Patent Grant
12121307
The present application is directed to vision-based position and/or orientation determination for endovascular and other intraluminal tools or medical instruments, such as catheters. In some embodiments, the devices, systems, and methods described herein can be included in or used in conjunction with robotic medical systems configured to facilitate control and operation of the medical instrument.
Endovascular medical procedures are common. During an endovascular procedure, a tool or medical instrument that is generally configured as a long, thin, flexible body is inserted into and navigated through a lumen or other cavity of the body.
In some instances, the tools or medical instruments are articulable or controllable, for example, using one or more pull wires, to allow an operator to navigate the tool or medical instrument within the body. Such navigation is often accomplished through deflection (for example, bending) of the distal tip of the tool or medical instrument.
Some tools or medical instruments are configured for manual control, for example, using knobs or levers mounted on a proximally-located handle of the tool or medical instrument. In other instances, the tools or medical instruments can be configured for robotic control, for example, control by a robotic medical system. In some embodiments, an operator can use the robotic medical system (for example, a controller, user interface, and/or the like) to robotically control the tool or medical instrument.
This application describes devices, systems, and methods for detecting or determining position and/or orientation of endovascular or other intraluminal tools or medical instruments, such as catheters. The determination of position and orientation can be made based on a two-dimensional medical image, such as a single plane X-ray image. For example, computer vision can be used to analyze a two-dimensional medical image to determine the position and/or orientation of the catheter based on radio-opaque markers that are included on the catheter. In some instances, five degrees of freedom for the catheter can be determined: two positional degrees of freedom (e.g., x and y position) and three degrees of freedom relating to orientation (e.g., heading, incline, and roll). Various example configurations for the radio-opaque markers are disclosed. The use of other configurations for the radio-opaque markers is also possible, and this disclosure should not be limited to only the disclosed configurations.
The devices, systems, and methods described herein can provide several notable advantages over existing technologies. For example, position and orientation can be determined using minimally sophisticated medical imaging (e.g., single plane X-ray). Suitable medical imaging devices are widely available, allowing the devices, systems, and methods described herein to be widely available. Additionally, position and orientation can be determined without the additional hardware that is often required by other systems. For example, existing systems often determine position and orientation using electromagnetic sensors and electromagnetic field generators. Such systems are cumbersome and relatively inaccurate, requiring precise registrations with other forms of data (e.g., medical imaging data, computer models, robotic movement data, etc.) in order to be useful. With the principals described herein, the need for such systems can be avoided. Finally, the devices, systems, and methods of the present application can allow for safer and more precise control of a catheter. This can, in turn, facilitate remote or autonomous control of the catheter. These and other benefits and advantages of the application will become more apparent after considering the disclosure and drawings in the Detailed Description section below.
In a first aspect, a computer-implemented system comprises at least one processor and at least one electronic storage medium, the electronic storage medium storing instructions configured to cause the at least one processor to: receive, from a medical imaging device, a two-dimensional medical image including a view of at least a distal portion of a medical instrument, the distal portion of the medical instrument including one or more fiducials positioned thereon, the one or more fiducials being radio-opaque and visible in the medical image; detect, within the medical image, a two-dimensional appearance of the one or more fiducials; and based on the two-dimensional appearance of the one or more fiducials, determine at least one of: a roll angle of the distal portion of the medical instrument, and an incline of the distal portion of the medical instrument.
The system may include one or more of the following features in any combination: (a) wherein the at least one processor is configured to detect the two-dimensional appearance of the one or more fiducials based on a computer vision algorithm; (b) the at least one processor is configured to detect the two-dimensional appearance of the one or more fiducials using a neural network; (c) wherein the at least one processor is further configured to determine both of the roll angle of the distal portion of the medical instrument and the incline of the distal portion of the medical instrument; (d) wherein the at least one processor is further configured to determine the incline with respect to an image plane of the two-dimensional medical image; (e) wherein the one or more fiducials are configured such that the two-dimensional appearance of the fiducials within the medical image is visually distinguishable for different roll angles and different inclines of the medical instrument; (f) wherein the one or more fiducials are configured such that the two-dimensional appearance of the fiducials within the medical image is visually distinguishable for different roll angles and different inclines of the medical instrument for incremental changes of less than 5 degrees, less than 10 degrees, less than 15 degrees, less than 20 degrees, less than 25 degrees, less than 30 degrees, less than 35 degrees, or less than 40 degrees; (g) wherein the one or more fiducials are configured such that the two-dimensional appearance of the fiducials within the medical image is visually distinguishable for different roll angles and different inclines of the medical instrument for incremental changes of about 5 degrees, about 10 degrees, about 15 degrees, about 20 degrees, about 25 degrees, about 30 degrees, about 35 degrees, or about 40 degrees; (h) wherein the at least one processor is further configured to detect, within the medical image, a distal tip of the medical instrument, and based on the detected distal tip of the medical instrument, determine a two-dimensional position of the distal tip of the medical instrument within a plane of the two-dimensional medical image; (i) wherein detecting the distal tip of the medical instrument comprises determining, based on the medical image, a centerline of the distal portion of the medical instrument, and determining an endpoint for the centerline; (j) wherein the at least one processor is further configured to detect, within the medical image, a portion of the medical instrument, and based on the detected distal portion of the medical instrument, determine a heading of the medical instrument within a plane of the two-dimensional medical image; (k) wherein determining the heading of the medical instrument comprises determining, based on the medical image, a centerline of the distal portion of the medical instrument, and determining an endpoint for the centerline, and determining a vector extending from the endpoint, the vector being colinear with a distal portion of the centerline; (1) wherein the medical instrument comprises an endoluminal medical instrument; (m) wherein the medical instrument comprises a catheter; (n) wherein the medical imaging device comprises an X-ray device; (o) wherein the processor is further configured to determine one or more motor controls configured to cause articulation of the distal portion of the medical instrument, wherein the one or more motor controls are determined at least in part based on the determined roll angle or the determined incline, and transmit the one or more motor controls to a robotic system coupled with the medical instrument, whereby the robotic system causes articulation of the medical instrument based on the one or more motor controls; (p) wherein the processor is further configured to determine the one or more motor controls based on a user input; (q) wherein the processor is further configured to cause the determined roll angle or the determined incline to be displayed on a user display; (r) the processor is further configured to cause the two-dimensional medical image to be displayed on the user display; and/or other features as described throughout this application.
In another aspect, a method includes: receiving, from a medical imaging device, a two-dimensional medical image including a view of at least a distal portion of a medical instrument, the distal portion of the medical instrument including one or more fiducials positioned thereon, the one or more fiducials being radio-opaque and visible in the medical image; detecting, within the medical image, a two-dimensional appearance of the one or more fiducials; and based on the two-dimensional appearance of the one or more fiducials, determining at least one of: a roll angle of the distal portion of the medical instrument, and an incline of the distal portion of the medical instrument.
The method may include one or more of the following features in any combination: (a) wherein detecting the two-dimensional appearance of the one or more fiducials is based on a computer vision algorithm; (b) detecting the two-dimensional appearance of the one or more fiducials using a neural network; (c) determining both of the roll angle of the distal portion of the medical instrument and the incline of the distal portion of the medical instrument; (d) determining the incline with respect to an image plane of the two-dimensional medical image; (r) wherein the one or more fiducials are configured such that the two-dimensional appearance of the fiducials within the medical image is visually distinguishable for different roll angles and different inclines of the medical instrument; (f) wherein the one or more fiducials are configured such that the two-dimensional appearance of the fiducials within the medical image is visually distinguishable for different roll angles and different inclines of the medical instrument for incremental changes of less than 5 degrees, less than 10 degrees, less than 15 degrees, less than 20 degrees, less than 25 degrees, less than 30 degrees, less than 35 degrees, or less than 40 degrees; (g) wherein the one or more fiducials are configured such that the two-dimensional appearance of the fiducials within the medical image is visually distinguishable for different roll angles and different inclines of the medical instrument for incremental changes of about 5 degrees, about 10 degrees, about 15 degrees, about 20 degrees, about 25 degrees, about 30 degrees, about 35 degrees, or about 40 degrees; (h) detecting, within the medical image, a distal tip of the medical instrument, and based on the detected distal tip of the medical instrument, determining a two-dimensional position of the distal tip of the medical instrument within a plane of the two-dimensional medical image; (i) wherein detecting the distal tip of the medical instrument comprises determining, based on the medical image, a centerline of the distal portion of the medical instrument, and determining an endpoint for the centerline; (j) detecting, within the medical image, a portion of the medical instrument, and based on the detected distal portion of the medical instrument, determine a heading of the medical instrument within a plane of the two-dimensional medical image; (k) wherein determining the heading of the medical instrument comprises determining, based on the medical image, a centerline of the distal portion of the medical instrument, and determining an endpoint for the centerline, and determining a vector extending from the endpoint, the vector being colinear with a distal portion of the centerline; (1) wherein the medical instrument comprises an endoluminal medical instrument; (m) wherein the medical instrument comprises a catheter; (n) wherein the medical imaging device comprises an X-ray device; (o) determining one or more motor controls configured to cause articulation of the distal portion of the medical instrument, wherein the one or more motor controls are determined at least in part based on the determined roll angle or the determined incline, and transmitting the one or more motor controls to a robotic system coupled with the medical instrument, whereby the robotic system causes articulation of the medical instrument based on the one or more motor controls; (p) determining the one or more motor controls based on a user input; (q) causing the determined roll angle or the determined incline to be displayed on a user display; ® causing the two-dimensional medical image to be displayed on the user display; and/or other features as described throughout this application.
For purposes of this summary, certain aspects, advantages, and novel features are described herein. It is to be understood that not necessarily all such advantages may be achieved in accordance with any particular embodiment. Thus, for example, those skilled in the art will recognize the disclosures herein may be embodied or carried out in a manner that achieves one or more advantages taught herein without necessarily achieving other advantages as may be taught or suggested herein.
All of the embodiments described herein are intended to be within the scope of the present disclosure. These and other embodiments will be readily apparent to those skilled in the art from the following detailed description, having reference to the attached figures. The invention is not intended to be limited to any particular disclosed embodiment or embodiments.
These and other features, aspects and advantages of the present application are described with reference to drawings of certain embodiments, which are intended to illustrate, but not to limit, the present disclosure. It is to be understood that the attached drawings are for the purpose of illustrating concepts disclosed in the present application and may not be to scale.
This application describes devices, systems, and methods for detecting or determining the position and/or orientation of endovascular or other intraluminal tools or medical instruments, such as catheters. In some instances, the term “pose” is used herein to refer to the position and orientation of a catheter. In some embodiments, determination of pose can be made based on a two-dimensional medical image, such as a single plane X-ray image, and one or more radio-opaque markers included on a catheter. Computer vision models can be employed to detect the radio-opaque markers in the two-dimensional medical image and to determine the pose of the catheter therefrom. In some instances, the pose can be defined by five degrees of freedom for the catheter. The five degrees of freedom can include two positional degrees of freedom (e.g., x and y position) and three degrees of freedom relating to orientation (e.g., heading, incline, and roll). In other embodiments, the pose can comprise greater (e.g., six) or fewer (e.g., four or fewer) degrees of freedom. The pose of an instrument can be defined in many different ways. While this application primarily describes examples of pose in terms of x, y, and z for position, and heading, incline, and roll for orientation, other methods for describing or defining the pose (e.g., alternative coordinate systems, alternative naming conventions, etc.) are possible, and the principles of this application extend to all methods for defining pose. Further, in some embodiments, the methods and systems of this application may be used to determine one, more than one, or all elements of pose.
The principals described herein can be applicable to robotic medical procedures, for example, where the catheter is robotically controlled by a robotic medical system that is configured to insert, retract, roll, and/or articulate the catheter based on inputs received from a physician or in an autonomous or semi-autonomous manner. In some instances the principals of this disclosure may also be applicable to manually controlled catheters.
The principles of this disclosure are described below with primary reference to examples wherein the medical instrument or tool is an endovascular catheter configured to navigate within the vasculature of the patient. These examples, however, should not be construed as limiting of the principles of the disclosure. Those of skill in the art, upon consideration of the principles disclosed herein, will appreciate that the devices, systems, and methods for detecting or determining position and/or orientation described herein have application in other contexts. For examples, the principles described herein can be useful with other endoluminal, endoscopic, or laparoscopic tools, instruments, procedures and/or the like. For ease of illustration, however, a primary example related to an endovascular catheter is provided. Accordingly, it should be realized that any of the following description of an endovascular catheter or catheter may also be applied to other endoluminal, endoscopic, and/or laparoscopic tools or the like. Additionally, it should be realized that while this application provides several example configurations for tools or medical instruments that include specific configurations of radio-opaque markers, other configurations of radio-opaque markers can also be used.
Safe navigation of a catheter within a patient's body generally requires an accurate understanding of the current pose of the catheter. It can be difficult to gain an accurate understanding of pose from a single two-dimensional medical image. For example,
Perhaps even more critically, from
As will be described in more detail below, the systems, methods, and devices provide for accurate determination of the pose of a catheter (including its roll) based on detection of radio-opaque fiducials included on the catheter. In some embodiments, detection of the radio-opaque fiducials is achieved using computer vision analysis of a two-dimensional medical image of the catheter. The methods and systems described herein can also be used with biplane imaging systems to determine six degree of freedom pose estimates of the catheter. In such cases, determination of incline may (in some instances) be determined from the biplane images, while roll angle can be determined based on computer vision analysis of the radio-opaque fiducials included on the catheter.
As will be further described herein, the use of computer vision algorithms and unique radio-opaque markings or fiducials can be included on the catheter that may be used to quantitatively estimate the endovascular tool's five-dimensional pose (for example, two-dimensional position (e.g., see
To facilitate navigation, in some embodiments (such as the illustrated embodiment of
The catheter 100 can be configured such that control thereof (e.g., control of the deflection of the distal portion of the catheter 100) can be accomplished manually or robotically. For example, in some embodiments that are configured for manual control, the body 101 and pull wires 105 extend proximally to a handle located on a proximal end of the catheter 100 (not shown). The handle can be configured to by operated by hand (e.g., manually) to actuate the pull wires 105. For example, the handle can include one or more manual inputs such as levers, buttons, dials and/or the like that allow a user to manually actuate the pull wires 105 to cause deflection of the distal portion of the catheter 100. In some robotically controllable embodiments, the body 101 and pull wires 105 extend proximally to a base located on a proximal end of the catheter 100 (not shown). The base can be configured to connect to and be operated by a robotic medical system to actuate the pull wires 105. For example, the base can include one or more robotic inputs configured to engage with robotic outputs or actuators on the robotic medical system. In some embodiments, other methods and configurations for manual and robotic control may be used.
In general, during an endovascular procedure, a physician or other operator, attempts to guide the distal end 103 of the catheter 100 to a specific location, such as, for example a treatment site. For example, one such procedure is a mechanical thrombectomy. A large vessel occlusion (LVO) stroke occurs when a blood clot lodges in at least one of the internal carotid, proximal middle cerebral artery, proximal anterior cerebral artery, basilar artery, or vertebral artery. Such a clot can partially or completely occlude downstream blood supply to brain tissue resulting in neuronal infarction and subsequent neurological impairment or death. During a mechanical thrombectomy, a physician gains access to the patient's vasculature and inserts a catheter, such as catheter 100. The catheter 100 is guided to the obstruction using, for example, one or more medical images similar to the one shown in
While medical images, such as that shown in
Considering
As described above, the catheter 100 may include an articulable portion that is actuated via pull wires. To accurately control the articulation of the catheter 100, one must understand the roll orientation of the catheter 100. As a simplified example, if the physician actuates the right most pull wire 105 expecting that this will cause the catheter 100 to deflect to the right, the catheter 100 may move unexpectedly to the left if the catheter 100 is rolled 180 degrees such that the right most pull wire 105 is positioned on the left side of the catheter 100. The physician may estimate the roll position of the catheter 100 based on the roll position of the proximal end of the catheter 100. However, due to the complex shape of the vasculature, the roll position at the proximal end of the catheter 100 may not (and often does not) directly correspond to the roll position at the distal end 103 of the catheter 100. Thus, to understand how actuation of the pull wires 105 will cause the distal portion of the catheter 100 to deflect, one must generally understand the roll position at the distal portion of the catheter 100.
Considering
Out-of-Plane or Incline Angle Detection for Endovascular and Other Intraluminal Tools
As discussed in the preceding section, in some embodiments, such as the embodiment illustrated in
Considering the example of
To further illustrate the principles and concepts,
If one considers that the marker 107 continues to turn out of the plane of the page, the minor axis of the ellipse will continue to increase in length until the minor and major axes are equal and the distal end of the marker 107 will present as a circle in the plane of the page.
From the examples provided, it can be seen that one can analyze the two-dimensional shape created by the ring or circled-shaped marker 107 within a two-dimensional imaging plane to determine the orientation of the marker 107 into and out of the page. Other shapes for marker 107 are possible. For example, the marker 107 need not always comprise a ring shape.
In some embodiments, a computer system, such as or which can include a computer vision system, can be used to (1) detect the shape (e.g., the visible, ellipse, circle, curve, or line) created by the distal end of the ring-shaped marker 107 within the two-dimensional image, and (2) extract or determine the out of the plane angle of the marker 107 or the tool incline from the detected shape. In some embodiments, the computer system may utilize artificial intelligence or machine learning to perform such functionality. In some embodiments, for example, a neural network can be trained to detect the shape created by the distal end of the ring-shaped marker 107 within the two-dimensional image, and extract or determine the out of the plane angle of the marker 107 from the detected shape. In some embodiments, computer vision can be used to define the orientation of the tool along the z-axis.
As mentioned above, determination of the out of the plane angle of the catheter 100 can be important in improving and/or facilitating navigation through a luminal network, such as the vasculature. In general, the vasculature of a patient will not lie within a single plane. This is apparent considering the model example vasculature provided in
Alternatively or in addition to detecting the shape of the distal tip of the catheter 100 within the plane of the image, other methods or mechanisms may also be employed for determining the out of the plane angle of the catheter 100. For example, in some embodiments, a degree of angulation may be presumed for each vessel through which the catheter 100 passes, for example, based on the general or average angulation of that vessel across the population. In some embodiments, the angulation of a vessel can be determined based on a CT scan. In some embodiments, an additional medical image at a different orientation than the first medical image (e.g., a lateral angiographic view) can be provided. In some embodiments, the medical imager can be moved so as to gain an understanding of the out of the plane angulation.
Although many of the preceding examples have described the use of a ring-shaped marker 107, other types of markers or fiducials can be used as described further below.
Incline Sign Detection for Endovascular and Other Intraluminal Tools
As discussed in the preceding section, the magnitude of the incline of endovascular and other intraluminal tools can, in some embodiments, be determined by analysis of the elliptical shape of the tool mouth (or a marker, such a ring-shaped marker, included thereon). However, the sign of the degree of angulation is not always readily identifiable solely from analysis of the marker 107 because the elliptical shape may look the same when imaged in two dimensions for both positive angulation and negative angulations of the same degree of incline. To determine whether the incline is positive or negative, an additional method may be desired.
In some embodiments, a computer system, such as a computer vision system, can be used to (1) detect the position of the semicircular marker 110 in combination with helical fiducial 111 within the two-dimensional image, and (2) extract or determine the sign of the tool incline from the detected position. In some embodiments, the computer system may utilize artificial intelligence or machine learning to perform such functionality. In some embodiments, for example, a neural network can be trained to detect the position of the semicircular marker 110 in combination with helical fiducial 111 within the two-dimensional image, and extract or determine the sign of the tool incline from the detected position. It should be noted that in some embodiments, the machine learning algorithm does not hardcode the aforementioned approach. Instead, the machine learning algorithm trains a deep neural network to directly predict the incline angle from the input of the X-ray image.
In some embodiments, the radio-opaque markers provide unique or visually distinguishable two-dimensional appearances at all different roll or incline positions. In some embodiments, the radio-opaque markers provide unique or visually distinguishable two-dimensional appearances at different roll or incline positions within increments of about, at least, or at most 1 degree, 2 degrees, 3 degrees, 4 degrees, 5 degrees, 7.5 degrees, 10 degrees, 12.5 degrees, 15 degrees, 17.5 degrees, 20 degrees, 25 degrees, 30 degrees, or 40 degrees. That is, in some embodiments, the radio-opaque markers are configured with a three-dimensional shape that, when viewed within the two-dimensional plane of a two-dimensional medical imaging device, provides a unique or visually distinguishable appearance that can be distinguished at the different incremental roll or incline angles listed above. The above listed increments can be considered minimum resolutions for the system or the minimum change in roll or incline that is detectable by the system.
Tool Position and Heading for Endovascular and Other Intraluminal Tools
In some embodiments, the system may be configured to predict where the full tool body is, and then from this tool body we the two-dimensional tip location can be extracted. This approach may be beneficial because the tool body provides a very strong training signal for learning deep neural network segmentation models. That is, in some instances, it may be easier for a neural network of computer vision algorithm to detect the body of a catheter and then extract the location of the tip from there. In some embodiments, catheter kinematics are further used refine this estimate.
For example, a deep neural network can be used to estimate the two-dimensional centerline position of the catheter based on one or more images of the catheter navigating within the body.
In some embodiments, a machine learning algorithm for estimating the position of a catheter and/or other tool may use the following approach. First, the image generation procedure is modified by drawing the catheter on top of tangible X-ray images (e.g., as shown in
Second, the two-dimensional x and y position is estimated. In some embodiments, radio-opaque markings may be added to the tool body, such as, for example, a full-length helix, to assist with the identification. In some embodiments, the three-dimensional x, y, and z position may be estimated instead. However, estimation of the Z position may require calibration of the X-ray camera to obtain its camera intrinsic, which requires an additional step of a pre-operation image calibration. In some instances, the two-dimensional position estimation will be preferable so the pre-operation image calibration step does not need to be completed and because the z position may not be necessary because motion planning is primarily conducted in two-dimensions. Further, a z position estimate can be obtained by rotating the C-arm. Using this method, the two-dimensional x and y position of the full tool body may be predicted. Thereafter, the two-dimensional x and y position location of the tool tip (such as, for example, the distal end 103 of catheter 100) can be determined. This approach may be used because the tool body provides a very strong training signal for learning deep neural network segmentation models. In some embodiments, catheter kinematics may be used to further refine the position estimate.
As noted above, heading can refer to a measure of angulation or articulation about the z-axis and/or where the device is pointing in the image plane. To determine the heading of endovascular and/or other intraluminal tools, such as a catheter, the deep neural network prediction of the catheter body position may be used. Based on the prediction of the two-dimensional x and y position of the catheter tip, a second position located on the catheter body may be determined. The second position may be an infinitesimal distance from the tool tip in a direction along the catheter body. The heading angle of the catheter may then be calculated using trigonometry based on the x and y position of the tool tip and the second position along the catheter body.
Roll Angle Detection for Endovascular and Other Intraluminal Tools
As noted above, radio-opaque markers can be placed at the distal tip 103 of a catheter 100 to improve the visibility of the catheter 100 in a medical image (see
In the illustrated embodiment, a first marker 108A is positioned a first distance 2D (twice the diameter of the tool) from the distal tip 103 and at a 45-degree rotational offset with respect to a first pull wire 105. A second marker 108B is positioned an additional distance 2D from the distal tip 103 (4D) and at a 135-degree rotational offset with respect to the first pull wire 105. A third marker 108C is positioned an additional distance 2D from the distal tip 103 (6D) and at a 225-degree rotational offset with respect to the first pull wire 105. A fourth marker 108D is positioned an additional distance 2D from the distal tip 103 (8D) and at a 315-degree rotational offset with respect to the first pull wire 105. In some embodiments, the rotational offset of first marker 108A from the first pull wire may be, for example, 0-degrees, 5-degrees, 10-degrees, 15-degrees, 20-degrees, 25-degrees, 30-degrees, 35-degrees, 40-degrees, 45-degrees, 50-degrees, 55-degrees, 60-degrees, 65-degrees, 70-degrees, 75-degrees, 80-degrees, 85-degrees, 90-degrees, and/or the like, with the second marker 108B, third marker 108C, and fourth marker 108D being an additional 90-degrees, 180 degrees, and 270-degrees respectively rotationally offset from first marker 108A. In some embodiments, the distance 2D is approximately 5 mm.
In the illustrated example, each marker 108 is positioned at a longitudinal distance that is twice the diameter D of the catheter 100 below the marker 108 above it (or below the distal tip 103 for the first marker). This need not be the case in all embodiments, and other spacings are possible. In the illustrated example, each marker 108 is positioned at 90-degree offsets and between the adjacent pull wires 105. Again, this need not be the case in all embodiments and other spacings are possible. Further, in
With such a configuration or other suitable configurations, the appearance of the markers 108A-108D within a medical image provides a unique appearance from which the roll of the catheter 100 can be determined.
In some embodiments, determining roll from the markers 108A-108D can be accomplished as follows: (1) the locations of the markers 108A-108D within an image can be determined, in some embodiments, this is accomplished through computer vision or a neural network that has been trained to identify the markers 108A-108D; (2) the centerline of the catheter 100 can be determined using, for example, computer vision or a neural network; (3) the distance (with appropriate sign, positive or negative) between each of the markers 108A-108D and the centerline can be determined; (4) the signed distance between the markers and the centerline can be used to determine the roll angle using geometry principles.
Use of four markers 108A-108D, for example, as shown in
In some embodiments, increasing the axial distance between the markers 108A-108D, can increase the signal to noise ratio, for example, allowing the markers 108A-108D to be more easily identified within the medical image. In some embodiments, more than four markers may be used to determine the roll of the catheter 100. In some embodiments, less than four markers may be used to determine the roll of the catheter 100.
As shown in
In some embodiments, radio-opaque sleeves or other features can be coupled to the pull wires. In such cases, a computer vision algorithm can be configured to either detect the features at opposite sides of the catheter or to detect when the features overlap. In either case, these features would allow the computer vision system to assess if the tool is oriented with each pull wire in plane.
In some embodiments, the radio-opaque markers provide unique or visually distinguishable two-dimensional appearances at all different roll positions. In some embodiments, the radio-opaque markers provide unique or visually distinguishable two-dimensional appearances at different roll positions within increments of about, at least, or at most 1 degree, 2 degrees, 3 degrees, 4 degrees, 5 degrees, 7.5 degrees, 10 degrees, 12.5 degrees, 15 degrees, 17.5 degrees, 20 degrees, 25 degrees, 30 degrees, or 40 degrees. The above listed increments can be considered minimum resolutions for the system or the minimum change in roll that is detectable by the system.
In some embodiments, the roll angle determined based on the markers of any of these embodiments can be used by a motion planning algorithm to determine how it will move the catheter. In one embodiment, the algorithm can be configured to rotate the catheter until the radio-opaque identifiers aligns with the imaging plane. In another embodiment, the algorithm can measure the rotation of the tool using the radio-opaque identifiers and update which pull wires it uses to execute a maneuver.
As shown in
Safeguards Against Unexpected Catheter Motion
Unexpected motion of the distal end of a catheter can jeopardize the safety of endovascular or other procedures. The term “unexpected motion,” as referred to herein, describes any movement or behavior of the distal end of a catheter that is not predicted based on the movement and/or control of the proximal end of the catheter. An example of unexpected motion may be a poor torque response where an unexpected roll motion occurs at the distal end of a catheter. The unexpected roll motion may occur when the rotation at the distal end of the catheter does not correlate to the rotation at the proximal end. For example, sometimes when the proximal end of the catheter is rotating, the distal end of the catheter may be rotating at a slower rate or may not be rotating at all. As the proximal end of the catheter continues to rotate and the distal end rotates at a slower rate or does not rotate at all, torque builds up in the catheter. If the proximal end of the catheter continues to rotate, eventually the torque in the catheter may cause the distal end of the catheter to rotate very quickly to catch up with the rotation imposed on the proximal end of the catheter and the quick rotation may be characterized as unexpected motion which could cause harm to the patient. For example, the unexpected motion at the distal end could cause damage to or a tear in a vessel wall.
As previously described, the degree of rotation of the distal end of a tool about its centerline may be informed by the addition of radio-opaque rotation fiducials. Using the methods described herein, unexpected motion at the distal end of a catheter may be prevented by tracking and comparing the rotation rate and roll of the distal and proximal ends of a catheter. In some embodiments, the comparison may be performed at discrete steps. In some embodiments, the comparison may be performed continuously. In some embodiments, rotation at the proximal end of a catheter may be prevented when there is a difference of more than a specific number of degrees of rotation between the proximal and distal ends of the catheter. By preventing further rotation after the difference in rotation is calculated at a specific amount, the system may prevent unexpected motion (for example, snapping and/or whipping of the distal end) to increase the safety of the procedure.
In some embodiments, a computer vision system may be used to identify the fiducials to model how far the distal catheter tip has rotated in relation to how far the motors controlling rotation at the proximal end of the catheter have moved. In some embodiments, this method may be paired with other safety information such as, for example, force detection and/or the like. In some embodiments, a similar method may be applied to detect discrepancies in expected advancement and retraction of the catheter as compared to actual advancement and retraction of the catheter. Use of this method may be used to identify potential obstructions to catheter motion. For example, using the methods described herein to determine the actual position a catheter tip can be compared to the expected position of a catheter tip based on how the catheter was controlled.
Automated Tool Tracking
The automated identification of a tool, tool tip and/or tool orientation may be used to control movement of a decoupled imaging source to maintain optimal viewing. For example, in an endovascular procedure, movement of an angiography system could be precisely controlled and/or centered on the tool in question without manual manipulation. In another example, in a laparoscopic surgery, movement of a camera could be precisely controlled and/or centered on the tool in question without manual manipulation. Being able to maintain optimal viewing in this manner may have the advantage of significantly better imaging for the surgeon. In some cases, automated tool tracking may improve the speed and efficiency of procedures, for example, the procedure would be faster and more efficient because the surgeon does not have to put down the tools and move the camera intermittently. In some cases, automated tool tracking may reduce the number of personal required in the operation room because no one needs to control the camera.
Automated tool tracking may be most useful for interventional and/or surgical applications where the source of input imaging is decoupled from the navigating and/or interventional tool. In these procedures, generally an assistant must manually track the surgeon's tools which may result in both lag and imprecision. For example, in an angiographic procedure, the automated tool tracking system may be used to maintain the tool tip halfway across and one third up the image screen for the entire procedure instead of the current method of having the physician put down the tools and manually readjust the screen to focus on the tool. In another example, in a laparoscopic or thoracoscopic procedure, the automated tool tracking system may be used to enable an external camera source to be automatically adjusted to maintain the tools in the center of the image, thus improving the focus and positioning of the camera.
Example Endovascular and Other Applicable Procedures
The various technologies disclosed herein related to determination of position and/or orientation determination can be used to facilitate the treatment of various diseases and other conditions where a robotic or manual device is advanced through an intraluminal (e.g., intravascular) network of a subject to reach the site of intravascular pathology (e.g., thrombosis, embolus, occlusion, aneurysm, rupture, bleeding, dissection, etc.). In some embodiments, the systems, devices, and methods described herein can be used to facilitate one or more endovascular purposes, surgeries, and/or treatments. For example, in some embodiments, the systems, processes, and methods described herein can be used for one or more of removal of intravascular blockage/reestablishment of perfusion; treatment of vessel wall injury (aneurysm and/or dissection); treatment of bleeding: aneurysm rupture/trauma; and/or the like. Moreover, in some embodiments, the systems, devices, and methods described herein can be used to treat vascular trauma.
In some embodiments, the systems, devices, and methods described herein can be used to facilitate neurovascular applications and/or treatments, such as for example to treat subarachnoid hemorrhage, aneurysm, arteriovenous malformation, and/or the like. In some embodiments, the systems, devices, and methods described herein can be used for cardiovascular applications and/or treatments, such as for example to treat myocardial infarction, coronary artery disease, pacemaker insertion, and/or the like. In some embodiments, the systems, devices, and methods described herein can be used for aortic applications and/or treatments, such as for example to treat aortic dissection, aortic aneurysm, and/or the like. In some embodiments, the systems, devices, and methods described herein can be used for peripheral emboli applications and/or treatments. In some embodiments, the systems, devices, and methods described herein can be used for vascular trauma applications and/or treatments. In some embodiments, the systems, devices, and methods described herein can be used for venous applications and/or treatments.
While the features of this application have largely been described in the context of endoluminal or endovascular procedures, the inventions described herein may also be practiced fluoroscopically guided procedures, such as endoscopic retrograde cholangiopancreatography (ERCP), discography and vertebroplasty, orthopedic and podiatric surgery, urological procedures including pyelography, intracardiac placement of intracardiac devices, ablations, and lumbar punctures.
Computer System
In some embodiments, the systems, processes, and methods described herein are implemented using a computing system, such as the one illustrated in
The computer system 1002 can comprise an pose determination module 1014 that carries out the functions, methods, acts, and/or processes described herein. The module 1014 is executed on the computer system 1002 by a central processing unit 1006 discussed further below.
In general the word “module,” as used herein, refers to logic embodied in hardware or firmware or to a collection of software instructions, having entry and exit points. Modules are written in a program language, such as JAVA, C, C++, and/or the like. Software modules can be compiled or linked into an executable program, installed in a dynamic link library, or can be written in an interpreted language such as BASIC, PERL, LAU, PHP, or Python and/or any such languages. Software modules can be called from other modules or from themselves, and/or can be invoked in response to detected events or interruptions. Modules implemented in hardware include connected logic units such as gates and flip-flops, and/or can include programmable units, such as programmable gate arrays and/or processors.
Generally, the modules described herein refer to logical modules that can be combined with other modules or divided into sub-modules despite their physical organization or storage. The modules are executed by one or more computing systems and can be stored on or within any suitable computer readable medium, or implemented in-whole or in-part within special designed hardware or firmware. Not all calculations, analysis, and/or optimization require the use of computer systems, though any of the above-described methods, calculations, processes, or analyses can be facilitated through the use of computers. Further, in some embodiments, process blocks described herein can be altered, rearranged, combined, and/or omitted.
The computer system 1002 includes one or more processing units (CPU) 1006, which can comprise a microprocessor. The computer system 1002 further includes a physical memory 1010, such as random access memory (RAM) for temporary storage of information, a read only memory (ROM) for permanent storage of information, and a mass storage device 1004, such as a backing store, hard drive, rotating magnetic disks, solid state disks (SSD), flash memory, phase-change memory (PCM), 3D XPoint memory, diskette, or optical media storage device. Alternatively, the mass storage device can be implemented in an array of servers. Typically, the components of the computer system 1002 are connected to the computer using a standards-based bus system. The bus system can be implemented using various protocols, such as Peripheral Component Interconnect (PCI), Micro Channel, SCSI, Industrial Standard Architecture (ISA) and Extended ISA (EISA) architectures.
The computer system 1002 includes one or more input/output (I/O) devices and interfaces 1012, such as a keyboard, mouse, touch pad, and printer. The I/O devices and interfaces 1012 can include one or more display devices, such as a monitor, that allows the visual presentation of data to a user. More particularly, a display device provides for the presentation of GUIs as application software data, and multi-media presentations, for example. The I/O devices and interfaces 1012 can also provide a communications interface to various external devices. The computer system 1002 can comprise one or more multi-media devices 1008, such as speakers, video cards, graphics accelerators, and microphones, for example.
Computing System Device/Operating System
The computer system 1002 can run on a variety of computing devices, such as a server, a Windows server, a Structure Query Language server, a Unix Server, a personal computer, a laptop computer, and so forth. In other embodiments, the computer system 1002 can run on a cluster computer system, a mainframe computer system and/or other computing system suitable for controlling and/or communicating with large databases, performing high volume transaction processing, and generating reports from large databases. The computing system 1002 is generally controlled and coordinated by an operating system software, such as z/OS, Windows, Linux, UNIX, BSD, PHP, SunOS, Solaris, MacOS, ICloud services or other compatible operating systems, including proprietary operating systems. Operating systems control and schedule computer processes for execution, perform memory management, provide file system, networking, and I/O services, and provide a user interface, such as a graphical user interface (GUI), among other things.
Network
The computer system 1002 illustrated in
The output module can be implemented as a combination of an all-points addressable display such as a cathode ray tube (CRT), a liquid crystal display (LCD), a plasma display, or other types and/or combinations of displays. The output module can be implemented to communicate with input devices 1012 and they also include software with the appropriate interfaces which allow a user to access data through the use of stylized screen elements, such as menus, windows, dialogue boxes, tool bars, and controls (for example, radio buttons, check boxes, sliding scales, and so forth). Furthermore, the output module can communicate with a set of input and output devices to receive signals from the user.
Other Systems
The computing system 1002 can include one or more internal and/or external data sources (for example, data sources 1022). In some embodiments, one or more of the data repositories and the data sources described above can be implemented using a relational database, such as DB2, Sybase, Oracle, CodeBase, and Microsoft® SQL Server as well as other types of databases such as a flat-file database, an entity relationship database, and object-oriented database, and/or a record-based database.
The computer system 1002 can also access one or more databases 1022. The databases 1022 can be stored in a database or data repository. The computer system 1002 can access the one or more databases 1022 through a network 1018 or can directly access the database or data repository through I/O devices and interfaces 1012. The data repository storing the one or more databases 1022 can reside within the computer system 1002.
URLs and Cookies
In some embodiments, one or more features of the systems, methods, and devices described herein can utilize a URL and/or cookies, for example for storing and/or transmitting data or user information. A Uniform Resource Locator (URL) can include a web address and/or a reference to a web resource that is stored on a database and/or a server. The URL can specify the location of the resource on a computer and/or a computer network. The URL can include a mechanism to retrieve the network resource. The source of the network resource can receive a URL, identify the location of the web resource, and transmit the web resource back to the requestor. A URL can be converted to an IP address, and a Doman Name System (DNS) can look up the URL and its corresponding IP address. URLs can be references to web pages, file transfers, emails, database accesses, and other applications. The URLs can include a sequence of characters that identify a path, domain name, a file extension, a host name, a query, a fragment, scheme, a protocol identifier, a port number, a username, a password, a flag, an object, a resource name and/or the like. The systems disclosed herein can generate, receive, transmit, apply, parse, serialize, render, and/or perform an action on a URL.
A cookie, also referred to as an HTTP cookie, a web cookie, an internet cookie, and a browser cookie, can include data sent from a website and/or stored on a user's computer. This data can be stored by a user's web browser while the user is browsing. The cookies can include useful information for websites to remember prior browsing information, such as a shopping cart on an online store, clicking of buttons, login information, and/or records of web pages or network resources visited in the past. Cookies can also include information that the user enters, such as names, addresses, passwords, credit card information, etc. Cookies can also perform computer functions. For example, authentication cookies can be used by applications (for example, a web browser) to identify whether the user is already logged in (for example, to a web site). The cookie data can be encrypted to provide security for the consumer. Tracking cookies can be used to compile historical browsing histories of individuals. Systems disclosed herein can generate and use cookies to access data of an individual. Systems can also generate and use JSON web tokens to store authenticity information, HTTP authentication as authentication protocols, IP addresses to track session or identity information, URLs, and the like.
It will now be evident to those skilled in the art that there has been described herein methods, systems, and devices for improved routing of catheters and other devices to targeted anatomical locations using robotically controlled assemblies. Although the inventions hereof have been described by way of several embodiments, it will be evident that other adaptations and modifications can be employed without departing from the spirit and scope thereof. The terms and expressions employed herein have been used as terms of description and not of limitation; and thus, there is no intent of excluding equivalents, but on the contrary it is intended to cover any and all equivalents that may be employed without departing from the spirit and scope of the inventions.
While the disclosure has been described with reference to certain embodiments, it will be understood that various changes may be made, and equivalents may be substituted for elements thereof without departing from the scope of the disclosure. In addition, many modifications will be appreciated to adapt a particular instrument, situation, or material to the teachings of the disclosure without departing from the essential scope thereof. Therefore, it is intended that the disclosure is not limited to the particular embodiment disclosed as the best mode contemplated for carrying out this disclosure, but that the disclosure will include all embodiments falling within the scope of the appended claims.
Although several embodiments and examples are disclosed herein, the present application extends beyond the specifically disclosed embodiments to other alternative embodiments and/or uses of the inventions and modifications and equivalents thereof. It is also contemplated that various combinations or subcombinations of the specific features and aspects of the embodiments may be made and still fall within the scope of the inventions. Accordingly, it should be understood that various features and aspects of the disclosed embodiments can be combined with or substituted for one another in order to form varying modes of the disclosed inventions. Thus, it is intended that the scope of the present inventions herein disclosed should not be limited by the particular disclosed embodiments described above but should be determined only by a fair reading of the claims that follow.
While the embodiments disclosed herein are susceptible to various modifications, and alternative forms, specific examples thereof have been shown in the drawings and are herein described in detail. It should be understood, however, that the inventions are not to be limited to the particular forms or methods disclosed, but, to the contrary, the inventions are to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the various embodiments described and the appended claims. Any methods disclosed herein need not be performed in the order recited. The methods disclosed herein include certain actions taken by a practitioner; however, they can also include any third-party instruction of those actions, either expressly or by implication. For example, actions such as “advancing a catheter or microcatheter” or “advancing one portion of the device (e.g., linearly) relative to another portion of the device to rotate the distal end of the device” include instructing advancing a catheter” or “instructing advancing one portion of the device,” respectively. The ranges disclosed herein also encompass any and all overlap, sub-ranges, and combinations thereof. Language such as “up to,” “at least,” “greater than,” “less than,” “between,” and the like includes the number recited. Numbers preceded by a term such as “about” or “approximately” include the recited numbers. For example, “about 10 mm” includes “10 mm.” Terms or phrases preceded by a term such as “substantially” include the recited term or phrase. For example, “substantially parallel” includes “parallel.”
This application claims priority to U.S. application Ser. No. 17/810,102, filed Jun. 30, 2022, which claims priority to U.S. Provisional Application No. 63/202,963, filed Jul. 1, 2021, and to U.S. Provisional Application No. 63/264,531, filed Nov. 24, 2021, each of which are incorporated herein by reference. Any and all applications for which a foreign or domestic priority claim is identified in the Application Data Sheet as filed with the present application are hereby incorporated by reference under 37 CFR 1.57.
| Number | Name | Date | Kind |
|---|---|---|---|
| 819885 | Holmen | May 1906 | A |
| 5456574 | Bos et al. | Oct 1995 | A |
| 5607393 | Ensminger et al. | Mar 1997 | A |
| 5792123 | Ensminger | Aug 1998 | A |
| 6132368 | Cooper | Oct 2000 | A |
| 6171329 | Shaw et al. | Jan 2001 | B1 |
| 6203568 | Lombardi et al. | Mar 2001 | B1 |
| 6361557 | Gittings et al. | Mar 2002 | B1 |
| 6522933 | Nguyen | Feb 2003 | B2 |
| 6572610 | Kovalcheck et al. | Jun 2003 | B2 |
| 6592581 | Bowe | Jul 2003 | B2 |
| 6602267 | Castaneda | Aug 2003 | B2 |
| 6623508 | Shaw et al. | Sep 2003 | B2 |
| 6645218 | Cassidy et al. | Nov 2003 | B1 |
| 6648875 | Simpson et al. | Nov 2003 | B2 |
| 6652506 | Bowe et al. | Nov 2003 | B2 |
| 6669692 | Nelson | Dec 2003 | B1 |
| 6743239 | Kuehn et al. | Jun 2004 | B1 |
| 6858005 | Ohline et al. | Feb 2005 | B2 |
| 6860878 | Brock | Mar 2005 | B2 |
| 6869414 | Simpson et al. | Mar 2005 | B2 |
| 6872433 | Seward et al. | Mar 2005 | B2 |
| 6926711 | Lentz et al. | Aug 2005 | B2 |
| 6979312 | Shimada | Dec 2005 | B2 |
| 6986769 | Nelson et al. | Jan 2006 | B2 |
| 7011655 | Thompson et al. | Mar 2006 | B2 |
| 7052489 | Griego et al. | May 2006 | B2 |
| 7122020 | Mogul | Oct 2006 | B2 |
| 7285108 | Koerner et al. | Oct 2007 | B2 |
| 7331967 | Lee et al. | Feb 2008 | B2 |
| 7357774 | Cooper | Apr 2008 | B2 |
| 7379790 | Toth | May 2008 | B2 |
| 7419477 | Simpson et al. | Sep 2008 | B2 |
| 7503914 | Coleman et al. | Mar 2009 | B2 |
| 7608056 | Kennedy, II | Oct 2009 | B2 |
| 7615044 | Scheibe et al. | Nov 2009 | B2 |
| 7717875 | Knudson et al. | May 2010 | B2 |
| 7731706 | Potter | Jun 2010 | B2 |
| 7758569 | Brock | Jul 2010 | B2 |
| 7789875 | Brock et al. | Sep 2010 | B2 |
| 7803130 | Ryan et al. | Sep 2010 | B2 |
| 7819884 | Lee et al. | Oct 2010 | B2 |
| 7840261 | Rosenman et al. | Nov 2010 | B2 |
| 7942854 | Von Oepen et al. | May 2011 | B1 |
| 7947050 | Lee et al. | May 2011 | B2 |
| 7947051 | Lee et al. | May 2011 | B2 |
| 7963913 | Devengenzo et al. | Jun 2011 | B2 |
| 7972323 | Bencini et al. | Jul 2011 | B1 |
| 8000764 | Rashidi | Aug 2011 | B2 |
| 8002739 | Lee et al. | Aug 2011 | B2 |
| 8016784 | Hayzelden et al. | Sep 2011 | B1 |
| 8052607 | Byrd | Nov 2011 | B2 |
| 8062212 | Belson | Nov 2011 | B2 |
| 8066664 | LaDuca et al. | Nov 2011 | B2 |
| 8118803 | Chow | Feb 2012 | B1 |
| 8137263 | Marescaux et al. | Mar 2012 | B2 |
| 8142367 | Gardeski et al. | Mar 2012 | B2 |
| 8162934 | Potter | Apr 2012 | B2 |
| 8170657 | Ehrenreich | May 2012 | B1 |
| 8182470 | Devengenzo et al. | May 2012 | B2 |
| 8192399 | Birchard | Jun 2012 | B2 |
| 8214019 | Govari et al. | Jul 2012 | B2 |
| 8218846 | Trumer et al. | Jul 2012 | B2 |
| 8231610 | Jo et al. | Jul 2012 | B2 |
| 8251977 | Partlett | Aug 2012 | B2 |
| 8256428 | Hindricks et al. | Sep 2012 | B2 |
| 8273016 | O'Sullivan | Sep 2012 | B2 |
| 8273054 | St. Germain et al. | Sep 2012 | B2 |
| 8295943 | Eggen et al. | Oct 2012 | B2 |
| 8298177 | Selkee | Oct 2012 | B2 |
| 8303576 | Brock | Nov 2012 | B2 |
| 8348931 | Cooper et al. | Jan 2013 | B2 |
| 8372033 | Kronstedt et al. | Feb 2013 | B2 |
| 8388572 | Olsen et al. | Mar 2013 | B2 |
| 8409245 | Lee | Apr 2013 | B2 |
| 8409273 | Thornton et al. | Apr 2013 | B2 |
| 8414598 | Brock et al. | Apr 2013 | B2 |
| 8460236 | Roelle et al. | Jun 2013 | B2 |
| 8486022 | Ludwig et al. | Jul 2013 | B2 |
| 8506555 | Morales | Aug 2013 | B2 |
| 8506562 | Anderson et al. | Aug 2013 | B2 |
| 8517923 | Belson et al. | Aug 2013 | B2 |
| 8529582 | Devengenzo et al. | Sep 2013 | B2 |
| 8568435 | Pillai et al. | Oct 2013 | B2 |
| 8603135 | Mueller | Dec 2013 | B2 |
| 8607935 | Goldberg et al. | Dec 2013 | B2 |
| 8608773 | Tierney et al. | Dec 2013 | B2 |
| 8620400 | de la Rama et al. | Dec 2013 | B2 |
| 8632468 | Glossop et al. | Jan 2014 | B2 |
| 8636764 | Miles et al. | Jan 2014 | B2 |
| 8641602 | Belson | Feb 2014 | B2 |
| 8641700 | Devengenzo et al. | Feb 2014 | B2 |
| 8647362 | Griego | Feb 2014 | B2 |
| 8671817 | Bogusky | Mar 2014 | B1 |
| 8672837 | Roelle et al. | Mar 2014 | B2 |
| 8672922 | Loh et al. | Mar 2014 | B2 |
| 8690871 | Partlett et al. | Apr 2014 | B2 |
| 8706260 | Stewart et al. | Apr 2014 | B2 |
| 8709037 | Lee et al. | Apr 2014 | B2 |
| 8715318 | Miles et al. | May 2014 | B2 |
| 8721530 | Ohline et al. | May 2014 | B2 |
| D708740 | Osypka et al. | Jul 2014 | S |
| 8808169 | Macnamara et al. | Aug 2014 | B2 |
| 8808278 | Goldberg et al. | Aug 2014 | B2 |
| 8827948 | Romo et al. | Sep 2014 | B2 |
| 8834489 | Cooper et al. | Sep 2014 | B2 |
| 8840560 | Hossack et al. | Sep 2014 | B2 |
| 8864656 | Konstorum | Oct 2014 | B2 |
| D718437 | Osypka | Nov 2014 | S |
| 8894610 | Macnamara et al. | Nov 2014 | B2 |
| 8920429 | Hinman et al. | Dec 2014 | B2 |
| 8939960 | Rosenman et al. | Jan 2015 | B2 |
| 8961533 | Stahler et al. | Feb 2015 | B2 |
| 8979740 | Butler | Mar 2015 | B2 |
| 9005217 | Govari et al. | Apr 2015 | B2 |
| 9023060 | Cooper et al. | May 2015 | B2 |
| 9061120 | Osypka et al. | Jun 2015 | B2 |
| 9084621 | Weitzner et al. | Jul 2015 | B2 |
| 9085085 | Danitz et al. | Jul 2015 | B2 |
| 9089356 | Chen et al. | Jul 2015 | B2 |
| 9095253 | Hinman et al. | Aug 2015 | B2 |
| 9101379 | Au et al. | Aug 2015 | B2 |
| 9101735 | Rothe et al. | Aug 2015 | B2 |
| 9107673 | Chong et al. | Aug 2015 | B2 |
| 9114232 | Olson et al. | Aug 2015 | B2 |
| 9138166 | Wong et al. | Sep 2015 | B2 |
| 9149937 | Goldberg et al. | Oct 2015 | B2 |
| 9155449 | Danitz et al. | Oct 2015 | B2 |
| 9173551 | Peters et al. | Nov 2015 | B2 |
| 9173713 | Hart et al. | Nov 2015 | B2 |
| 9186046 | Ramamurthy et al. | Nov 2015 | B2 |
| 9199061 | Selkee | Dec 2015 | B2 |
| 9204933 | Reis et al. | Dec 2015 | B2 |
| 9241770 | Stokes et al. | Jan 2016 | B2 |
| 9254372 | Ryan | Feb 2016 | B2 |
| 9259271 | Anvari et al. | Feb 2016 | B2 |
| 9308349 | Rezac et al. | Apr 2016 | B2 |
| 9314587 | Arnim et al. | Apr 2016 | B2 |
| 9314591 | Ogle | Apr 2016 | B2 |
| 9326822 | Lewis et al. | May 2016 | B2 |
| 9333324 | Cohen et al. | May 2016 | B2 |
| 9351716 | Miles et al. | May 2016 | B2 |
| 9364636 | Grewe et al. | Jun 2016 | B2 |
| 9370868 | Danitz et al. | Jun 2016 | B2 |
| 9399300 | Goldberg et al. | Jul 2016 | B2 |
| 9427282 | Belson et al. | Aug 2016 | B2 |
| 9434077 | Danitz et al. | Sep 2016 | B2 |
| 9439732 | Devengenzo et al. | Sep 2016 | B2 |
| 9474527 | Knodel et al. | Oct 2016 | B1 |
| 9477301 | Kishi | Oct 2016 | B2 |
| 9480533 | Devengenzo et al. | Nov 2016 | B2 |
| 9492192 | To et al. | Nov 2016 | B2 |
| 9498602 | Osypka et al. | Nov 2016 | B2 |
| 9504371 | Mitchell et al. | Nov 2016 | B2 |
| 9504372 | Kim | Nov 2016 | B2 |
| 9510885 | Burger et al. | Dec 2016 | B2 |
| 9517326 | Hinman et al. | Dec 2016 | B2 |
| 9533122 | Weitzner et al. | Jan 2017 | B2 |
| 9572957 | Osypka et al. | Feb 2017 | B2 |
| D782037 | Osypka et al. | Mar 2017 | S |
| 9585647 | Clark | Mar 2017 | B2 |
| 9597514 | Khairkhahan et al. | Mar 2017 | B2 |
| 9622729 | Dewaele et al. | Apr 2017 | B2 |
| 9636483 | Hart et al. | May 2017 | B2 |
| 9642514 | Gilboa | May 2017 | B2 |
| 9662473 | McDaniel et al. | May 2017 | B2 |
| 9675422 | Hourtash et al. | Jun 2017 | B2 |
| 9693759 | Seguy | Jul 2017 | B2 |
| 9693780 | Miles et al. | Jul 2017 | B2 |
| 9713509 | Schuh et al. | Jul 2017 | B2 |
| 9724163 | Orban | Aug 2017 | B2 |
| 9737365 | Hegeman et al. | Aug 2017 | B2 |
| 9737371 | Romo et al. | Aug 2017 | B2 |
| 9743990 | Au et al. | Aug 2017 | B2 |
| 9744335 | Jiang | Aug 2017 | B2 |
| 9750577 | Pacheco et al. | Sep 2017 | B2 |
| 9757149 | Cooper et al. | Sep 2017 | B2 |
| 9757538 | Kimmel et al. | Sep 2017 | B2 |
| 9763741 | Alvarez et al. | Sep 2017 | B2 |
| 9775963 | Miller | Oct 2017 | B2 |
| 9782130 | Hauck et al. | Oct 2017 | B2 |
| 9814490 | Neoh et al. | Nov 2017 | B2 |
| 9844412 | Bogusky et al. | Dec 2017 | B2 |
| 9848949 | Osypka | Dec 2017 | B2 |
| 9861790 | Selkee | Jan 2018 | B2 |
| 9867982 | Berthiaume et al. | Jan 2018 | B2 |
| 9877781 | Grasse et al. | Jan 2018 | B2 |
| 9883864 | Miles et al. | Feb 2018 | B2 |
| 9889273 | Cully et al. | Feb 2018 | B2 |
| 9895813 | Blumenkranz et al. | Feb 2018 | B2 |
| 9901408 | Larkin | Feb 2018 | B2 |
| 9907570 | Osypka et al. | Mar 2018 | B2 |
| 9913684 | Osypka | Mar 2018 | B2 |
| 9937322 | Drake et al. | Apr 2018 | B2 |
| 9949749 | Noonan et al. | Apr 2018 | B2 |
| 9955986 | Shah | May 2018 | B2 |
| 9956051 | Stokes et al. | May 2018 | B2 |
| 9962066 | Rogers et al. | May 2018 | B2 |
| 9968241 | Iuel | May 2018 | B2 |
| 9974619 | Goldenberg et al. | May 2018 | B2 |
| 9993211 | Tokuda et al. | Jun 2018 | B2 |
| 9993313 | Schuh et al. | Jun 2018 | B2 |
| 10010367 | Chien et al. | Jul 2018 | B2 |
| 10010699 | Cohen et al. | Jul 2018 | B2 |
| 10010700 | Romoscanu | Jul 2018 | B2 |
| 10016187 | Castro | Jul 2018 | B2 |
| 10016191 | Kaiser et al. | Jul 2018 | B2 |
| 10029073 | Kabe et al. | Jul 2018 | B2 |
| 10029074 | Mogul | Jul 2018 | B2 |
| 10035002 | Weiss | Jul 2018 | B2 |
| 10039436 | Tah et al. | Aug 2018 | B2 |
| 10076337 | Miles et al. | Sep 2018 | B2 |
| 10080608 | Datta et al. | Sep 2018 | B2 |
| 10099036 | Heideman et al. | Oct 2018 | B2 |
| 10130427 | Tanner et al. | Nov 2018 | B2 |
| 10143360 | Roelle et al. | Dec 2018 | B2 |
| 10159533 | Moll et al. | Dec 2018 | B2 |
| 10173073 | Webler, Jr. et al. | Jan 2019 | B2 |
| 10188273 | Tilson et al. | Jan 2019 | B2 |
| 10188832 | Salahieh et al. | Jan 2019 | B2 |
| 10201394 | Yanagihara et al. | Feb 2019 | B2 |
| 10219874 | Yu et al. | Mar 2019 | B2 |
| 10238837 | Duindam et al. | Mar 2019 | B2 |
| 10265502 | Tsai et al. | Apr 2019 | B2 |
| 10286183 | Flygare et al. | May 2019 | B2 |
| 10292719 | Burger et al. | May 2019 | B2 |
| 10300286 | Ward et al. | May 2019 | B2 |
| 10307214 | Lathrop et al. | Jun 2019 | B2 |
| 10321923 | DeGraaf et al. | Jun 2019 | B2 |
| 10321964 | Grover et al. | Jun 2019 | B2 |
| 10327625 | Belson et al. | Jun 2019 | B2 |
| 10335243 | Yanagihara et al. | Jul 2019 | B2 |
| 10342411 | Japerson et al. | Jul 2019 | B2 |
| 10342625 | Loh et al. | Jul 2019 | B2 |
| 10349943 | Noonan et al. | Jul 2019 | B2 |
| 10363109 | Dachs et al. | Jul 2019 | B2 |
| 10376170 | Quinn et al. | Aug 2019 | B2 |
| 10398499 | Clark et al. | Sep 2019 | B2 |
| 10405939 | Romo | Sep 2019 | B2 |
| 10405940 | Romo | Sep 2019 | B2 |
| 10413708 | Loh | Sep 2019 | B2 |
| 10426557 | Amiri et al. | Oct 2019 | B2 |
| 10426560 | Bowling et al. | Oct 2019 | B2 |
| 10454347 | Covington et al. | Oct 2019 | B2 |
| 10456166 | Cooper et al. | Oct 2019 | B2 |
| 10463439 | Joseph et al. | Nov 2019 | B2 |
| 10463440 | Bowling et al. | Nov 2019 | B2 |
| 10470830 | Hill et al. | Nov 2019 | B2 |
| 10478296 | Le et al. | Nov 2019 | B2 |
| 10482599 | Mintz et al. | Nov 2019 | B2 |
| 10485621 | Morrissette et al. | Nov 2019 | B2 |
| 10493239 | Hart et al. | Dec 2019 | B2 |
| 10493241 | Jiang | Dec 2019 | B2 |
| 10500373 | Barrish et al. | Dec 2019 | B2 |
| 10507063 | Zuhars | Dec 2019 | B2 |
| 10512757 | Laby et al. | Dec 2019 | B2 |
| 10525233 | Barrish et al. | Jan 2020 | B2 |
| 10525257 | Govea et al. | Jan 2020 | B2 |
| 10537385 | Wu et al. | Jan 2020 | B2 |
| 10537713 | Kidd et al. | Jan 2020 | B2 |
| 10543047 | Yu | Jan 2020 | B2 |
| 10555780 | Tanner et al. | Feb 2020 | B2 |
| 10556092 | Yu et al. | Feb 2020 | B2 |
| 10568708 | Au et al. | Feb 2020 | B2 |
| 10575745 | Solis | Mar 2020 | B2 |
| 10576243 | Suon et al. | Mar 2020 | B2 |
| 10582929 | Miles et al. | Mar 2020 | B2 |
| 10583271 | Bogusky | Mar 2020 | B2 |
| 10588495 | Simmons et al. | Mar 2020 | B2 |
| 10588620 | Caffes et al. | Mar 2020 | B2 |
| 10603124 | Camarillo et al. | Mar 2020 | B2 |
| 10610312 | Srivastava et al. | Apr 2020 | B2 |
| 10610320 | Dachs et al. | Apr 2020 | B2 |
| 10617402 | Reddy et al. | Apr 2020 | B2 |
| 10617848 | Weitzner et al. | Apr 2020 | B2 |
| 10631937 | Tyulmankov et al. | Apr 2020 | B2 |
| 10631949 | Schuh et al. | Apr 2020 | B2 |
| 10632281 | Rosenman et al. | Apr 2020 | B2 |
| 10646696 | Barrish et al. | May 2020 | B2 |
| 10667871 | Romo et al. | Jun 2020 | B2 |
| 10682503 | Gerrans et al. | Jun 2020 | B2 |
| 10687691 | Smith et al. | Jun 2020 | B2 |
| 10695536 | Weitzner et al. | Jun 2020 | B2 |
| 10709512 | Bajo et al. | Jul 2020 | B2 |
| 10737064 | Ju | Aug 2020 | B1 |
| 10737073 | Barrish et al. | Aug 2020 | B2 |
| 10744303 | Duindam et al. | Aug 2020 | B2 |
| 10758310 | Shelton, IV | Sep 2020 | B2 |
| 10758714 | Laby et al. | Sep 2020 | B2 |
| 10772637 | Miles et al. | Sep 2020 | B2 |
| 10779710 | Matthison-Hansen | Sep 2020 | B2 |
| 10779803 | Prisco et al. | Sep 2020 | B2 |
| 10779898 | Hill et al. | Sep 2020 | B2 |
| 10786329 | Schuh et al. | Sep 2020 | B2 |
| 10792061 | Dewaele et al. | Oct 2020 | B2 |
| 10792464 | Romo et al. | Oct 2020 | B2 |
| 10792468 | Bolduc et al. | Oct 2020 | B2 |
| 10799096 | Golden et al. | Oct 2020 | B2 |
| 10799224 | Tasci | Oct 2020 | B2 |
| 10806897 | Furnish | Oct 2020 | B2 |
| 10806899 | Laby et al. | Oct 2020 | B2 |
| 10813708 | Reinstein et al. | Oct 2020 | B2 |
| 10814101 | Jiang | Oct 2020 | B2 |
| 10814102 | Laby et al. | Oct 2020 | B2 |
| 10881832 | Chu | Jan 2021 | B2 |
| 10932877 | Devengenzo et al. | Mar 2021 | B2 |
| 10939972 | Au | Mar 2021 | B2 |
| 10939973 | DiMaio et al. | Mar 2021 | B2 |
| 10944728 | Wiener | Mar 2021 | B2 |
| 11058508 | Scheib et al. | Jul 2021 | B2 |
| 11129685 | Zemlok et al. | Sep 2021 | B2 |
| 11160621 | Steger | Nov 2021 | B2 |
| 11213362 | Sharon et al. | Jan 2022 | B2 |
| 11241291 | Sharon et al. | Feb 2022 | B2 |
| 11259881 | Kilroy et al. | Mar 2022 | B2 |
| 11284956 | Zemlok et al. | Mar 2022 | B2 |
| 11318297 | Li et al. | May 2022 | B2 |
| 11369450 | Bernstein | Jun 2022 | B2 |
| 11406465 | Zemlok et al. | Aug 2022 | B2 |
| 11419691 | Kim et al. | Aug 2022 | B2 |
| 11446105 | Dachs et al. | Sep 2022 | B2 |
| 20070078334 | Scully | Apr 2007 | A1 |
| 20080119872 | Brock et al. | May 2008 | A1 |
| 20080125793 | Brock et al. | May 2008 | A1 |
| 20080125794 | Brock et al. | May 2008 | A1 |
| 20080177285 | Brock et al. | Jul 2008 | A1 |
| 20080228065 | Viswanathan et al. | Sep 2008 | A1 |
| 20080262301 | Gibbons et al. | Oct 2008 | A1 |
| 20080319314 | Hill et al. | Dec 2008 | A1 |
| 20090105700 | Anderson | Apr 2009 | A1 |
| 20090171147 | Lee et al. | Jul 2009 | A1 |
| 20090324161 | Prisco | Dec 2009 | A1 |
| 20100204646 | Plicchi et al. | Aug 2010 | A1 |
| 20100234938 | Taheri et al. | Sep 2010 | A1 |
| 20100249497 | Peine et al. | Sep 2010 | A1 |
| 20110046441 | Wiltshire et al. | Feb 2011 | A1 |
| 20110066161 | Cooper | Mar 2011 | A1 |
| 20110154473 | Anderson et al. | Jun 2011 | A1 |
| 20110213383 | Lee et al. | Sep 2011 | A1 |
| 20110276034 | Tomarelli et al. | Nov 2011 | A1 |
| 20110319714 | Roelle et al. | Dec 2011 | A1 |
| 20120016311 | Altman et al. | Jan 2012 | A1 |
| 20120253324 | Lee et al. | Oct 2012 | A1 |
| 20120287238 | Onishi | Nov 2012 | A1 |
| 20130041314 | Dillon | Feb 2013 | A1 |
| 20130158509 | Consigny et al. | Jun 2013 | A1 |
| 20130253469 | Freed | Sep 2013 | A1 |
| 20150045696 | Osypka | Feb 2015 | A1 |
| 20150057610 | Osypka et al. | Feb 2015 | A1 |
| 20150105721 | Osypka et al. | Apr 2015 | A1 |
| 20150164594 | Romo et al. | Jun 2015 | A1 |
| 20150202020 | Fisher | Jul 2015 | A1 |
| 20150335383 | Cohen | Nov 2015 | A9 |
| 20160067457 | Selkee | Mar 2016 | A1 |
| 20160113711 | Osypka et al. | Apr 2016 | A1 |
| 20160213423 | Kauphusman et al. | Jul 2016 | A1 |
| 20160213884 | Park | Jul 2016 | A1 |
| 20160228032 | Walker et al. | Aug 2016 | A1 |
| 20160287226 | Al-Obani et al. | Oct 2016 | A1 |
| 20170106170 | Hsueh et al. | Apr 2017 | A1 |
| 20170224199 | Demers et al. | Aug 2017 | A1 |
| 20170273665 | Kapoor et al. | Sep 2017 | A1 |
| 20170312002 | Sliwa et al. | Nov 2017 | A1 |
| 20180085552 | Miller | Mar 2018 | A1 |
| 20180104007 | Scheller et al. | Apr 2018 | A1 |
| 20180117280 | Chu | May 2018 | A1 |
| 20180140177 | Liu et al. | May 2018 | A1 |
| 20180207402 | Tegg | Jul 2018 | A1 |
| 20180228557 | Darisse et al. | Aug 2018 | A1 |
| 20180333212 | Goldenberg et al. | Nov 2018 | A1 |
| 20180344979 | Schultz | Dec 2018 | A1 |
| 20190000546 | Romoscanu | Jan 2019 | A1 |
| 20190008601 | Pereira et al. | Jan 2019 | A1 |
| 20190015633 | Bednarek et al. | Jan 2019 | A1 |
| 20190022356 | Bansal et al. | Jan 2019 | A1 |
| 20190076093 | Saroha et al. | Mar 2019 | A1 |
| 20190076160 | Lin et al. | Mar 2019 | A1 |
| 20190105481 | Lin et al. | Apr 2019 | A1 |
| 20190111239 | Bolduc et al. | Apr 2019 | A1 |
| 20190142413 | Fairneny | May 2019 | A1 |
| 20190142591 | Rohl et al. | May 2019 | A1 |
| 20190151614 | Hsueh et al. | May 2019 | A1 |
| 20190209810 | Reid et al. | Jul 2019 | A1 |
| 20190216486 | Weitzman | Jul 2019 | A1 |
| 20190223734 | Lakkireddy et al. | Jul 2019 | A1 |
| 20190224466 | Oliverius et al. | Jul 2019 | A1 |
| 20190254690 | Cabiri et al. | Aug 2019 | A1 |
| 20190255292 | Osypka et al. | Aug 2019 | A1 |
| 20190269368 | Hauck et al. | Sep 2019 | A1 |
| 20190274757 | Mahapatra et al. | Sep 2019 | A1 |
| 20190290886 | Campbell et al. | Sep 2019 | A1 |
| 20190298969 | Dale et al. | Oct 2019 | A1 |
| 20190307517 | Arai | Oct 2019 | A1 |
| 20190314052 | Thapliyal et al. | Oct 2019 | A1 |
| 20190314608 | Della Vecchia | Oct 2019 | A1 |
| 20190321597 | Van Hoven et al. | Oct 2019 | A1 |
| 20190365204 | Lang et al. | Dec 2019 | A1 |
| 20190365536 | Prabhu | Dec 2019 | A1 |
| 20190366040 | Golden et al. | Dec 2019 | A1 |
| 20190388099 | Zuhars et al. | Dec 2019 | A1 |
| 20200015911 | Yi | Jan 2020 | A1 |
| 20200023151 | Karlsson et al. | Jan 2020 | A1 |
| 20200060683 | Friedman et al. | Feb 2020 | A1 |
| 20200060820 | Ben-Zvi et al. | Feb 2020 | A1 |
| 20200069389 | Morrissette et al. | Mar 2020 | A1 |
| 20200078019 | Dell et al. | Mar 2020 | A1 |
| 20200085483 | Tegg et al. | Mar 2020 | A1 |
| 20200100896 | Jimenez et al. | Apr 2020 | A1 |
| 20200138454 | Patel et al. | May 2020 | A1 |
| 20200179650 | Beeckler et al. | Jun 2020 | A1 |
| 20200179651 | Scheltes et al. | Jun 2020 | A1 |
| 20200196896 | Solis | Jun 2020 | A1 |
| 20200197111 | Kim et al. | Jun 2020 | A1 |
| 20200222139 | Dachs et al. | Jul 2020 | A1 |
| 20200222667 | Tang et al. | Jul 2020 | A1 |
| 20200230354 | von Oepen et al. | Jul 2020 | A1 |
| 20200230360 | Yu et al. | Jul 2020 | A1 |
| 20200269017 | Winston et al. | Aug 2020 | A1 |
| 20200276415 | Tang et al. | Sep 2020 | A1 |
| 20200289224 | Johnson et al. | Sep 2020 | A1 |
| 20200289789 | Scheibe et al. | Sep 2020 | A1 |
| 20200297444 | Camarillo | Sep 2020 | A1 |
| 20200297971 | Beeckler et al. | Sep 2020 | A1 |
| 20200330729 | Petitpierre et al. | Oct 2020 | A1 |
| 20200337766 | Dando et al. | Oct 2020 | A1 |
| 20200337792 | Zemlok et al. | Oct 2020 | A1 |
| 20200383734 | Dahdouh | Dec 2020 | A1 |
| 20200405397 | Liu | Dec 2020 | A1 |
| 20210015573 | Tsao et al. | Jan 2021 | A1 |
| 20210030496 | Devengenzo et al. | Feb 2021 | A1 |
| 20210045626 | Hsu | Feb 2021 | A1 |
| 20210153961 | DiMaio et al. | May 2021 | A1 |
| 20210154473 | Pobiel | May 2021 | A1 |
| 20210220060 | Kapadia et al. | Jul 2021 | A1 |
| 20210236773 | Dupont | Aug 2021 | A1 |
| 20210267702 | Kim et al. | Sep 2021 | A1 |
| 20210267703 | Devengenzo et al. | Sep 2021 | A1 |
| 20220000570 | Steger | Jan 2022 | A1 |
| 20220008152 | Daniel | Jan 2022 | A1 |
| 20220044057 | Ganesan et al. | Feb 2022 | A1 |
| 20220071723 | Sharon et al. | Mar 2022 | A1 |
| 20220230348 | Huber et al. | Jul 2022 | A1 |
| 20220273388 | Bernstein | Sep 2022 | A1 |
| 20220296321 | Sharon et al. | Sep 2022 | A1 |
| 20220296322 | Gomez et al. | Sep 2022 | A1 |
| 20220304758 | Scheib et al. | Sep 2022 | A1 |
| 20220361959 | Weber et al. | Nov 2022 | A1 |
| Number | Date | Country |
|---|---|---|
| 105125241 | Dec 2015 | CN |
| 20 2012 006 263 | Sep 2013 | DE |
| WO 2013141011 | Sep 2013 | WO |
| WO 2017003453 | Jan 2017 | WO |
| WO 2017070193 | Apr 2017 | WO |
| WO 2017115352 | Jul 2017 | WO |
| WO 2017155867 | Sep 2017 | WO |
| WO 2018080470 | May 2018 | WO |
| WO 2018204202 | Jul 2018 | WO |
| WO 2019089053 | May 2019 | WO |
| WO 2019136357 | Jul 2019 | WO |
| WO 2019156559 | Aug 2019 | WO |
| WO 2019240655 | Dec 2019 | WO |
| WO 2020150709 | Jul 2020 | WO |
| WO 2020187663 | Sep 2020 | WO |
| WO 2021138096 | Jul 2021 | WO |
| WO 2022066730 | Mar 2022 | WO |
| Entry |
|---|
| Adeoye O, Albright K, Carr B, et al., “Geographic access to acute stroke care in the United States.” Stroke. 2014. 45:3019-3024. |
| Bajo et al.. “Finding Lost Wrenches: Using Continuum Robots for Contact Detection and Estimation of Contact Location.” 2010 IEEE International Conference on Robotics and Automation. Anchorage Convention District. May 3-8, 2010, Anchorage, Alaska, USA. pp. 3666-3673. |
| A. Bajo and N. Simaan, “Kinematics-Based Detection and Localization of Contacts Along Multisegment Continuum Robots,” IEEE Transactions on Robotics, vol. 28, No. 2, pp. 291-302, Apr. 2012. |
| A. Bajo and N. Simaan, “Hybrid motion/force control of multibackbone continuum robots,” The International Journal of Robotics Research. 35(4): 422-434. 2016. |
| J. Burgner-Kahrs, D. C. Rucker, and H. Choset, “Continuum Robots for Medical Applications—A Survey,” IEEE Transactions on Robotics, 31(6): 1261-1280, Dec. 2015. |
| Campbell BCV, De Silva DA, Macleod MR, Coutts SB, Schwamm LH, Davis SM, and Donnan GA, “Ischaemic stroke” (2019) 5(70) Nature Reviews Disease Primers; pp. 1-6. |
| Comprehensive Stroke Center', The Joint Commission (Web Page) (2021); pp. 1-5. |
| J. J. Craig, Introduction to Robotics: Mechanics and Control. Pearson Prentice Hall Upper Saddle River, 2005, vol. 3. TOC. |
| Dasgupta et al., A Fully Convolutional Neural Network Based Structure Prediction Approach towards the Retival Vessel Segmentation, Nov. 16, 2016; pp. 1-4. |
| A. Degirmenci, P. M. Loschak, C. M. Tschabrunn, E. Anter, and R. D. Howe, “Compensation for Unconstrained Catheter Shaft Motion in Cardiac Catheters,” in 2016 IEEE International Conference on Robotics and Automation (ICRA). May 2016, pp. 4436-4442. |
| C. S. Dela Cruz et al. “Lung Cancer: Epidemiology, Etiology, and Prevention,” Clinics in Chest Medicine, vol. 32, No. 4, pp. 605-644, 2011. |
| International Search Report and Written Opinion for International Application No. PCT/US2021/070726 dated Sep. 17, 2021. |
| B. Jones and I. Walker, “Kinematics for multisection continuum robots,” IEEE Transactions on Robotics, vol. 22, No. 1, pp. 43-55, Feb. 2006. |
| B. Kim, J. Ha, F. C. Park, and P. E. Dupont, “Optimizing curvature sensor placement for fast, accurate shape sensing of continuum robots,” in Proceedings—IEEE International Conference on Robotics and Automation. Institute of Electrical and Electronics Engineers Inc., 2014, pp. 5374-5379. |
| Chunwoo Kim, S. C. Ryu, and P. E. Dupont, “Real-time adaptive kinematic model estimation of concentric tube robots,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, Sep. 2015, pp. 3214-3219. |
| A. Brij Koolwal, F. Barbagli, C. Carlson, and D. Liang, “An Ultrasound-based Localization Algorithm for Catheter Ablation Guidance in the Left Atrium,” The International Journal of Robotics Research, vol. 29, No. 6, pp. 643-665, May 2010. |
| A. W. Mahoney, T. L. Bruns, P. J. Swaney, and R. J. Webster, “On the inseparable nature of sensor selection, sensor placement, and state estimation for continuum robots or where to put your sensors and how to use them,” in 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, May 2016, pp. 4472-4478. |
| Messenger JC, Ho KKL, Young CH et al. and NCDR Science and Quality Oversight Committee Data Quality Workgroup, “The National Cardiovascular Data Registry (NCDR) Data Quality Brief: The NCDR Data Quality Program in 2012.” Journal of the American College of Cardiology. (2012); 60(16):1484-1488. |
| D. E. Ost, et al., “Diagnostic Yield and Complications of Bronchoscopy for Peripheral Lung Lesions. Results of the AQuIRE Registry,” American Journal of Respiratory and Critical Care Medicine, vol. 193, No. 1, pp. 68-77, Jan. 2016. |
| J. Rosell, A. Perez, P. Cabras, and A. Rosell, “Motion planning for the Virtual Bronchoscopy,” in 2012 IEEE International Conference on Robotics and Automation. IEEE, May 2012, pp. 2932-2937. |
| Saver JL, Goyal M, van der Lugt A, et al., “Time to treatment with endovascular thrombectomy and outcomes from ischemic stroke: a meta-analysis.” JAMA (2016) 316(12):1279-1288. |
| Sganga, Jake, et al. “Autonomous Driving in the Lung using Deep Learning for Localization.” Main Paper. Jul. 16, 2019; pp. 1-10. |
| Sganga, Jake, et al. “OffsetNet: Deep Learning for Localization in the Lung using Rendered Images.” Stanford Education; Sep. 15, 2018. pp. 1-7. |
| Sganga, Jake, et al. “Orientation Estimation of a Continuum Manipulator in a Phantom Lung.” Stanford Education; pp. 1-7. |
| O. K. Bruno Siciliano, Handbook of Robotics, 2008, Springer, vol. 53, No. 9. TOC. |
| S. Tully and H. Choset, “A Filtering Approach for Image-Guided Surgery with a Highly Articulated Surgical Snake Robot,” IEEE Transactions on Biomedical Engineering, vol. 63, No. 2, pp. 392-402, Feb. 2016. |
| Venema E, Groot AE, Lingsma HF, et al., ‘Effect of interhospital transfer on endovascular treatment for acute ischemic stroke’ (2019); pp. 1-8. |
| Walker. “Continuous Backbone “Continuum” Robot Manipulators.” ISRN Robotics. Hindawi Publishing Corporation. vol. 2013, Article ID 726506, 19 pages. |
| E. Wan and R. Van Der Merwe, “The unscented Kalman filter for nonlinear estimation,” in Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373). IEEE, pp. 153-158. |
| R. J. Webster and B. A. Jones, “Design and Kinematic Modeling of Constant Curvature Continuum Robots: A Review,” The International Journal of Robotics Research, vol. 29, No. 13, pp. 1661-1683, Nov. 2010. |
| R. Xu, A. Yurkewich, and R. V. Patel, “Curvature, Torsion, and Force Sensing in Continuum Robots Using Helically Wrapped FBG Sensors,” IEEE Robotics and Automation Letters, vol. 1, No. 2, pp. 1052-1059, Jul. 2016. |
| R. Xu, A. Yurkewich, and R. V. Patel, “Shape sensing for torsionally compliant concentric-tube robots,” in SPIE BiOS, I. Gannot, Ed. International Society for Optics and Photonics, Mar. 2016, Proc. of SPIE vol. 9702, 97020V. 9 pages. |
| Yang et al., Deep Learning Segmentation of Major Vessels in X-Ray Coronary Angiography, Scientific Reports (2019); pp. 1-11. |
| M. Yip and D. Camarillo, “Model-Less Feedback Control of Continuum Manipulators in Constrained Environments,” IEEE Transactions on Robotics, vol. 30, No. 4, pp. 880-889, Aug. 2014. |
| International Search Report for International Application No. PCT/US2022/035848 dated Aug. 12, 2022. |
| Buther F, Dawood M, Stegger L, Wubbeling F, Schafers M, Schober O, Schafers KP. List mode-driven cardiac and respiratory gating in PET. J Nucl Med. 2009;50(5):674-81. Epub Apr. 18, 2009. doi: 10.2967/jnumed.108.059204. PubMed PMID: 19372491. |
| Desjardins B, Kazerooni EA. ECG-gated cardiac CT. AJR Am J Roentgenol. 2004;182(4):993-1010. Epub Mar. 25, 2004. doi: 10.2214/ajr.182.4.1820993. PubMed PMID: 15039178. |
| Fallavollita P. Is single-view fluoroscopy sufficient in guiding cardiac ablation procedures? Int J Biomed Imaging. 2010;2010:631264. doi: 10.1155/2010/631264. PubMed PMID: 20368770; PMCID: PMC2846336. |
| Jang S-J, Torabinia M, Dhrif H, Caprio A, Liu J, Wong SC, Mosadegh B. Development of aHybrid Training Simulator for Structural Heart Disease Interventions. Advanced Intelligent Systems. 2020;2(12):2000109. |
| Koivumaki T, Nekolla SG, Furst S, Loher S, Vauhkonen M, Schwaiger M, Hakulinen MA. An integrated bioimpedance—ECG gating technique for respiratory and cardiac motion compensation in cardiac PET. Phys Med Biol. 2014;59(21):6373-85. Epub Oct. 9, 2014. doi: 10.1088/0031-9155/59/21/6373. PubMed PMID: 25295531. |
| Liu J, AF Aref SJ, Singh G, Caprio A, Moghadam AAA, Jang S-J, Wong SC, Min JK, Dunham S, Mosadegh B. An augmented reality system for image guidance of transcatheter procedures for structural heart disease. PLOS ONE. 2019;14(7):e0219174. doi: 10.1371/journal. pone.0219174. |
| Ronneberger O, Fischer P, Brox T, editors. U-net: Convolutional networks for biomedicalimage segmentation. International Conference on Medical image computing and computerassisted intervention; 2015: Springer. |
| Sra J, Krum D, Choudhuri I, Belanger B, Palma M, Brodnick D, Rowe DB. Identifying the third dimension in 2D fluoroscopy to create 3D cardiac maps. JCI Insight. 2016;I(21):e90453. doi: 10.1172/jci.insight.90453. PubMed PMID: 28018976; PMCID: PMC5 161213. |
| Torabinia, M., Caprio, A., Jang, S. J., Ma, T., Tran, H., Mekki, L., & Mosadegh, B. (2021). Deep learning-driven catheter tracking from bi-plane X-ray fluoroscopy of 3D printed heart phantoms. Mini-invasive Surgery, 5. |
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