform a reference frame FRF for a sensor disposed within the surgical tool to into the fluoroscopy reference frame FF. This transformation then localizes the tool relative to the intra-operative fluoroscopy image.


Once the fiber sensor of the tool has been registered or localized to the fluoroscopy image, the tool operator can now move or drive the tool to various, desired points visualized in the fluoroscopy image. Moreover, the computer 36 may be configured to track the marked points over time, such that an appropriate transformation may be updated.


In one exemplary configuration, the identifiable markers need not be on the portion of the tool that is inserted into the patient. For example, markers may be embedded on a splayer 24, which may allow for larger and more complex markers to provide enhanced registration capabilities.


As described above, in addition to utilizing fluoroscopy marked points, it is also contemplated that distinct shapes that may be visible under floursocopy may also be used. However, this technique will require some image segmentation.


With respect to the proposed technique of localizing a sensor reference frame FRF to the fluoroscopy reference frame FF, the localization sensor could serve to reduce the use of fluoroscopy during a procedure. More specifically, use of fluoroscopy will only be required when re-registration appears to be required from the captured image and the data obtained from the sensor if the accuracy of the registration needs to be improved at any point during the procedure.


In certain arrangements, it may be desirable to further register the tool to a 3D model reference frame AMF, as illustrated in FIG. 3. Registration to the 3D Model is discussed more fully below.


Registration Through Successive Physical Components


Another technique proposed to register a tool 18 to a desired reference frame involves the use of physical components of the medical system 10 and multiplying successive transformations. This proposed technique 300 is illustrated schematically in FIG. 5 and involves finding a transformation path from a tool reference frame such as a fiber sensor, splayer 24, or catheter 18, to the table 12, as in most surgical suite setups, the table location is generally known with respect to the fluoroscopy system 30. More specifically, registration technique 300 involves determining a tool reference frame 302 (where the tool reference frame may be defined as the sensor reference frame FRF, splayer reference frame SRF or a catheter reference frame) and correlating the tool reference frame to a table reference frame TRF in a second step 304, thereby registering the tool 18 to the table 12. Registering the too 118 to the table 12 will serve to provide necessary information to permit registration to an additional target frame, such as a fluoroscopy reference frame FF, for example. Because the table 12 location is typically known with respect to a fluoroscopy system 30, once the tool 18 is registered to the table reference frame TRF, a comparison of set reference points of the table 12 with corresponding reference points in a fluoroscopy image may be used to determine a transformation matrix to transform the table reference frame TRF into the fluoroscopy reference frame FF. This transformation then localizes the tool relative to the intra-operative fluoroscopy image.


However, it is understood that the present disclosure does not require that the tool 18 be registered to the table 12. Indeed, it is expressly contemplated that registration of the tool 18 to other physical components within the surgical suite may also be utilized. This proposed technique requires the use of other sensors in addition to, or alternative to a fiber sensor, however. Exemplary configurations of registration through physical surgical suite components is are discussed in further detail below.


One exemplary method of performing registration through successive physical components is illustrated in the flow chart in FIG. 6. In this technique, the registration process 400 begins with the step 402 of determining the location of the setup joint 20 with respect to the table 12. Encoders on the RCM 22 and setup joint 20, with kinematic models may be used to determine the location of the setup joint 20 with respect to the table 12. More specifically, the encoders assist with determining the location of the RCM 22 with respect to the table 12. With the location value of the position that the setup joint 20 is fixed to the table 12, the location of the splayer carriage 24 carried by the RCM 22 with respect to the table 12 can be determined; i.e., the setup joint reference frame SJF is localized with the RCM reference frame RRF. Because information about the catheter will be known in relation to the splayer carriage 24 from an in-factory calibration, in step 404 of the registration process 400, a comparison of the splayer carriage 24 information with the can be used to determine a transformation matrix that can be used to transform the splayer carriage reference frame SRF to the table reference frame TRF. As described above, because the table 12 location is known with respect to the fluoroscopy system 30, in step 406 another transformation may be done from the table reference frame TRF to the fluoroscopy reference frame FF. This final transformation, i.e., from the table reference frame TRF to the fluoroscopy reference frame FF, then localizes the tool relative to the intra-operative fluoroscopy image.


In another exemplary method of performing registration through successive physical components, inertial sensors on the RCM 22, coupled with the information about the initial position of the RCM 22 on the table 12, may be used to assist in localizing the catheter splayer reference frame SRF to the table reference frame TRF. More specifically, once the RCM 22 is localized to the table reference frame TRF, the catheter splayer reference frame SRF may be localized to the table reference frame TRF, as the position of the catheter splayer 24 with respect to the RCM 22 will be known from in-factory calibration.


Yet another exemplary method 500 of performing registration through physical components is illustrated in FIG. 7. The method 500 uses a second fiber optic sensor. In a first step 502, one end of the fiber optic sensor is fixed to the table 12. Next, in step 504, the other end of the sensor is fixed to the splayer 24 in a known orientation/position. In this technique, a position and orientation transformation between the tip and base of the fiber sensor may be determined, thereby localizing the catheter splayer reference frame SRF to the table reference frame TRF in step 506. However, it is understood that the initial position of the fix point at the table must be known. Once the catheter splayer reference frame SRF is localized to the table reference frame TRF, because the table 12 location is known with respect to the fluoroscopy system 30, in step 508 another transformation may be done from the table reference frame TRF to the fluoroscopy reference frame FF. This final transformation, i.e., from the table reference frame TRF to the fluoroscopy reference frame FF, then localizes the tool relative to the intra-operative fluoroscopy image.


A further exemplary method of performing registration of a surgical tool to a physical component includes using electromagnetic sensors to track the location of the splayer 24 with respect to an electromagnetic sensor at a known location on the table 12. In using this technique, because the tool location is calibrated to the splayer 24 in the factory, once the splayer 24 is localized to the table reference frame TRF, the tool may be localized to the fluoroscopy reference frame FF as the table 12 is known with respect to the fluoroscopy system 30.


In yet another exemplary method, instead of electromagnetic sensors, overhead cameras or other visualization techniques may be employed to track distinct features on the splayer 24 and the table 12 to determine the respective orientation and position with regard to each other.


A further technique may use the range sensors (such as, e.g., IR or ultrasound) to find the distance to several distinct and predetermined points on the table 12 and the splayer 24. Once the splayer 24 is localized to the table reference frame TRF, the tool may be localized to the fluoroscopy reference frame FF as the table 12 is known with respect to the fluoroscopy system 30.


All of the above techniques serve to register the tool to a physical component within the surgical suite, such as, for example, the table 12. Some of the above techniques require the RCM 22 and setup joint 20 to be registered to the table 12. That pre-registration step may be achieved by using some known feature on the table 12 that the setup joint 20 may reference. In another exemplary configuration, the tip of a sensor equipped tool may be used to touch or register the known feature on the table 12 to locate the table 12 with respect to other equipment within the surgical suite.


The kinematics of the RCM 22 can also be calculated by holding the tip of a fiber optic equipped tool in an arbitrary fixed location and cycling through the various axes of the RCM 22. By keeping the tip in a fixed location, the relative changes to the fiber origin can be observed, and thus the kinematics of the system can be determined and localized to the table 12. Once localized to the table reference frame TRF, the tool may then be localized to the fluoroscopy reference frame FF, as discussed above.


In addition to adding the sensors discussed in the above techniques, additional modifications may be made to the location of the fiber base to facilitate registering the fiber sensor to the physical structure within the suite, such as, for example, the table 12. For example, one modification is to extend the length of a fiber in the catheter so that the origin/base can be extended out of the splayer 24 and attached to a fixture having a known location on the table 12. Once localized to the table reference frame TRF, the tool may then be localized to the fluoroscopy reference frame FF, as discussed above.


Registration to a 3D Model


Registration of the tool to a 3D Model is also contemplated in this disclosure. Such registration may be performed directly from the fiber sensor reference frame FRF to the 3D Model frame AMF. In one exemplary technique, the operator is utilized. When the tool (such as the catheter) is inserted into the patient, tortuosity can be visualized from the fiber sensor data, as well as on the pre-operative 3D Model. To register the tool in the 3D Model, the operator may translate and rotate the 3D Model so that distinct images and/or features in the tortuosity match or line up with the shape of the fibers. However, in using this technique, every time the patient moves, the tool should be re-registered.


In another exemplary arrangement, rather than have an operator manually match features in the tortuosity, in one technique, a computer algorithm such as automated geometric search or mathematical optimization techniques that segments the model and matches the model and tool shape dynamically may also be used to match various shapes or features from the fiber sensor to the 3D preoperative Model. However, if the patient moves, even slightly, the 3D Model could be mis-registered. Thus, the algorithms may be used to re-register the tool automatically or the user could use an input device, such as a track ball or mouse to move the 3D Model manually.


Another proposed technique may be used to register the fiber sensor to the 3D Model through the fluoroscopy image, as illustrated in FIG. 3. In this technique, any of the above described techniques for registering the surgical tool 12 to the fluoroscopy reference frame FF may be utilized. To register the fluoroscopy reference frame FF to the 3D Model reference frame AMF, in one exemplary configuration, specific anatomical landmarks may be used to provide recognizable reference points. The only requirement for this approach is to have an anatomical landmark that is recognizable in both the fluoroscopy reference frame FF, as well as the pre-operative 3D Model reference frame AMF. Once the recognizable point is identified in the fluoroscopy image, the 3D Model may then by rotated by the operator to line up the recognized points in the fluoroscopy images with the 3D Model images. This action serves to register the fluoroscopy reference frame FF to the frame of the 3D Model AMF. As the tool has previously been localized to the fluoroscopy reference plane FF, so now once the fluoroscopy reference plane FF is so registered, the tool's location within the patient's anatomy may be determined with reference to the 3D Model. Thus, the tool is localized to the 3D Model. In one exemplary configuration, a visual representation to the tool, based on the transformation matrix, may be displayed on the 3D Model. In this manner, the tool operator may then navigate the tool through the 3D Model.


While certain of the above described techniques utilized distinct marked points of a tool, such as a medical catheter, to register the tool with the fluoroscopy image, it is also understood that registration of the tool may occur based on the location of the tool at the distinct anatomical landmarks. In other words, as the tip of the tool can be driven to a known anatomical location in the patient, the 3D Model may then be rotated by the user to overlay the known anatomical location in the 3D Model with the fluoroscopy image, in which the known anatomical location is visible. Such action will also serve to register the tool with the 3D Model or localize the tool in the reference frame of the 3D model reference frame AMF.


In another exemplary configuration, instead of, or in addition to, having the user manually rotate the 3D Model to correspond with the fluoroscopy image to line up distinct landmarks visible in both the fluoroscopy image and the 3D Model, the computer 36 may be programmed to employ a suitable algorithm such as automated geometric search or mathematical optimization techniques configured to match a distinct shape measured by the fiber sensor with a corresponding shape in the 3D Model. In this manner, the tool may also be registered with the 3D Model. The accuracy of this method will depend on the size of vessel that the tool is in, and the degree of curvature of the tool. Accuracy will be improved if the tool is in a smaller vessel and will be worse if the tool is in larger vessels. This automated technique can also be used in conjunction with the manual techniques described above. For example, the computer may be programmed to do automatic registration and suggest a preferred registration but the user may do final adjustments of the model. Once the tool is localized in the 3D Model of the patient's anatomy, the user may then proceed to maneuver the tool in the 3D Model.


Another technique that may be utilized to register the tool to the 3D Model through fluoroscopy system 30 involves the use of radiopaque markers. More specifically, radiopaque markers can be fixed to the anatomy. However, these markers would need to be present during preoperative imaging when the 3D Model is created, and remain in the same location during intraoperative fluoroscopy imaging. With this technique, the position of these markers in the fluoroscopy reference frame FF can be used to correlate to the same markers in the 3D Model reference frame AMF, thereby registering the fiber sensor to the 3D Model reference frame AMF.


Another technique that may be utilized to register the surgical tool to a 3D Model utilizes intravascular imaging. This technique allows for 3D visualization of a surgical tool, such as, a catheter, in the anatomy, but without the use of fluoroscopic imaging. Such a technique can benefit both physicians and patients by improving the ease of tool navigation, as well as and reducing radiation exposure of personnel inside the operating room.


The registration technique 600 begins by utilizing a sensor 602 operatively coupled to the tool to sense a shape of the tool 604 while in the patient. This sensed shape is then mathematically correlated against features of the vascular model such as centerlines or walls in which a larger correlation value corresponds to a better match. The correlation can be performed in real-time on each shape or by batch processing a sequence of shapes. This proposed technique assumes that the tool will always follow a unique configuration through the vasculature, and thus, a global maximum for the correlation exists. However, the correlation may return many local maxima since the tool configuration may follow many different paths between fixed distal and proximal ends. Choosing an incorrect maximum introduces registration error. Furthermore, in some cases, the pre-operative 3D model may differ from the actual vasculature for a number of reasons, including, for example, patient motion or inaccuracies in pre-operative sensing. Such situations also may lead to registration error.


Recent advances in intravascular imaging technology have brought about sensors 604 that can provide information about the local structure of vessel walls 606. Such information may be used for shape registration and environmental mapping. Two examples of these sensors are intravascular ultrasound (NUS) probes, and optical coherence tomography (OCT). Intravascular ultrasound periodically produces a 2-D cross-sectional view of the blood vessel either to the sides of the catheter in standard NUS or to the front of a catheter in Forward-Facing IVUS. Optical Coherence Tomography periodically produces a local 3D view of the vessel into which the tool is inserted. The images produced by these technologies may be processed to provide an estimate of a curve or surface representing the vessel wall 606. The sensors 604 may also determine the location of the catheter's endpoint within the vascular cross-section. Use of the sensors coupled with the tool 602 to provide shape information coupled with information obtainable from sensors 604 configured to provide information about the vessel walls 606 can assist in defining the 3D shape of the blood vessel 608.


Once the shape of the vessel is defined or otherwise reconstructed using the combined sensor data, the shape can be mathematically correlated to the 3D model 610, thereby registering the tool to the 3D Model 612. In implementation, the 3D reconstruction and correlation steps may be combined into a single recursive filtering algorithm. A Bayesian filter (e.g. Kalman Filter (EKF), Unscented Kalman Filter (UKF), or Particle Filter) may be used to develop an estimate of the tool's position relative to the pre-op 3D model given both imaging and sensor 602 information. The filter's state is a set of points or a parametric curve representing the position and shape of the tool 602 with respect to the pre-op 3D model, as well as the velocity of this shape. For accurate registration, patient motion may also be taken into account. Thus, the filter's state may also contains warping parameters for the pre-op 3D model. These warping parameters may be evenly distributed, or may be selected based on the structure of the anatomy around the vasculature. The motion of the structure of the anatomy around the vasculature may be measured using visible light tracking technologies such as stereoscopic cameras, structured light tracking technologies, and/or other localization technologies attached to the patient skin.


The recursive filtering algorithm operates by predicting the motion of the tool in the 3D model, then performing an update of the filter hypothesis given new sensor measurements. At each time-step, a kinematic model of the catheter and control inputs such as current pull-wire tension and displacement may be used to perform the filter's motion update. The filter's measurement update may apply a correction to the tool registration and model warping parameters by comparing a predicted vessel wall with the sensed position and orientation of the vessel from the imaging and sensor measurements. The update effectively executes the correlation between 3-D sensor information and the 3D model. Performing these correlations repeatedly in a recursive filtering framework may provide a real-time catheter position estimate. Furthermore, the filter's parameters may be tuned such that differences between the measurements and the model over a small time constant (ms) will lead to changes in the catheter position estimate in order to filter out high-frequency sensor noise. Differences over a large time constant (seconds) may lead to changes in the model's warping parameters.


Thus, once the tool has been registered to the 3D model, the location of the tool within the 3D model may be determined, allowing an operator to drive the tool within the vasculature using the 3D model without requiring intra-operative fluoroscopy.


Sensors 604 may also be utilized to sense the environment around the tool. Thus. once the tool is registered to the 3D the model, this environmental information, such as, for example, vascular occlusions may be displayed at the correct position in the 3D Model.


More specifically, after tool registration, the intravascular imaging sensor 604 provides a mechanism to sense and display features of the environment surrounding the tool without the use of fluoroscopy. There are many ways to display this information. One non-limiting option is to simply provide a display of a real-time view of the imaging sensor's output alongside a view of the catheter's location in the 3D model or superimposed on top of the 3D model. Another option may be to analyze the intravascular image to detect environmental changes. For example, IVUS image processing techniques can be used to detect areas of plaque in the image. This information can be used to annotate the IVUS image in order to aleli the physician to environmental conditions. Since a combination of IVUS and sensor data 602 may provide 3D information on the structure of these plaque formations, the 3D pre-op model can also be annotated. In this way, the existing work that has used IVUS to perform vascular sensing may be leveraged by the combined IVUS and sensor system to provide a 3D view of the environment to the physician.


Each of the individual variations described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other variations. Modifications may be made to adapt a particular situation, material, composition of matter, process, process act(s) or step(s) to the objective(s), spirit or scope of the present invention.


Methods recited herein may be carried out in any order of the recited events which is logically possible, as well as the recited order of events. Furthermore, where a range of values is provided, every intervening value between the upper and lower limit of that range and any other stated or intervening value in that stated range is encompassed within the invention. Also, any optional feature of the inventive variations described may be set forth and claimed independently, or in combination with any one or more of the features described herein.


All existing subject matter mentioned herein (e.g., publications, patents, patent applications and hardware) is incorporated by reference herein in its entirety except insofar as the subject matter may conflict with that of the present invention (in which case what is present herein shall prevail). The referenced items are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present invention is not entitled to antedate such material by virtue of prior invention.


Reference to a singular item, includes the possibility that there are plural of the same items present. More specifically, as used herein and in the appended claims, the singular forms “a,” “an,” “said” and “the” include plural referents unless the context clearly dictates otherwise. It is further noted that the claims may be drafted to exclude any optional element. As such, this statement is intended to serve as antecedent basis for use of such exclusive terminology as “solely,” “only” and the like in connection with the recitation of claim elements, or use of a “negative” limitation. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.


This disclosure is not intended to be limited to the scope of the particular forms set forth, but is intended to cover alternatives, modifications, and equivalents of the variations described herein. Further, the scope of the disclosure fully encompasses other variations that may become obvious to those skilled in the art in view of this disclosure. The scope of the present invention is limited only by the appended claims.


While multiple embodiments and variations of the many aspects of the invention have been disclosed and described herein, such disclosure is provided for purposes of illustration only. Many combinations and permutations of the disclosed system are useful in minimally invasive medical intervention and diagnosis, and the system is configured to be flexible. The foregoing illustrated and described embodiments of the invention are susceptible to various modifications and alternative forms, and it should be understood that the invention generally, as well as the specific embodiments described herein, are not limited to the particular forms or methods disclosed, but also cover all modifications, equivalents and alternatives falling within the scope of the appended claims. Further, the various features and aspects of the illustrated embodiments may be incorporated into other embodiments, even if no so described herein, as will be apparent to those skilled in the art.

Claims
  • 1. A robotic drive system comprising: (a) a tool having one or more distinctive elements;(b) one or more sensors coupled to the tool;(c) an input device; and(d) a controller, the input device being coupled with the controller, the controller being configured to register the tool to a fluoroscopy image of an anatomy of a patient by: (i) identifying one or more of the distinctive elements of the tool in the fluoroscopy image, the identifying including receiving a user input via the input device, the user input including the user selecting one or more certain marked points of the tool in the fluoroscopy image, the one or more distinctive elements being defined by the certain one or more marked points selected by the user such that the user identifies the one or more distinctive elements,(ii) tracking a location of the tool using the one or more sensors,(iii) comparing the one or more distinctive elements of the tool in the fluoroscopy image to one or more corresponding measured points of the tool to generate a registration of the tool to the fluoroscopy image, and(iv) determining a location of the tool in the fluoroscopy image of the anatomy, based on the registration of the tool to the fluoroscopy image.
  • 2. The robotic drive system of claim 1, wherein the one or more distinctive elements of the tool comprises one or more of a tip of the tool, a shape of tool, or an articulation band of the tool.
  • 3. The robotic drive system of claim 1, wherein the one or more distinctive elements of the tool comprises one or more fluoroscopy markers.
  • 4. The robotic drive system of claim 3, wherein the one or more fluoroscopy markers is configured to be positioned outside the patient.
  • 5. The robotic drive system of claim 3, further comprising a splayer operatively connected to the tool, wherein the one or more fluoroscopy markers is positioned on the splayer.
  • 6. The robotic drive system of claim 1, wherein the one or more sensors comprises a fiber sensor.
  • 7. The robotic drive system of claim 1, wherein a position, an orientation, or a shape of the one or more sensors are known in relation to the tool.
  • 8. The robotic drive system of claim 1, the controller being further configured to use the location of the tool in the fluoroscopy image to permit intuitive driving of the tool using the robotic drive system.
  • 9. The robotic drive system of claim 1, the controller being further configured to drive the tool to one or more marked points in the fluoroscopy image.
  • 10. A robotic drive system comprising: (a) a tool having one or more distinctive elements;(b) one or more sensors coupled to the tool;(c) an input device; and(d) a controller, the input device being coupled with the controller, the controller being configured to register the tool to a fluoroscopy image of an anatomy of a patient by: (i) tracking a location of the tool using the one or more sensors,(ii) comparing the one or more distinctive elements of the tool in the fluoroscopy image to one or more corresponding measured points of the tool to generate a registration of the tool to the fluoroscopy image, the registration being generated by determining a transformation matrix that transforms one or more reference frames corresponding to the one or more sensors into a fluoroscopy reference frame,(iii) determining a location of the tool in the fluoroscopy image of the anatomy, based on the registration of the tool to the fluoroscopy image, and(iv) displaying a location of the tool in the fluoroscopy image of the anatomy, based on the registration of the tool to the fluoroscopy image.
  • 11. The robotic drive system of claim 10, the controller being configured to register the tool to a fluoroscopy image of an anatomy of a patient by identifying the one or more distinctive elements of the tool.
  • 12. The robotic drive system of claim 11, wherein the one or more distinctive elements of the tool comprises one or more of a tip of the tool, a shape of tool, or an articulation band of the tool.
  • 13. The robotic drive system of claim 11, wherein the one or more distinctive elements of the tool comprises one or more fluoroscopy markers.
  • 14. The robotic drive system of claim 13, wherein the one or more fluoroscopy markers is configured to be positioned outside the patient.
  • 15. The robotic drive system of claim 13, further comprising a splayer operatively connected to the tool, wherein the one or more fluoroscopy markers is positioned on the splayer.
  • 16. The robotic drive system of claim 10, wherein the one or more sensors comprises a fiber sensor.
  • 17. The robotic drive system of claim 10, wherein a position, an orientation, or a shape of the one or more sensors are known in relation to the tool.
  • 18. The robotic drive system of claim 10, the controller being further configured to use the location of the tool in the fluoroscopy image to permit intuitive driving of the tool using the robotic drive system.
  • 19. The robotic drive system of claim 10, the controller being further configured to drive the tool to one or more marked points in the fluoroscopy image.
  • 20. A robotic drive system comprising: (a) a tool having one or more distinctive elements;(b) one or more sensors coupled to the tool;(c) an input device; and(d) a controller, the input device being coupled with the controller, the controller being configured to register the tool to a three-dimensional model of an anatomy of a patient by: (i) tracking a location of the tool in a patient using the one or more sensors,(ii) comparing the one or more distinctive elements of the tool in a three-dimensional model to one or more corresponding measured points of the tool to generate a registration of the tool to the three-dimensional model, the three-dimensional model including anatomy of the patient, the registration being generated by determining a transformation matrix that transforms one or more reference frames corresponding to the one or more sensors into a fluoroscopy reference frame,(iii) determining a location of the tool in the three-dimensional model, based on the registration of the tool to the three-dimensional model, and(iv) displaying a location of the tool in three-dimensional model, based on the registration of the tool to the three-dimensional model.
CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 17/462,073, filed Aug. 31, 2021, issued as U.S. Pat. No. 11,653,905 on May 23, 2023, which is a continuation of U.S. patent application Ser. No. 16/675,832, filed Nov. 6, 2019, issued as U.S. Pat. No. 11,129,602 on Sep. 28, 2021, which is a continuation of U.S. patent application Ser. No. 16/165,377, filed Oct. 19, 2018, issued as U.S. Pat. No. 10,531,864 on Jan. 14, 2020, which is a continuation of U.S. patent application Ser. No. 15/649,522, filed Jul. 13, 2017, now U.S. Pat. No. 10,130,345, which is a continuation of Ser. No. 14/663,021, filed Mar. 19, 2015, now U.S. Pat. No. 9,710,921, which is a continuation of U.S. patent application Ser. No. 13/835,698, filed Mar. 15, 2013, now U.S. Pat. No. 9,014,851, entitled “SYSTEM AND METHODS FOR TRACKING ROBOTICALLY CONTROLLED MEDICAL INSTRUMENTS,” the contents of which are hereby incorporated in its entirety by reference in their entireties for all purposes.

US Referenced Citations (580)
Number Name Date Kind
4745908 Wardle May 1988 A
5273025 Sakiyam et al. Dec 1993 A
5526812 Dumoulin et al. Jun 1996 A
5550953 Seraji Aug 1996 A
5831614 Tognazzini et al. Nov 1998 A
5935075 Casscells Aug 1999 A
6038467 De Bliek et al. Mar 2000 A
6047080 Chen Apr 2000 A
6059718 Taniguchi et al. May 2000 A
6063095 Wang et al. May 2000 A
6167292 Badano Dec 2000 A
6203493 Ben-Haim Mar 2001 B1
6246784 Summers Jun 2001 B1
6246898 Vesely Jun 2001 B1
6332089 Acker Dec 2001 B1
6425865 Salcudean et al. Jul 2002 B1
6466198 Feinstein Oct 2002 B1
6490467 Bucholz Dec 2002 B1
6545760 Froggatt et al. Apr 2003 B1
6553251 Landesmaki Apr 2003 B1
6665554 Charles Dec 2003 B1
6690963 Ben-Haim Feb 2004 B2
6690964 Beiger et al. Feb 2004 B2
6755797 Stouffer Jun 2004 B1
6812842 Dimmer Nov 2004 B2
6899672 Chin May 2005 B2
6926709 Beiger et al. Aug 2005 B2
7180976 Wink Feb 2007 B2
7206627 Abovitz Apr 2007 B2
7233820 Gilboa Jun 2007 B2
7386339 Strommer et al. Jun 2008 B2
7618371 Younge et al. Nov 2009 B2
7697972 Verard Apr 2010 B2
7756563 Higgins Jul 2010 B2
7850642 Moll et al. Dec 2010 B2
7901348 Soper Mar 2011 B2
7935059 Younge et al. May 2011 B2
7972298 Wallace et al. Jul 2011 B2
7974681 Wallace et al. Jul 2011 B2
7976539 Hlavka et al. Jul 2011 B2
8021326 Moll et al. Sep 2011 B2
8041413 Barbagli et al. Oct 2011 B2
8050523 Younge et al. Nov 2011 B2
8052621 Wallace et al. Nov 2011 B2
8052636 Moll et al. Nov 2011 B2
8092397 Wallace et al. Jan 2012 B2
8155403 Tschirren Apr 2012 B2
8190238 Moll et al. May 2012 B2
8257303 Moll et al. Sep 2012 B2
8285364 Barbagli et al. Oct 2012 B2
8290571 Younge et al. Oct 2012 B2
8298135 Ito et al. Oct 2012 B2
8317746 Sewell et al. Nov 2012 B2
8388538 Younge et al. Mar 2013 B2
8388556 Wallace et al. Mar 2013 B2
8391957 Carlson et al. Mar 2013 B2
8394054 Wallace et al. Mar 2013 B2
8409136 Wallace et al. Apr 2013 B2
8409172 Moll et al. Apr 2013 B2
8409234 Stahler et al. Apr 2013 B2
8460236 Roelle et al. Jun 2013 B2
8498691 Moll et al. Jul 2013 B2
8515215 Younge et al. Aug 2013 B2
8617102 Moll et al. Dec 2013 B2
8672837 Roelle et al. Mar 2014 B2
8705903 Younge et al. Apr 2014 B2
8801661 Moll et al. Aug 2014 B2
8811777 Younge et al. Aug 2014 B2
8818143 Younge et al. Aug 2014 B2
8821376 Tolkowsky Sep 2014 B2
8858424 Hasegawa Oct 2014 B2
8864655 Ramamurthy et al. Oct 2014 B2
8926603 Hlavka et al. Jan 2015 B2
8929631 Pfister et al. Jan 2015 B2
8974408 Wallace et al. Mar 2015 B2
9014851 Wong et al. Apr 2015 B2
9039685 Larkin et al. May 2015 B2
9066740 Carlson et al. Jun 2015 B2
9084623 Gomez et al. Jul 2015 B2
9125639 Mathis Sep 2015 B2
9138129 Diolaiti Sep 2015 B2
9173713 Hart et al. Nov 2015 B2
9183354 Baker et al. Nov 2015 B2
9186046 Ramamurthy et al. Nov 2015 B2
9186047 Ramamurthy et al. Nov 2015 B2
9271663 Walker et al. Mar 2016 B2
9272416 Hourtash et al. Mar 2016 B2
9289578 Walker et al. Mar 2016 B2
9404734 Ramamurthy et al. Aug 2016 B2
9441954 Ramamurthy et al. Sep 2016 B2
9457168 Moll et al. Oct 2016 B2
9459087 Dunbar Oct 2016 B2
9498601 Tanner et al. Nov 2016 B2
9500472 Ramamurthy et al. Nov 2016 B2
9500473 Ramamurthy et al. Nov 2016 B2
9504604 Alvarez Nov 2016 B2
9561083 Yu et al. Feb 2017 B2
9603668 Weingarten et al. Mar 2017 B2
9622827 Yu et al. Apr 2017 B2
9629595 Walker et al. Apr 2017 B2
9629682 Wallace et al. Apr 2017 B2
9636184 Lee et al. May 2017 B2
9710921 Wong Jul 2017 B2
9713509 Schuh et al. Jul 2017 B2
9717563 Tognaccini Aug 2017 B2
9726476 Ramamurthy et al. Aug 2017 B2
9727963 Mintz et al. Aug 2017 B2
9737371 Romo et al. Aug 2017 B2
9737373 Schuh Aug 2017 B2
9744335 Jiang Aug 2017 B2
9763741 Alvarez et al. Sep 2017 B2
9788910 Schuh Oct 2017 B2
9844412 Bogusky et al. Dec 2017 B2
9867635 Alvarez et al. Jan 2018 B2
9918681 Wallace et al. Mar 2018 B2
9931025 Graetzel et al. Apr 2018 B1
9949749 Noonan et al. Apr 2018 B2
9955986 Shah May 2018 B2
9962228 Schuh et al. May 2018 B2
9980785 Schuh May 2018 B2
9993313 Schuh et al. Jun 2018 B2
10016900 Meyer et al. Jul 2018 B1
10022192 Ummalaneni Jul 2018 B1
10046140 Kokish et al. Aug 2018 B2
10080576 Romo et al. Sep 2018 B2
10123755 Walker et al. Nov 2018 B2
10130345 Wong et al. Nov 2018 B2
10136950 Schoenefeld Nov 2018 B2
10136959 Mintz et al. Nov 2018 B2
10143360 Roelle et al. Dec 2018 B2
10143526 Walker et al. Dec 2018 B2
10145747 Lin et al. Dec 2018 B1
10149720 Romo Dec 2018 B2
10159532 Ummalaneni et al. Dec 2018 B1
10159533 Moll et al. Dec 2018 B2
10169875 Mintz et al. Jan 2019 B2
10219874 Yu et al. Mar 2019 B2
10231793 Romo Mar 2019 B2
10231867 Alvarez et al. Mar 2019 B2
10244926 Noonan et al. Apr 2019 B2
10278778 State May 2019 B2
10285574 Landey et al. May 2019 B2
10314463 Agrawal et al. Jun 2019 B2
10383765 Alvarez et al. Aug 2019 B2
10398518 Yu et al. Sep 2019 B2
10405939 Romo et al. Sep 2019 B2
10405940 Romo Sep 2019 B2
10426559 Graetzel et al. Oct 2019 B2
10426661 Kintz Oct 2019 B2
10434660 Meyer Oct 2019 B2
10464209 Ho et al. Nov 2019 B2
10470830 Hill Nov 2019 B2
10482599 Mintz et al. Nov 2019 B2
10492741 Walker et al. Dec 2019 B2
10493241 Jiang Dec 2019 B2
10500001 Yu et al. Dec 2019 B2
10517692 Eyre et al. Dec 2019 B2
10524866 Srinivasan Jan 2020 B2
10531864 Wong et al. Jan 2020 B2
10539478 Lin Jan 2020 B2
10555778 Ummalaneni et al. Feb 2020 B2
10639114 Schuh May 2020 B2
10667875 DeFonzo Jun 2020 B2
10702348 Moll et al. Jul 2020 B2
10743751 Landey et al. Aug 2020 B2
10751140 Wallace et al. Aug 2020 B2
10765303 Graetzel et al. Sep 2020 B2
10765487 Ho Sep 2020 B2
10820954 Marsot et al. Nov 2020 B2
10835153 Rafii-Tari et al. Nov 2020 B2
10850013 Hsu Dec 2020 B2
11051681 Roelle et al. Jul 2021 B2
11129602 Wong Sep 2021 B2
11653905 Wong et al. May 2023 B2
20010021843 Bosselmann et al. Sep 2001 A1
20010039421 Heilbrun Nov 2001 A1
20020065455 Ben-Haim et al. May 2002 A1
20020077533 Bieger et al. Jun 2002 A1
20020120188 Brock et al. Aug 2002 A1
20030105603 Hardesty Jun 2003 A1
20030125622 Schweikard Jul 2003 A1
20030181809 Hall et al. Sep 2003 A1
20030195664 Nowlin et al. Oct 2003 A1
20040047044 Dalton Mar 2004 A1
20040072066 Cho et al. Apr 2004 A1
20040097806 Hunter et al. May 2004 A1
20040186349 Ewers Sep 2004 A1
20040249267 Gilboa Dec 2004 A1
20040263535 Birkenbach et al. Dec 2004 A1
20050027397 Niemeyer Feb 2005 A1
20050060006 Pflueger Mar 2005 A1
20050085714 Foley et al. Apr 2005 A1
20050107679 Geiger May 2005 A1
20050143649 Minai et al. Jun 2005 A1
20050143655 Satoh Jun 2005 A1
20050182295 Soper et al. Aug 2005 A1
20050182319 Glossop Aug 2005 A1
20050193451 Quistgaard et al. Sep 2005 A1
20050197557 Strommer et al. Sep 2005 A1
20050256398 Hastings Nov 2005 A1
20050272975 McWeeney et al. Dec 2005 A1
20060004286 Chang Jan 2006 A1
20060013523 Childers et al. Jan 2006 A1
20060015096 Hauck et al. Jan 2006 A1
20060025668 Peterson Feb 2006 A1
20060058643 Florent Mar 2006 A1
20060084860 Geiger Apr 2006 A1
20060095066 Chang May 2006 A1
20060098851 Shoham et al. May 2006 A1
20060100610 Wallace et al. May 2006 A1
20060149134 Soper et al. Jul 2006 A1
20060173290 Lavallee et al. Aug 2006 A1
20060184016 Glossop Aug 2006 A1
20060200026 Wallace et al. Sep 2006 A1
20060209019 Hu Sep 2006 A1
20060258935 Pile-Spellman et al. Nov 2006 A1
20060258938 Hoffman et al. Nov 2006 A1
20070013336 Nowlin et al. Jan 2007 A1
20070032826 Schwartz Feb 2007 A1
20070055128 Glossop Mar 2007 A1
20070055144 Neustadter Mar 2007 A1
20070073136 Metzger Mar 2007 A1
20070083193 Werneth Apr 2007 A1
20070123748 Meglan May 2007 A1
20070135803 Belson Jun 2007 A1
20070135886 Maschke Jun 2007 A1
20070156019 Larkin et al. Jul 2007 A1
20070161857 Durant et al. Jul 2007 A1
20070167743 Honda Jul 2007 A1
20070167801 Webler et al. Jul 2007 A1
20070208252 Makower Sep 2007 A1
20070253599 White et al. Nov 2007 A1
20070265503 Schlesinger et al. Nov 2007 A1
20070269001 Maschke Nov 2007 A1
20070293721 Gilboa Dec 2007 A1
20070299353 Harley et al. Dec 2007 A1
20080027464 Moll et al. Jan 2008 A1
20080071140 Gattani Mar 2008 A1
20080079421 Jensen Apr 2008 A1
20080082109 Moll et al. Apr 2008 A1
20080103389 Begelman et al. May 2008 A1
20080118118 Berger May 2008 A1
20080118135 Averbach May 2008 A1
20080123921 Gielen et al. May 2008 A1
20080140087 Barbagli et al. Jun 2008 A1
20080147089 Loh Jun 2008 A1
20080161681 Hauck Jul 2008 A1
20080183064 Chandonnet Jul 2008 A1
20080183068 Carls et al. Jul 2008 A1
20080183073 Higgins et al. Jul 2008 A1
20080183188 Carls et al. Jul 2008 A1
20080201016 Finlay Aug 2008 A1
20080207997 Higgins et al. Aug 2008 A1
20080212082 Froggatt et al. Sep 2008 A1
20080218770 Moll et al. Sep 2008 A1
20080221425 Olson et al. Sep 2008 A1
20080243142 Gildenberg Oct 2008 A1
20080255505 Carlson et al. Oct 2008 A1
20080262297 Gilboa Oct 2008 A1
20080275349 Halperin Nov 2008 A1
20080275367 Barbagli et al. Nov 2008 A1
20080287963 Rogers et al. Nov 2008 A1
20080300478 Zuhars et al. Dec 2008 A1
20080306490 Lakin et al. Dec 2008 A1
20080312501 Hasegawa et al. Dec 2008 A1
20090012533 Barbagli et al. Jan 2009 A1
20090024141 Stahler et al. Jan 2009 A1
20090030307 Govari Jan 2009 A1
20090054729 Mori Feb 2009 A1
20090076476 Barbagli et al. Mar 2009 A1
20090137952 Ramamurthy May 2009 A1
20090138025 Stahler et al. May 2009 A1
20090149867 Glozman Jun 2009 A1
20090209817 Averbuch Aug 2009 A1
20090227861 Ganatra Sep 2009 A1
20090228020 Wallace et al. Sep 2009 A1
20090248036 Hoffman et al. Oct 2009 A1
20090259230 Khadem Oct 2009 A1
20090262109 Markowitz et al. Oct 2009 A1
20090292166 Ito Nov 2009 A1
20090295797 Sakaguchi Dec 2009 A1
20100008555 Trumer Jan 2010 A1
20100030061 Canfield Feb 2010 A1
20100039506 Sarvestani et al. Feb 2010 A1
20100041949 Tolkowsky Feb 2010 A1
20100054536 Huang Mar 2010 A1
20100113852 Sydora May 2010 A1
20100114115 Schlesinger et al. May 2010 A1
20100121139 OuYang May 2010 A1
20100160733 Gilboa Jun 2010 A1
20100161022 Tolkowsky Jun 2010 A1
20100161129 Costa et al. Jun 2010 A1
20100225209 Goldberg Sep 2010 A1
20100240989 Stoianovici Sep 2010 A1
20100290530 Huang et al. Nov 2010 A1
20100292565 Meyer Nov 2010 A1
20100298641 Tanaka Nov 2010 A1
20100328455 Nam et al. Dec 2010 A1
20100331856 Carlson et al. Dec 2010 A1
20110015648 Alvarez et al. Jan 2011 A1
20110054303 Barrick Mar 2011 A1
20110092808 Shachar Apr 2011 A1
20110152880 Alvarez et al. Jun 2011 A1
20110184238 Higgins Jul 2011 A1
20110234780 Ito Sep 2011 A1
20110238082 Wenderow Sep 2011 A1
20110238083 Moll et al. Sep 2011 A1
20110245665 Nentwick Oct 2011 A1
20110248987 Mitchell Oct 2011 A1
20110249016 Zhang Oct 2011 A1
20110257480 Takahashi Oct 2011 A1
20110270273 Moll et al. Nov 2011 A1
20110276179 Banks et al. Nov 2011 A1
20110295247 Schlesinger et al. Dec 2011 A1
20110295248 Wallace et al. Dec 2011 A1
20110295267 Tanner et al. Dec 2011 A1
20110295268 Roelle et al. Dec 2011 A1
20110319910 Roelle et al. Dec 2011 A1
20120046521 Hunter et al. Feb 2012 A1
20120056986 Popovic Mar 2012 A1
20120059248 Noising Mar 2012 A1
20120062714 Liu Mar 2012 A1
20120065481 Hunter Mar 2012 A1
20120069167 Liu et al. Mar 2012 A1
20120071782 Patil et al. Mar 2012 A1
20120082351 Higgins Apr 2012 A1
20120116253 Wallace et al. May 2012 A1
20120120305 Takahashi May 2012 A1
20120165656 Montag Jun 2012 A1
20120172712 Bar-Tal Jul 2012 A1
20120191079 Moll et al. Jul 2012 A1
20120209069 Popovic Aug 2012 A1
20120209293 Carlson Aug 2012 A1
20120215094 Rahimian et al. Aug 2012 A1
20120219185 Hu Aug 2012 A1
20120230565 Steinberg Sep 2012 A1
20120289777 Chopra Nov 2012 A1
20120289783 Duindam et al. Nov 2012 A1
20120302869 Koyrakh Nov 2012 A1
20130060146 Yang et al. Mar 2013 A1
20130085330 Ramamurthy et al. Apr 2013 A1
20130085331 Ramamurthy et al. Apr 2013 A1
20130085333 Ramamurthy et al. Apr 2013 A1
20130090528 Ramamurthy et al. Apr 2013 A1
20130090530 Ramamurthy Apr 2013 A1
20130090552 Ramamurthy et al. Apr 2013 A1
20130144116 Cooper et al. Jun 2013 A1
20130165945 Roelle Jun 2013 A9
20130190741 Moll et al. Jul 2013 A1
20130204124 Duindam Aug 2013 A1
20130225942 Holsing Aug 2013 A1
20130243153 Sra Sep 2013 A1
20130246334 Ahuja Sep 2013 A1
20130259315 Angot et al. Oct 2013 A1
20130303892 Zhao Nov 2013 A1
20130329977 Tolkowsky Dec 2013 A1
20130345718 Crawford Dec 2013 A1
20140058406 Tsekos Feb 2014 A1
20140072192 Reiner Mar 2014 A1
20140107390 Brown Apr 2014 A1
20140114180 Jain Apr 2014 A1
20140148808 Inkpen et al. Apr 2014 A1
20140142591 Alvarez et al. May 2014 A1
20140148673 Bogusky May 2014 A1
20140180063 Zhao Jun 2014 A1
20140235943 Paris Aug 2014 A1
20140243849 Saglam Aug 2014 A1
20140257746 Dunbar et al. Sep 2014 A1
20140261453 Carlson Sep 2014 A1
20140264081 Walker et al. Sep 2014 A1
20140275988 Walker et al. Sep 2014 A1
20140276033 Brannan Sep 2014 A1
20140276594 Tanner et al. Sep 2014 A1
20140276937 Wong et al. Sep 2014 A1
20140296655 Akhbardeh et al. Oct 2014 A1
20140296657 Izmirli Oct 2014 A1
20140309527 Namati et al. Oct 2014 A1
20140309649 Alvarez et al. Oct 2014 A1
20140343416 Panescu Nov 2014 A1
20140350391 Prisco et al. Nov 2014 A1
20140357984 Wallace et al. Dec 2014 A1
20140364739 Liu Dec 2014 A1
20140364870 Alvarez et al. Dec 2014 A1
20150051482 Liu et al. Feb 2015 A1
20150051592 Kintz Feb 2015 A1
20150054929 Ito et al. Feb 2015 A1
20150057498 Akimoto Feb 2015 A1
20150073266 Brannan Mar 2015 A1
20150119638 Yu et al. Apr 2015 A1
20150133963 Barbagli May 2015 A1
20150141808 Elhawary May 2015 A1
20150141858 Razavi May 2015 A1
20150142013 Tanner et al. May 2015 A1
20150164594 Romo et al. Jun 2015 A1
20150164596 Romo Jun 2015 A1
20150223725 Engel Aug 2015 A1
20150223765 Chopra Aug 2015 A1
20150223897 Kostrzewski et al. Aug 2015 A1
20150223902 Walker et al. Aug 2015 A1
20150255782 Kim et al. Sep 2015 A1
20150265087 Park Sep 2015 A1
20150265359 Camarillo Sep 2015 A1
20150265368 Chopra Sep 2015 A1
20150275986 Cooper Oct 2015 A1
20150287192 Sasaki Oct 2015 A1
20150297133 Jouanique-Dubuis et al. Oct 2015 A1
20150305650 Hunter Oct 2015 A1
20150313503 Seibel et al. Nov 2015 A1
20150335480 Alvarez et al. Nov 2015 A1
20150374956 Bogusky Dec 2015 A1
20160000302 Brown Jan 2016 A1
20160000414 Brown Jan 2016 A1
20160000520 Lachmanovich Jan 2016 A1
20160001038 Romo et al. Jan 2016 A1
20160008033 Hawkins et al. Jan 2016 A1
20160067009 Ramamurthy et al. Mar 2016 A1
20160111192 Suzara Apr 2016 A1
20160128992 Hudson May 2016 A1
20160183841 Duindam et al. Jun 2016 A1
20160199134 Brown et al. Jul 2016 A1
20160206389 Miller Jul 2016 A1
20160213432 Flexman Jul 2016 A1
20160228032 Walker et al. Aug 2016 A1
20160270865 Landey et al. Sep 2016 A1
20160287279 Bovay et al. Oct 2016 A1
20160287346 Hyodo et al. Oct 2016 A1
20160314710 Jarc Oct 2016 A1
20160331469 Hall et al. Nov 2016 A1
20160360947 Lida Dec 2016 A1
20160372743 Cho et al. Dec 2016 A1
20160374541 Agrawal et al. Dec 2016 A1
20170007337 Dan Jan 2017 A1
20170023423 Jackson Jan 2017 A1
20170055851 Al-Ali Mar 2017 A1
20170079725 Hoffman Mar 2017 A1
20170079726 Hoffman Mar 2017 A1
20170086929 Moll et al. Mar 2017 A1
20170100199 Yu et al. Apr 2017 A1
20170119413 Romo May 2017 A1
20170119481 Romo et al. May 2017 A1
20170119484 Tanner et al. May 2017 A1
20170165011 Bovay et al. Jun 2017 A1
20170172673 Yu et al. Jun 2017 A1
20170189118 Chopra Jul 2017 A1
20170202627 Sramek et al. Jul 2017 A1
20170209073 Sramek et al. Jul 2017 A1
20170209224 Walker et al. Jul 2017 A1
20170215808 Shimol et al. Aug 2017 A1
20170215969 Zhai et al. Aug 2017 A1
20170215978 Wallace et al. Aug 2017 A1
20170238807 Veritkov et al. Aug 2017 A9
20170258366 Tupin Sep 2017 A1
20170290631 Lee et al. Oct 2017 A1
20170296032 Li Oct 2017 A1
20170296202 Brown Oct 2017 A1
20170303941 Eisner Oct 2017 A1
20170325896 Donhowe Nov 2017 A1
20170333679 Jiang Nov 2017 A1
20170340241 Yamada Nov 2017 A1
20170340396 Romo et al. Nov 2017 A1
20170348067 Krimsky Dec 2017 A1
20170360508 Germain et al. Dec 2017 A1
20170367782 Schuh et al. Dec 2017 A1
20180025666 Ho et al. Jan 2018 A1
20180055576 Koyrakh Mar 2018 A1
20180055582 Krimsky Mar 2018 A1
20180098690 Iwaki Apr 2018 A1
20180177383 Noonan et al. Jun 2018 A1
20180177556 Noonan et al. Jun 2018 A1
20180214011 Graetzel et al. Aug 2018 A1
20180217734 Koenig et al. Aug 2018 A1
20180221038 Noonan et al. Aug 2018 A1
20180221039 Shah Aug 2018 A1
20180240237 Donhowe et al. Aug 2018 A1
20180250083 Schuh et al. Sep 2018 A1
20180263714 Kostrzewski Sep 2018 A1
20180271616 Schuh et al. Sep 2018 A1
20180279852 Rafii-Tari et al. Oct 2018 A1
20180280660 Landey et al. Oct 2018 A1
20180286108 Hirakawa Oct 2018 A1
20180289243 Landey et al. Oct 2018 A1
20180289431 Draper et al. Oct 2018 A1
20180308232 Gliner Oct 2018 A1
20180308247 Gupta Oct 2018 A1
20180325499 Landey et al. Nov 2018 A1
20180333044 Jenkins Nov 2018 A1
20180360435 Romo Dec 2018 A1
20180368920 Ummalaneni Dec 2018 A1
20190000559 Berman et al. Jan 2019 A1
20190000560 Berman et al. Jan 2019 A1
20190000566 Graetzel et al. Jan 2019 A1
20190000568 Connolly et al. Jan 2019 A1
20190000576 Mintz et al. Jan 2019 A1
20190046814 Senden et al. Feb 2019 A1
20190066314 Abhari Feb 2019 A1
20190086349 Nelson Mar 2019 A1
20190110839 Rafii-Tari et al. Apr 2019 A1
20190151148 Alvarez et al. Apr 2019 A1
20190142519 Siemionow et al. May 2019 A1
20190167366 Ummalaneni Jun 2019 A1
20190175009 Mintz Jun 2019 A1
20190175062 Rafii-Tari et al. Jun 2019 A1
20190175799 Hsu Jun 2019 A1
20190183585 Rafii-Tari et al. Jun 2019 A1
20190183587 Rafii-Tari et al. Jun 2019 A1
20190216548 Ummalaneni Jul 2019 A1
20190216576 Eyre Jul 2019 A1
20190223974 Romo Jul 2019 A1
20190228525 Mintz et al. Jul 2019 A1
20190262086 Connolly et al. Aug 2019 A1
20190269468 Hsu et al. Sep 2019 A1
20190274764 Romo Sep 2019 A1
20190287673 Michihata Sep 2019 A1
20190290109 Agrawal et al. Sep 2019 A1
20190298160 Ummalaneni et al. Oct 2019 A1
20190298460 Al-Jadda Oct 2019 A1
20190298465 Chin Oct 2019 A1
20190336238 Yu Nov 2019 A1
20190365201 Noonan et al. Dec 2019 A1
20190365209 Ye et al. Dec 2019 A1
20190365479 Rafii-Tari Dec 2019 A1
20190365486 Srinivasan et al. Dec 2019 A1
20190375383 Alvarez Dec 2019 A1
20190380787 Ye Dec 2019 A1
20190380797 Yu Dec 2019 A1
20200000533 Schuh Jan 2020 A1
20200022767 Hill Jan 2020 A1
20200038123 Graetzel Feb 2020 A1
20200039086 Meyer Feb 2020 A1
20200046434 Graetzel Feb 2020 A1
20200054408 Schuh et al. Feb 2020 A1
20200060516 Baez Feb 2020 A1
20200078103 Duindam Mar 2020 A1
20200085516 DeFonzo Mar 2020 A1
20200093549 Chin Mar 2020 A1
20200093554 Schuh Mar 2020 A1
20200100845 Julian Apr 2020 A1
20200100855 Leparmentier Apr 2020 A1
20200101264 Jiang Apr 2020 A1
20200107894 Wallace Apr 2020 A1
20200121502 Kintz Apr 2020 A1
20200146769 Eyre May 2020 A1
20200155084 Walker May 2020 A1
20200170720 Ummalaneni Jun 2020 A1
20200171660 Ho Jun 2020 A1
20200188043 Yu Jun 2020 A1
20200197112 Chin Jun 2020 A1
20200206472 Ma Jul 2020 A1
20200217733 Lin Jul 2020 A1
20200222134 Schuh Jul 2020 A1
20200237458 DeFonzo Jul 2020 A1
20200261172 Romo Aug 2020 A1
20200268459 Noonan et al. Aug 2020 A1
20200268460 Tse Aug 2020 A1
20200281787 Ruiz Sep 2020 A1
20200297437 Schuh Sep 2020 A1
20200297444 Camarillo Sep 2020 A1
20200305983 Yampolsky Oct 2020 A1
20200305989 Schuh Oct 2020 A1
20200305992 Schuh Oct 2020 A1
20200315717 Bovay Oct 2020 A1
20200315723 Hassan Oct 2020 A1
20200323596 Moll Oct 2020 A1
20200330167 Romo Oct 2020 A1
20200345216 Jenkins Nov 2020 A1
20200352420 Graetzel Nov 2020 A1
20200360183 Alvarez Nov 2020 A1
20200367726 Landey et al. Nov 2020 A1
20200367981 Ho et al. Nov 2020 A1
20200375678 Wallace Dec 2020 A1
20200405317 Wallace Dec 2020 A1
20200405411 Draper et al. Dec 2020 A1
20200405419 Mao Dec 2020 A1
20200405420 Purohit Dec 2020 A1
20200405423 Schuh Dec 2020 A1
20200405424 Schuh Dec 2020 A1
20200405434 Schuh Dec 2020 A1
20200406002 Romo Dec 2020 A1
20210007819 Schuh Jan 2021 A1
20210008341 Landey et al. Jan 2021 A1
Foreign Referenced Citations (25)
Number Date Country
101147676 Mar 2008 CN
101222882 Jul 2008 CN
102316817 Jan 2012 CN
102458295 May 2012 CN
102973317 Mar 2013 CN
103705307 Apr 2014 CN
103735313 Apr 2014 CN
105559850 May 2016 CN
105559886 May 2016 CN
105611881 May 2016 CN
104931059 Sep 2018 CN
3025630 Jun 2016 EP
20140009359 Jan 2014 KR
101713676 Mar 2017 KR
2569699 Nov 2015 RU
WO 2005087128 Sep 2005 WO
WO 2006051523 May 2006 WO
WO 2006099056 Sep 2006 WO
WO 2009097461 Jun 2007 WO
WO 2013116140 Aug 2013 WO
WO 2014058838 Apr 2014 WO
WO 2015089013 Jun 2015 WO
WO 2017048194 Mar 2017 WO
WO 2017099108 Apr 2017 WO
WO 2017167754 Oct 2017 WO
Non-Patent Literature Citations (39)
Entry
Al-Ahmad, Amin, Jessica D. Grossman, and Paul J. Wang. “Early experience with a computerized robotically controlled catheter system.” Journal of Interventional Cardiac Electrophysiology 12 (2005): 199-202.
Ciuti, Gastone, et al. “Intra-operative monocular 3D reconstruction for image-guided navigation in active locomotion capsule endoscopy.” 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob). IEEE, 2012.
Duncan, Roger. “Sensing Shape: Fiber-Bragg-grating sensor arrays monitor shape at a high resolution.” Spie's OE Magazine (2005): 18-21.
Fallavollita, Pascal. “Acquiring multiview c-arm images to assist cardiac ablation procedures.” EURASIP Journal on Image and Video Processing 2010 (2010): 1-10.
Froggatt, Mark, and Jason Moore. “High-spatial-resolution distributed strain measurement in optical fiber with Rayleigh scatter.” Applied optics 37.10 (1998): 1735-1740.
Gutiérrez, Luis F., et al. “A practical global distortion correction method for an image intensifier based x-ray fluoroscopy system.” Medical physics 35.3 (2008): 997-1007.
Haigron, Pascal, et al. “Depth-map-based scene analysis for active navigation in virtual angioscopy.” IEEE Transactions on Medical Imaging 23.11 (2004): 1380-1390.
Hansen Medical, Inc. 2005, System Overview, product brochure, 2 pp., dated as available at http://hansenmedical.com/system.aspx on Jul. 14, 2006 (accessed Jun. 25, 2019, using the internet archive way back machine).
Hansen Medical, Inc. Bibliography, product brochure, 1 p., dated as available at http://hansenmedical.com/bibliography.aspx on Jul. 14, 2006 (accessed Jun. 25, 2019, using the internet archive way back machine).
Hansen Medical, Inc. dated 2007, Introducing the Sensei Robotic Catheter System, product brochure, 10 pages.
Hansen Medical, Inc. dated 2009, Sensei X Robotic Catheter System, product brochure, 5 pp.
Hansen Medical, Inc. Technology Advantages, product brochure, 1 p., dated as available at http://hansenmedical.com/advantages.aspx on Jul. 13, 2006 (accessed Jun. 25, 2019, using the internet archive way back machine).
http://www.sjmprofessional.com-Products-US-Mapping-and-Visualization-EnSite-Velocity.aspx.
Kiraly, Atilla P., et al. “Three-dimensional human airway segmentation methods for clinical virtual bronchoscopy.” Academic radiology 9.10 (2002): 1153-1168.
Kiraly, Atilla P., et al. “Three-dimensional path planning for virtual bronchoscopy.” IEEE Transactions on Medical Imaging 23.11 (2004): 1365-1379.
Konen, W., M. Scholz, and S. Tombrock. “The VN project: endoscopic image processing for neurosurgery.” Computer Aided Surgery 3.3 (1998): 144-148.
Kumar, Atul, et al. “Stereoscopic visualization of laparoscope image using depth information from 3D model.” Computer methods and programs in biomedicine 113.3 (2014): 862-868.
Livatino, Salvatore, et al. “Stereoscopic visualization and 3-D technologies in medical endoscopic teleoperation.” IEEE Transactions on Industrial Electronics 62.1 (2014): 525-535.
Luó, Xióngbiāo, et al. “Modified hybrid bronchoscope tracking based on sequential monte carlo sampler: Dynamic phantom validation.” Computer Vision-ACCV 2010: 10th Asian Conference on Computer Vision, Queenstown, New Zealand, Nov. 8-12, 2010, Revised Selected Papers, Part III 10. Springer Berlin Heidelberg, 2011.
Marrouche, Nassir F., et al. “Preliminary human experience using a novel robotic catheter remote control.” Heart Rhythm 2.5 (2005): S63.
Mayo Clinic, Robotic Surgery, https://www.mayoclinic.org/tests-procedures/robotic-surgery/about/pac-20394974?p=1, downloaded from the internet on Jul. 12, 2018, 2 pages.
Mourgues, Fabien, Eve Coste-Maniere, and CHIR Team www. inria. fr/chir. “Flexible calibration of actuated stereoscopic endoscope for overlay in robot assisted surgery.” Medical Image Computing and Computer-Assisted Intervention—MICCAI 2002: 5th International Conference Tokyo, Japan, Sep. 25-28, 2002 Proceedings, Part I 5. Springer Berlin Heidelberg, 2002.
Nadeem, Saad, and Arie Kaufman. “Depth reconstruction and computer-aided polyp detection in optical colonoscopy video frames.” arXiv preprint arXiv: 1609.01329 (2016).
Oh, Seil, et al. “Novel robotic catheter remote control system: Safety and accuracy in delivering RF lesions in all 4 cardiac chambers.” Heart Rhythm 2.5 (2005): S277-S278.
Point Cloud, Sep. 10, 2010, Wikipedia, 2 pages.
Racadio, John M., et al. “Live 3D guidance in the interventional radiology suite.” American Journal of Roentgenology 189.6 (2007): W357-W364.
Sato, Masaaki, Tomonori Murayama, and Jun Nakajima. “Techniques of stapler-based navigational thoracoscopic segmentectomy using virtual assisted lung mapping (VAL-MAP).” Journal of thoracic disease 8.Suppl 9 (2016): S716.
Shen, Mali, Stamatia Giannarou, and Guang-Zhong Yang. “Robust camera localisation with depth reconstruction for bronchoscopic navigation.” International journal of computer assisted radiology and surgery 10 (2015): 801-813.
Shi, Chaoyang, et al. “Simultaneous catheter and environment modeling for trans-catheter aortic valve implantation.” 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2014.
Slepian, dated 2010, Robotic Catheter Intervention: the Hansen Medical Sensei Robot Catheter System, PowerPoint presentation, 28 pages.
Solheim, Ole, et al. “Navigated resection of giant intracranial meningiomas based on intraoperative 3D ultrasound.” Acta neurochirurgica 151 (2009): 1143-1151.
Solomon, Stephen B., et al. “Three-dimensional CT-guided bronchoscopy with a real-time electromagnetic position sensor: a comparison of two image registration methods.” Chest 118.6 (2000): 1783-1787.
Song, Kai-Tai, and Chun-Ju Chen. “Autonomous and stable tracking of endoscope instrument tools with monocular camera.” 2012 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, 2012.
St. Jude Medical, EnSite Velocity Cardiac Mapping System, online, http://www.sjmprofessional.com-Products-US-Mapping-and-Visualization-EnSi- te-Velocity.aspx.
Vemuri, Anant Suraj, et al. “Interoperative biopsy site relocalization in endoluminal surgery.” IEEE Transactions on Biomedical Engineering 63.9 (2015): 1862-1873.
Verdaasdonk, R. M., et al. “Effect of microsecond pulse length and tip shape on explosive bubble formation of 2.78 μm Er, Cr; YSGG and 2.94 μm Er:YAG laser.” Proceedings of SPIE, vol. 8221, 12.
Wilson, Emmanuel, et al. “A buyer's guide to electromagnetic tracking systems for clinical applications.” Medical imaging 2008: visualization, image-guided procedures, and modeling. vol. 6918. SPIE, 2008.
Yip, Michael C., et al. “Tissue tracking and registration for image-guided surgery.” IEEE transactions on medical imaging 31.11 (2012): 2169-2182.
Zhou, Jin, et al. “Synthesis of stereoscopic views from monocular endoscopic videos.” 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition—Workshops. IEEE, 2010.
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Parent 17462073 Aug 2021 US
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Parent 16675832 Nov 2019 US
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