Patent Description:
Computer-assisted surgical systems facilitate planning and execution of TKA procedures to assist in the modification of bones and implant alignment on the bones. However, conventional computer-assisted surgical systems often do not address issues involving soft tissue, for example ligaments such as the posterior cruciate ligament (PCL) and anterior cruciate ligament (ACL), or take soft tissue into account when creating a surgical plan. This may result in iatrogenic soft tissue damage, weakening of attachment points between bone and soft tissue, impingement of ligaments by implant components, and other complications. <CIT> discloses a method of generating resection plane data for use in planning an arthroplasty procedure on a patient bone. The method includes: obtaining patient data associated with at least a portion of the patient bone, the patient data captured using a medical imaging machine; generating a three-dimensional patient bone model from the patient data, the patient bone model including a polygonal surface mesh; identifying a location of a posterior point on the polygonal surface mesh; creating a three-dimensional shape centered at or near the location; identifying a most posterior vertex of all vertices of the polygonal surface mesh that may be enclosed by the three dimensional shape; using the most posterior vertex as a factor for determining a posterior resection depth; and generating resection data using the posterior resection depth, the resection data configured to be utilized by a navigation system during the arthroplasty procedure.

One implementation of the present disclosure is a surgical system. The surgical system includes a robotic device, a surgical tool mounted on the robotic device, and a processing circuit. The processing circuit is configured to receive image data of an anatomy, generate a virtual bone model based on the image data, identify a soft tissue
attachment point on the virtual bone model, plan placement of an implant based on the soft tissue attachment point, generate a control object based on the placement of the implant, and control the robotic device to confine the surgical tool within the control object.

In some embodiments, the soft tissue attachment point corresponds to a site where a posterior cruciate ligament or an anterior cruciate ligament attaches to a femur or a tibia. In some embodiments, the processing circuit is configured to plan placement of the implant based on the soft tissue attachment point by aligning an axis of the implant with a medial edge of the soft tissue attachment point.

In some embodiments, the processing circuit is further configured to generate a graphical user interface. The graphical user interface includes a visualization of the virtual bone model, the implant, and the soft tissue attachment point. In some embodiments, the graphical user interface includes a visualization of the control object. In some embodiments, the processing circuit is configured to restrict the control object from containing the soft tissue attachment point.

Another implementation of the present disclosure is a method. The method includes receiving image data of an anatomy, generating a virtual bone model based on the image data, identifying a soft tissue attachment point on the virtual bone model, determining implant size and placement based on the soft tissue attachment point, generating a control object based on the implant size and placement, constraining or controlling a surgical tool mounted on a robotic device based on the control object.

In some embodiments, the image data includes computed tomography images, and wherein the method also includes segmenting the computed tomography images to identify one or more bones in the images. In some embodiments, the soft tissue attachment point corresponds to a site where a posterior cruciate ligament or an anterior cruciate ligament attaches to a femur or a tibia. The soft tissue attachment point may correspond to a site where a patellar ligament attaches to a tibia. In some embodiments, determining implant placement includes aligning an axis of the implant with a medial edge of the soft tissue attachment point.

In some embodiments, the method also includes generating a graphical user interface that visualizes the virtual bone model, the implant, and the soft tissue attachment point. The graphical user interface may further provide a visualization of the control object. In some embodiments, the method includes predicting a line of action of a ligament based on the soft tissue attachment point, augmenting the virtual bone model with a virtual implant model of the implant, determining whether the line of action of the ligament intersects the virtual implant model, and, in response to a determination that the line of action of the ligament intersects the virtual implant model, providing an alert to a user. The method may include restriction the control object from containing the soft tissue attachment point.

Another implementation of the present disclosure is non-transitory computer-readable media storing program instructions that, when executed by one or more processors, cause the one or more processors to perform operations. The operations include receiving image data of an anatomy, generating a virtual bone model based on the image data, identifying a soft tissue attachment point on the virtual bone model, determining implant size and placement based on the soft tissue attachment point, generating a control object based on the implant size and placement, and constraining or controlling a surgical tool mounted on a robotic device based on the control object.

In some embodiments, the operations include restricting the control object from containing the soft tissue attachment point. In some embodiments, the operations include predicting a line of action of a ligament based on the soft tissue attachment point, augmenting the virtual bone model with a virtual implant model of the implant, determining whether the line of action of the ligament intersects the virtual implant model, and, in response to a determination that the line of action of the ligament intersects the virtual implant model, providing an alert to a user. In some embodiments, determining implant placement includes aligning an axis of the implant with a medial edge of the soft tissue attachment point.

This summary is illustrative only and is not intended to be in any way limiting. Other aspects, features, and advantages of the devices or processes described herein will become apparent in the detailed description set forth herein, taken in conjunction with the accompanying figures, wherein like reference numerals refer to like elements.

Referring now to <FIG>, a surgical system <NUM> for orthopedic surgery is shown, according to an exemplary embodiment. In general, the surgical system <NUM> is configured to facilitate the planning and execution of a surgical procedure. The surgical system <NUM> is configured to treat the anatomy of a patient, for example, as shown in <FIG>, a leg <NUM> of a patient <NUM> sitting or lying on table <NUM>. Leg <NUM> includes femur <NUM> and tibia <NUM>, between which a prosthetic knee implant is to be implanted in a total knee arthroscopy procedure. The surgical system <NUM> may additionally or alternatively be configured to facilitate the planning and execution of partial knee arthroscopy procedures, total and/or partial hip arthroscopy procedures, other joint procedures, spinal procedures, and any other surgical procedure (e.g., neurosurgical, orthopedic, urological, gynecological, dental, ENT, oncological). To facilitate the procedure, surgical system <NUM> includes robotic device <NUM>, tracking system <NUM>, and computing system <NUM>.

The robotic device <NUM> is configured to modify a patient's anatomy (e.g., femur <NUM> of patient <NUM>) under the control of the computing system <NUM>. One embodiment of the robotic device <NUM> is a haptic device. "Haptic" refers to a sense of touch, and the field of haptics relates to, among other things, human interactive devices that provide feedback to an operator. Feedback may include tactile sensations such as, for example, vibration. Feedback may also include providing force to a user, such as a positive force or a resistance to movement. One use of haptics is to provide a user of the device with guidance or limits for manipulation of that device. For example, a haptic device may be coupled to a surgical tool, which can be manipulated by a surgeon to perform a surgical procedure. The surgeon's manipulation of the surgical tool can be guided or limited through the use of haptics to provide feedback to the surgeon during manipulation of the surgical tool.

Another embodiment of the robotic device <NUM> is an autonomous or semi-autonomous robot. "Autonomous" refers to a robotic device's ability to act independently or semi-independently of human control by gathering information about its situation, determining a course of action, and automatically carrying out that course of action. For example, in such an embodiment, the robotic device <NUM>, in communication with the tracking system <NUM> and the computing system <NUM>, may autonomously complete a series of cuts in a patients' bone or soft tissue without direct human intervention. According to various embodiments, the robotic device <NUM> may carry out various combinations of unrestricted surgeon-controlled actions, haptically-constrained actions, and/or automated or autonomous robotic actions.

The robotic device <NUM> includes a base <NUM>, a robotic arm <NUM>, and a surgical tool <NUM>, and is communicably coupled to the computing system <NUM> and the tracking system <NUM>. The base <NUM> provides a moveable foundation for the robotic arm <NUM>, allowing the robotic arm <NUM> and the surgical tool <NUM> to be repositioned as needed relative to the patient <NUM> and the table <NUM>. The base <NUM> may also contain power systems, computing elements, motors, and other electronic or mechanical system necessary for the functions of the robotic arm <NUM> and the surgical tool <NUM> described below.

The robotic arm <NUM> is configured to support the surgical tool <NUM> and provide feedback as instructed by the computing system <NUM>. In some embodiments, the robotic arm <NUM> allows a user to manipulate the surgical tool <NUM> and provides force feedback to the user. In such an embodiment, the robotic arm <NUM> includes joints <NUM> and mount <NUM> that include motors, actuators, or other mechanisms configured to allow a user to freely translate and rotate the robotic arm <NUM> and surgical tool <NUM> through allowable poses while providing force feedback to constrain or prevent some movements of the robotic arm <NUM> and surgical tool <NUM> as instructed by computing system <NUM>. As described in detail below, the robotic arm <NUM> thereby allows a surgeon to have full control over the surgical tool <NUM> within a control object while providing force feedback along a boundary of that object (e.g., a vibration, a force preventing or resisting penetration of the boundary). In some embodiments, the robotic arm <NUM> is configured to move the surgical tool <NUM> to a new pose automatically without direct user manipulation, as instructed by computing system <NUM>, in order to position the robotic arm <NUM> as needed and/or complete certain surgical tasks, including, for example, cuts in a femur <NUM>.

In alternative embodiments, the robotic device <NUM> is a handheld robotic device or other type of robot. In the handheld robotic device, a portion (e.g., end effector, surgical tool) of the handheld robotic device may be robotically controlled/actuated relative to a body of the handheld robotic device. A user may hold, support, and manipulate the handheld robotic device as desired while the robotically-actuated portion is controlled to facilitate the surgeon in executing a surgical procedure. For example, the handheld robotic device may be controllable to retract a surgical cutting tool to prevent the user from operating the surgical cutting tool in an unsafe area. It should be understood that the systems and methods described herein may be implemented with various robotic devices of various types, designs, configurations, etc..

In the embodiment shown, the surgical tool <NUM> is configured to cut, grind, drill, partially resect, reshape, and/or otherwise modify a bone. For example, surgical tool <NUM> may be configured to make a series of cuts in femur <NUM> to prepare the femur <NUM> and or tibia <NUM> to receive an implant. The surgical tool <NUM> may be any suitable tool, and may be one of multiple tools interchangeably connectable to robotic device <NUM>. For example, as shown in <FIG> the surgical tool <NUM> is a spherical burr. The surgical tool <NUM> may also be a sagittal saw, for example with a blade aligned parallel with a tool axis or perpendicular to the tool axis. In other embodiments, the surgical tool <NUM> may be configured to execute one or more various other medical tasks (e.g., modifying soft tissue, implanting a prosthesis, generating an image, collecting data, providing retraction or tensioning).

Tracking system <NUM> is configured track the patient's anatomy (e.g., femur <NUM> and tibia <NUM>) and the robotic device <NUM> (i.e., surgical tool <NUM> and/or robotic arm <NUM>) to enable control of the surgical tool <NUM> coupled to the robotic arm <NUM>, to determine a position and orientation of actions completed by the surgical tool <NUM> relative to the patient's anatomy, and allow a user to visualize the femur <NUM>, the tibia <NUM>, the surgical tool <NUM>, and/or the robotic arm <NUM> on a display of the computing system <NUM>. More particularly, the tracking system <NUM> determines a position and orientation (i.e., pose) of objects (e.g., surgical tool <NUM>, femur <NUM>) with respect to a coordinate frame of reference and tracks (i.e., continuously determines) the pose of the objects during a surgical procedure. According to various embodiments, the tracking system <NUM> may be any type of navigation system, including a non-mechanical tracking system (e.g., an optical tracking system), a mechanical tracking system (e.g., tracking based on measuring the relative angles of joints <NUM> of the robotic arm <NUM>), or any combination of non-mechanical and mechanical tracking systems.

In the embodiment shown in <FIG>, the tracking system <NUM> includes an optical tracking system. Accordingly, tracking system <NUM> includes a first fiducial tree <NUM> coupled to the tibia <NUM>, a second fiducial tree <NUM> coupled to the femur <NUM>, a third fiducial tree <NUM> coupled to the base <NUM>, one or more fiducials <NUM> coupled to surgical tool <NUM>, and a detection device <NUM> configured to detect the three-dimensional position of fiducials (i.e., markers on fiducial trees <NUM>-<NUM>). As shown in <FIG>, detection device <NUM> includes a pair of cameras <NUM> in a stereoscopic arrangement. The fiducial trees <NUM>-<NUM> include fiducials, which are markers configured to show up clearly to the cameras <NUM> and/or be easily detectable by an image processing system using data from the cameras <NUM>, for example by being highly reflective of infrared radiation (e.g., emitted by an element of tracking system <NUM>). The stereoscopic arrangement of the cameras <NUM> on detection device <NUM> allows the position of each fiducial to be determined in 3D-space through a triangulation approach. Each fiducial has a geometric relationship to a corresponding object, such that tracking of the fiducials allows for the tracking of the object (e.g., tracking the second fiducial tree <NUM> allows the tracking system <NUM> to track the femur <NUM>), and the tracking system <NUM> may be configured to carry out a registration process to determine or verify this geometric relationship. Unique arrangements of the fiducials in the fiducial trees <NUM>-<NUM> (i.e., the fiducials in the first fiducial tree <NUM> are arranged in a different geometry than fiducials in the second fiducial tree <NUM>) allows for distinguishing the fiducial trees, and therefore the objects being tracked, from one another.

Using the tracking system <NUM> of <FIG> or some other approach to surgical navigation and tracking, the surgical system <NUM> can determine the position of the surgical tool <NUM> relative to a patient's anatomical feature, for example femur <NUM>, as the surgical tool <NUM> is used to make a cut in or otherwise modify the anatomical feature.

The computing system <NUM> is configured to create a surgical plan based on medical imaging or other data, receive data relating to the location of the surgical tool <NUM> and the patient's anatomy, and control the robotic device <NUM> in accordance with the surgical plan. In particular, in accordance with various embodiments described herein, the computing system <NUM> is configured to create a patient-specific surgical plan based on the location of and attachment points of soft tissue, and control the robotic device <NUM> using a control object that is patient-specific in accordance with the surgical plan. Accordingly, the computing system <NUM> is communicably coupled to the tracking system <NUM> and the robotic device <NUM> to facilitate electronic communication between the robotic device <NUM>, the tracking system <NUM>, and the computing system <NUM>. Further, the computing system <NUM> may be connected to a network to receive information related to a patient's medical history or other patient profile information, medical imaging, surgical plans, surgical procedures, and to perform various functions related to performance of surgical procedures, for example by accessing an electronic health records system. Computing system <NUM> includes processing circuit <NUM> and input/output device <NUM>. In some embodiments, a first computing device of computing system <NUM> (e.g., located in a surgeon's office, operated in a remote server) provides pre-operative features while a second computing device of computing system <NUM> (e.g., located in an operating room) controls the robotic device <NUM> and provides intraoperative features. According to various embodiments, the features and functions attributed herein to the computing system <NUM> may be implemented using any combination of or distribution between one or more devices, servers, cloud-based computing resources, etc..

The input/output device <NUM> is configured to receive user input and display output as needed for the functions and processes described herein. As shown in <FIG>, input/output device <NUM> includes a display <NUM> and a keyboard <NUM>. The display <NUM> is configured to display graphical user interfaces generated by the processing circuit <NUM> that include, for example, information about surgical plans, medical imaging, settings and other options for surgical system <NUM>, status information relating to the tracking system <NUM> and the robotic device <NUM>, and tracking visualizations based on data supplied by tracking system <NUM>. The keyboard <NUM> is configured to receive user input to those graphical user interfaces to control one or more functions of the surgical system <NUM>.

The processing circuit <NUM> is configured to facilitate the creation of a preoperative surgical plan prior to the surgical procedure and to facilitate computer-assistance or robotic-assistance in executing the surgical plan. An exemplary embodiment of the processing circuit <NUM> is shown in <FIG> and described in detail below with reference thereto.

Still referring to <FIG>, according to some embodiments the preoperative surgical plan is developed to be patient-specific utilizing a three-dimensional representation of a patient's anatomy, also referred to herein as a "virtual bone model. " A "virtual bone model" may include virtual representations of cartilage or other tissue in addition to bone. To obtain the virtual bone model, the processing circuit <NUM> receives imaging data of the patient's anatomy on which the surgical procedure is to be performed (e.g., femur <NUM>). The imaging data may be created using any suitable medical imaging technique to image the relevant anatomical feature, including computed tomography (CT), magnetic resonance imaging (MRI), and/or ultrasound. The imaging data is then segmented (i.e., the regions in the imaging corresponding to different anatomical features are distinguished) to obtain the virtual bone model. For example, MRI-based scan data of a knee is segmented to distinguish particular bones, ligaments, cartilage, and other tissue and processed to obtain a three-dimensional model of the imaged anatomy.

Alternatively, the virtual bone model may be obtained by selecting a three-dimensional model from a database or library of bone models. In one embodiment, the user may use input/output device <NUM> to select an appropriate model. In another embodiment, the processing circuit <NUM> may execute stored instructions to select an appropriate model based on images or other information provided about the patient. The selected bone model(s) from the database can then be deformed based on specific patient characteristics, creating a virtual bone model for use in surgical planning and implementation as described herein.

A preoperative surgical plan can then be created based on the virtual bone model. The surgical plan may be automatically generated by the processing circuit <NUM>, input by a user via input/output device <NUM>, or some combination of the two (e.g., the processing circuit <NUM> limits some features of user-created plans, generates a plan that a user can modify, etc.).

The preoperative surgical plan includes the desired cuts, holes, or other modifications to a patient's anatomy to be made using the surgical system <NUM>. For example, for a total knee arthroscopy procedure as described herein, the preoperative plan includes the cuts necessary to form surfaces on the femur <NUM> and tibia <NUM> to facilitate implantation of a prosthesis. Accordingly, the processing circuit <NUM> may receive, access, and/or store a model of the prosthesis to facilitate the generation of surgical plans.

The processing circuit <NUM> is further configured to generate a control object for the robotic device <NUM> in accordance with the surgical plan. In some embodiments as described herein, the control objects are patient-specific based on the location of and attachment points of soft tissue. The control object may take various forms according to the various types of possible robotic devices (e.g., haptic, autonomous, etc.). For example, in some embodiments, the control object defines instructions for the robotic device <NUM> to control the robotic device <NUM> to move within the control object (i.e., to autonomously make one or more cuts of the surgical plan guided by feedback from the tracking system <NUM>). In some embodiments, the control object includes a visualization of the surgical plan and the robotic device <NUM> on the display <NUM> to facilitate surgical navigation and help guide a surgeon to follow the surgical plan (e.g., without active control or force feedback of the robotic device <NUM>). In embodiments where the robotic device <NUM> is a haptic device, the control object may be a haptic object as described in the following paragraphs.

In an embodiment where the robotic device <NUM> is a haptic device, the processing circuit <NUM> is further configured to generate one or more haptic objects based on the preoperative surgical plan, particularly in consideration of the location of and attachment points of soft tissue, to assist the surgeon during implementation of the surgical plan by enabling constraint of the surgical tool <NUM> during the surgical procedure. A haptic object may be formed in one, two, or three dimensions. For example, a haptic object can be a line, a plane, or a three-dimensional volume. A haptic object may be curved with curved surfaces and/or have flat surfaces, and can be any shape, for example a funnel shape. Haptic objects can be created to represent a variety of desired outcomes for movement of the surgical tool <NUM> during the surgical procedure. One or more of the boundaries of a three-dimensional haptic object may represent one or more modifications, such as cuts, to be created on the surface of a bone. A planar haptic object may represent a modification, such as a cut, to be created on the surface of a bone (e.g., corresponding to the creation of surfaces intended to receive an implant).

In an embodiment where the robotic device <NUM> is a haptic device, the processing circuit <NUM> is further configured to generate a virtual tool representation of the surgical tool <NUM>. The virtual tool includes one or more haptic interaction points (HIPs), which represent and are associated with locations on the physical surgical tool <NUM>. In an embodiment in which the surgical tool <NUM> is a spherical burr (e.g., as shown in <FIG>), an HIP may represent the center of the spherical burr. If the surgical tool <NUM> is an irregular shape, for example as for a sagittal saw, the virtual representation of the sagittal saw may include numerous HIPs. Using multiple HIPs to generate haptic forces (e.g. positive force feedback or resistance to movement) on a surgical tool is described in <CIT>. In one embodiment of the present disclosure, a virtual tool representing a sagittal saw includes eleven HIPs. As used herein, references to an "HIP" are deemed to also include references to "one or more HIPs. " As described below, relationships between HIPs and haptic objects enable the surgical system <NUM> to constrain the surgical tool <NUM>.

Prior to performance of a surgical procedure, the patient's anatomy (e.g., femur <NUM>) may be registered to the virtual bone model of the patient's anatomy by any known registration technique. One possible registration technique is point-based registration, as described in <CIT>. Alternatively, registration may be accomplished by 2D/3D registration utilizing a hand-held radiographic imaging device, as described in <CIT>. Registration also includes registration of the surgical tool <NUM> to a virtual tool representation of the surgical tool <NUM>, so that the surgical system <NUM> can determine and monitor the pose of the surgical tool <NUM> relative to the patient (i.e., to femur <NUM>). Registration allows for accurate navigation, control, and/or force feedback during the surgical procedure.

The processing circuit <NUM> is configured to monitor the virtual positions of the virtual tool representation, the virtual bone model, and the control object (e.g., virtual haptic
objects) corresponding to the real-world positions of the patient's bone (e.g., femur <NUM>), the surgical tool <NUM>, and one or more lines, planes, or three-dimensional spaces defined by forces created by robotic device <NUM>. For example, if the patient's anatomy moves during the surgical procedure as tracked by the tracking system <NUM>, the processing circuit <NUM> correspondingly moves the virtual bone model. The virtual bone model therefore corresponds to, or is associated with, the patient's actual (i.e. physical) anatomy and the position and orientation of that anatomy in real/physical space. Similarly, any haptic objects, control objects, or other planned automated motions of robotic device <NUM> created during surgical planning that are linked to cuts, modifications, etc. to be made to that anatomy also move in correspondence with the patient's anatomy. In some embodiments, the surgical system <NUM> includes a clamp or brace to substantially immobilize the femur <NUM> to minimize the need to track and process motion of the femur <NUM>.

For embodiments where the robotic device <NUM> is a haptic device, the surgical system <NUM> is configured to constrain the surgical tool <NUM> based on relationships between HIPs and haptic objects. That is, when the processing circuit <NUM> uses data supplied by tracking system <NUM> to detect that a user is manipulating the surgical tool <NUM> to bring a HIP in virtual contact with a haptic object, the processing circuit <NUM> generates a control signal to the robotic arm <NUM> to provide haptic feedback (e.g., a force, a vibration) to the user to communicate a constraint on the movement of the surgical tool <NUM>. In general, the term "constrain," as used herein, is used to describe a tendency to restrict movement. However, the form of constraint imposed on surgical tool <NUM> depends on the form of the relevant haptic object. A haptic object may be formed in any desirable shape or configuration. As noted above, three exemplary embodiments include a line, plane, or three-dimensional volume. In one embodiment, the surgical tool <NUM> is constrained because a HIP of surgical tool <NUM> is restricted to movement along a linear haptic object. In another embodiment, the haptic object is a three-dimensional volume and the surgical tool <NUM> may be constrained by substantially preventing movement of the HIP outside of the volume enclosed by the walls of the three-dimensional haptic object. In another embodiment, the surgical tool <NUM> is constrained because a planar haptic object substantially prevents movement of the HIP outside of the plane and outside of the boundaries of the planar haptic object. For example, the processing circuit <NUM> can establish a planar haptic object corresponding to a planned planar distal cut of femur <NUM> in order to confine the surgical tool <NUM> substantially to the plane needed to carry out the planned distal cut.

For embodiments where the robotic device <NUM> is an autonomous device, the surgical system <NUM> is configured to autonomously move and operate the surgical tool <NUM> in accordance with the control object. For example, the control object may define areas relative to the femur <NUM> for which a cut should be made. In such a case, one or more motors, actuators, and/or other mechanisms of the robotic arm <NUM> and the surgical tool <NUM> are controllable to cause the surgical tool <NUM> to move and operate as necessary within the control object to make a planned cut, for example using tracking data from the tracking system <NUM> to allow for closed-loop control.

Referring now to <FIG>, a detailed block diagram of the processing circuit <NUM> is shown, according to an exemplary embodiment. The processing circuit <NUM> includes a processor <NUM> and a memory <NUM>. The processor <NUM> may be implemented as a general purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable electronic processing components. The memory <NUM> (e.g., memory, memory unit, storage device, etc.) includes one or more devices (e.g., RAM, ROM, Flash memory, hard disk storage, etc.) for storing data and/or computer code for completing or facilitating the various processes and functions described in the present application. The memory <NUM> may include volatile memory or non-volatile memory. The memory <NUM> may include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in the present application. According to an exemplary embodiment, the memory <NUM> is communicably connected to the processor <NUM> via the processing circuit <NUM> and includes computer code for executing (e.g., by the processing circuit <NUM> and/or processor <NUM>) one or more processes described herein.

As shown in <FIG>, the processing circuit <NUM> also includes a user interface circuit <NUM>, a segmentation circuit <NUM>, an attachment point identification circuit <NUM>, an implant placement circuit <NUM>, a surgical planning circuit <NUM>, an intraoperative control circuit <NUM>, and a communications interface <NUM>. The various circuits <NUM>-<NUM> are communicably coupled to one another, to the processor <NUM> and memory <NUM>, and to the communications interface <NUM>. Although shown in as a unified device in <FIG>, in some embodiments the processing circuit <NUM> and the elements thereof (i.e., the processor <NUM>, memory <NUM>, circuits <NUM>-<NUM>, and communications interface <NUM>) may be distributed among multiple computing devices, servers, robots, cloud resources, etc..

The communications interface <NUM> facilitates communication between the processing circuit <NUM> and the input/output device <NUM>, the tracking system <NUM>, and the robotic device <NUM> of <FIG>. The communications interface <NUM> also facilitates communication between the processing circuit <NUM> and a preoperative imaging system <NUM> or other system (e.g., electronic health record, patient information database) configured to provide the processing circuit <NUM> with preoperative medical imaging of the patient's anatomy. The communications interface <NUM> may include cryptographic and encryption capabilities to establish secure communication sessions to prevent or substantially mitigate cybersecurity risks and to comply with patient health record privacy laws and regulations.

The user interface circuit <NUM> is configured to generate various graphical user interfaces to provide to one or more users via input/output device <NUM> and to receive, parse, and interpret user input to the input/output device <NUM>. Example graphical user interfaces are shown in <FIG> and described in detail with reference thereto. The user interface circuit <NUM> is communicably coupled to the various circuits <NUM>-<NUM> to receive information for display in graphical user interfaces from the circuits <NUM>-<NUM> and to provide user input to the circuits <NUM>-<NUM>, as described in detail below.

The segmentation circuit <NUM> is configured to receive medical images from the preoperative imaging system <NUM>, segment the medical images, and generate a three-dimensional virtual bone model based on the medical images. In various embodiments, medical images may be captured using one or more of various imaging techniques, including computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, etc. In the embodiments described herein, the segmentation circuit <NUM> primarily receives and utilizes CT images, and the following description references CT images/imaging. However, it should be understood that in various other embodiments, the processing circuit <NUM> may utilize various other types of medical images in addition to or in alternative to CT images, for example magnetic resonance imaging (MRI), ultrasound, and/or x-ray, including three-dimensional reconstruction/modeling from two-dimensional x-ray/fluoroscopic images.

The CT images received by the segmentation circuit <NUM> capture a plurality of views of the femur <NUM> and/or tibia <NUM> of the patient. The plurality of views may be a series of slices, i.e., cross-sectional views at each of a plurality of positions along the patient's leg. Each CT image may therefore show a two-dimensional slice of the patient's leg at a given position. The positions and order of the CT images may be known.

The segmentation circuit <NUM> is configured to segment the CT images to distinguish bone (e.g., the femur <NUM> and tibia <NUM>) from surrounding tissue, fluid, etc. shown in the CT images. For example, the segmentation circuit <NUM> may determine a boundary of the bone shown in each CT image. In some embodiments, the segmentation circuit <NUM> automatically determines the boundary using automated image processing techniques (auto-segmentation). In other embodiments, the segmentation circuit <NUM> provides the CT images to the user interface circuit <NUM> for inclusion in a graphical user interface that prompts a user to input an indication of the boundary for each image. User input may be received that fully segments all images, or some combination of user input and auto-segmentation may be used. For example, a user may be prompted to check the accuracy of auto-segmentation and make adjustments as needed.

The segmentation circuit <NUM> is further configured to generate a virtual bone model (i.e., a three-dimensional model) based on the segmented CT images. In the embodiments shown, the segmentation circuit <NUM> generates a virtual bone model of the femur <NUM> and the tibia <NUM>. The segmentation circuit <NUM> may use the bone boundaries in each CT image slice defined during segmentation, stack the image slices to organize the boundaries in order and separated at known distances, and generate a surface that conforms to the boundaries. The segmentation circuit <NUM> may thereby generate a virtual bone model defined as a three-dimensional surface, a collection of voxels, or some other representation in a given coordinate system.

The attachment point identification circuit <NUM> receives the virtual bone model from the segmentation circuit <NUM> and identifies one or more soft tissue attachment points on the virtual bone model. A soft tissue attachment point is a representation in the coordinate system of the virtual bone model of a site on a bone where soft tissue attaches to the bone, for example a point or region where a ligament attaches to a bone. According to various embodiments and for various soft tissues and/or procedures, a soft tissue attachment point may be defined as a point, a line, a surface, a voxel or collection of voxels, or some other representation.

In the embodiment shown herein, the attachment point identification circuit <NUM> identifies a posterior cruciate ligament (PCL) attachment point that corresponds to the site where the patient's PCL attaches to the patient's tibia <NUM>. In some embodiments, the attachment point identification circuit <NUM> also identifies an anterior cruciate ligament (ACL) attachment point that corresponds to a site where the patient's ACL attaches to the patient's tibia <NUM>. The attachment point identification circuit <NUM> may also identify ligament attachment points corresponding to sites where the ACL and PCL attach to the femur <NUM>. Various other soft tissue attachment points may also be identified.

In some embodiments, the attachment point identification circuit <NUM> identifies soft tissue attachment points automatically. For example, the attachment point identification circuit <NUM> may determine extrema, inflection points, or other identifiable features on the surface of the virtual bone model. As another example, the attachment point identification circuit <NUM> operates a neural network trained via machine learning to identify soft tissue attachment points.

In some embodiments, the attachment point identification circuit <NUM> identifies soft tissue attachment points by instructing the user interface circuit <NUM> to generate a graphical user interface that prompts a user to select a soft tissue attachment point on the virtual bone model. The user interface circuit <NUM> may generate a graphical user interface that visualizes the virtual bone model and provides a tool for selecting a point or points on the virtual bone model using the input/output device <NUM>. The attachment point identification circuit <NUM> may receive the user input and define one or more soft tissue attachment points based on the user input.

The implant placement circuit <NUM> is configured to determine the size and placement of surgical implants based on the virtual bone model and the soft tissue attachment points. In some embodiments, the implant placement circuit <NUM> is configured to determine the placement of a tibial implant and a femoral implant for a total knee arthroscopy procedure based on the PCL attachment point on the tibia <NUM>. In such embodiments, the implant placement circuit <NUM> overlays a virtual tibial implant on the virtual bone model of the tibia <NUM> and a virtual femoral implant on the virtual bone model of the femur <NUM>. The implant placement circuit <NUM> positions the virtual tibial implant on the virtual bone model based on the PCL attachment point (e.g., to avoid interference with the PCL attachment point, to optimize rotation and coverage based on the PCL attachment point). <FIG> shows the use of imaging to identify the PCL attachment point on the bone model (frames A and B) and positioning a model of the implant on the bone model (frame C), and using the imaging to visually depict the relationship between the planned placement of the implant relative to the PCL (frame D) to determine whether impingement with the ligament might occur. A graphical user interface illustrating this alignment is shown in <FIG>.

In some embodiments, the implant placement circuit <NUM> is configured to predict the ACL and PCL line of action based on the PCL attachment points on the femur <NUM> and tibia <NUM> and the ACL attachment points on the PCL and ACL. That is, the implant placement circuit <NUM> is configured to generate a virtual ACL model and a virtual PCL model that predict the locations of the ACL and PCL between the tibia <NUM> and the femur <NUM>. The implant placement circuit <NUM> may then place the virtual tibial implant and the virtual femoral implant to avoid impingement (i.e., obstruction, pinching, restriction, etc.) of the virtual ACL model and the virtual PCL model by the virtual tibial implant and the virtual femoral implant through a full range of motion of the knee. The implant placement circuit <NUM> may thereby facilitate the prevention of impingement of the ACL or PCL by implant components.

In some embodiments, the user interface circuit <NUM> generates a graphical user interface showing the placement of the virtual implants overlaid on the virtual bone models, for example as shown in <FIG> and described in detail with reference thereto. The graphical user interface may allow a user to adjust the placement of the virtual implants. In such an embodiment, the implant placement circuit <NUM> may restrict the placement options available to the user to prevent the user from placing the virtual implants to interfere with an attachment point or to impinge the ACL or PCL. In other embodiments, the implant placement circuit <NUM> may generate an alert or warning (e.g., text message, audible alert) provided to input/output device <NUM> to inform the user of the interference or impingement while allowing the user to select such a placement.

The surgical planning circuit <NUM> receives the virtual bone model with virtual implants positioned thereon from the implant placement circuit <NUM>. The surgical planning circuit <NUM> is configured to plan the cuts to the femur <NUM> and tibia <NUM> needed to prepare the femur <NUM> and the tibia <NUM> to receive the implants in the positions determined by the implant placement circuit <NUM>. That is, the surgical planning circuit <NUM> determines how the femur <NUM> and the tibia <NUM> need to be modified such that femoral and tibial implants can placed on the femur <NUM> and tibia <NUM> in the same positions as the virtual femoral and tibial implants are positioned on the virtual bone models by the implant placement circuit <NUM>. The surgical planning circuit <NUM> may determine a surgical plan that includes a series of planned planar cuts to be made to the femur <NUM> and the tibia <NUM>.

The surgical planning circuit <NUM> may be configured to adjust the planned cuts based on one or more soft tissue attachment points. For example, the surgical planning circuit <NUM> may be configured to ensure that the planned cuts do not intersect a soft tissue attachment point, weaken a soft tissue attachment point, intersect soft tissue attached to a soft tissue attachment point, or pose a risk of harm to soft tissue in some other way. If such a cut is required to place an implant in a position determined by the implant placement circuit <NUM>, the surgical planning circuit <NUM> may send an error or warning message to the implant placement circuit <NUM> requesting that the implant placement circuit <NUM> revise the implant placement.

Based on the planned cuts, the surgical planning circuit <NUM> generates control objects (e.g., virtual haptic objects) for each of the planned cuts, which are based on the one or more soft tissue attachment points. Accordingly, the surgical planning circuit <NUM> uses the soft tissue attachment points to generate patient-specific control objects. Namely, the surgical planning circuit <NUM> may define one or more of the control objects to constrain the surgical tool from impacting one or more soft tissue attachment points. For example, a control object may correspond to a planar cut to be made to a distal surface of the tibia <NUM>. The surgical planning circuit <NUM> may shape the control object such that the control object does not intersect the tibial PCL attachment point. <FIG> show an illustration of such a control object, as described in detail below with reference thereto.

The intraoperative control circuit <NUM> is configured to facilitate implementation of the surgical plan generated by the surgical planning circuit <NUM>. The intraoperative control circuit <NUM> is communicable with the tracking system <NUM> and robotic device <NUM> to perform registration, navigation, and tracking, for example as described above with reference to <FIG>. The intraoperative control circuit <NUM> may register and track one or more soft tissue attachment points. The intraoperative control circuit <NUM> is also configured to control the robotic device <NUM> based on the control objects generated by the surgical planning circuit <NUM>. In embodiments where the robotic device <NUM> is a haptic device, the intraoperative control circuit <NUM> controls the robotic device <NUM> to confine the surgical tool <NUM> to the control objects, for example as described above with reference to <FIG>. In embodiments where the robotic device <NUM> is an autonomous or automated robotic device, the intraoperative control circuit <NUM> controls the robotic device <NUM> to move the surgical tool <NUM> within the control object to execute a cut or cuts, for example as described above with reference to <FIG>. The intraoperative control circuit <NUM> may thereby protect one or more soft tissue attachment points during the surgical procedure by controlling the robotic device <NUM> in accordance with the control objects generated by surgical planning circuit <NUM>.

Referring now to <FIG>, a flowchart of a process <NUM> for facilitating a joint arthroscopy procedure using the location of one or more soft tissue attachment points is shown, according to an exemplary embodiment. Process <NUM> can be executed by the surgical system <NUM> of <FIG> and the processing circuit <NUM> of <FIG>, and reference is made thereto in the following description. As described below, process <NUM> facilitates a total knee arthroscopy procedure. However, it should be understood that process <NUM> may be executed with various other systems and may be applicable to various other procedures, including partial knee arthroscopy, bicruciate retaining total knee arthroplasty, partial hip arthroscopy, and total hip arthroscopy, revision knee and hip procedures, among other procedures.

At step <NUM>, the processing circuit <NUM> receives computed tomography (CT) images of the anatomy of interest, for example the tibia <NUM> and femur <NUM> of the leg <NUM> of the patient <NUM>. The CT images may be captured by a preoperative imaging system <NUM> (e.g., a CT scanner) communicable with the processing circuit <NUM>. The CT images may include a collection of two-dimensional images showing cross-sectional slices of the leg <NUM> at various positions along the leg <NUM>.

At step <NUM>, the CT images are segmented to distinguish the various tissues and structures shown in the images. For example, the region corresponding to the tibia <NUM> or femur <NUM> in each CT image may be determined. A boundary line that outlines the tibia <NUM> or femur <NUM> in each CT image may be defined and stored. In some embodiments, the processing circuit <NUM> automatically segments the CT images to distinguish regions corresponding to the tibia <NUM> or femur <NUM> from the rest of the image (i.e., parts of the image showing soft tissue or other anatomical structures). In other embodiments, the processing circuit <NUM> generates a graphical user interface that allows a user to manually input an indication of the boundary of the tibia <NUM> or femur <NUM> in each CT image. In still other embodiments, some combination of auto-segmentation and user input is used to increase the efficiency and accuracy of the segmentation process. The processing circuit <NUM> thereby acquires segmented CT images that indicate the shape/boundary of the tibia <NUM> and/or femur <NUM> at various layers along the tibia <NUM>.

At step <NUM>, the processing circuit <NUM> generates a virtual bone model of tibia <NUM> and/or femur <NUM> based on the segmented CT images. That is, the boundary of the tibia <NUM> and/or femur <NUM> defined in each segmented CT image is stacked, with the separation between each CT image known. The stack of images may then be processed to generate a three-dimensional virtual bone model representing the tibia <NUM> and/or femur <NUM> (e.g., a virtual tibia model and a virtual femur model).

At step <NUM>, one or more soft tissue attachment points are identified on the virtual bone model. That is, one or more sites where soft tissue attaches to the tibia <NUM> or femur <NUM> are determined from the virtual bone model and/or the CT images. The coordinates of the sites in the coordinate system of the virtual bone model are determined and defined as soft tissue attachment points. For example, the PCL attachment point corresponding to the site where the PCL attaches to the tibia <NUM> may be identified and defined in this manner. In some embodiments, the processing circuit <NUM> generates a graphical user interface that allows the user to indicate or adjust the position of one or more soft tissue attachment points. An example of such a graphical user interface is shown in <FIG> and described in detail with reference thereto. In some embodiments, the processing circuit <NUM> is configured to automatically identify one or more soft tissue attachment points.

In some embodiments, soft tissue attachment points are identified using bone density information visible in the CT images. For example, a site on a bone that attaches to soft tissue may have a higher density than other regions of the bone. These high-density areas may be distinguishable from a CT image, for example appearing more opaque or brighter in the CT image. The processing circuit <NUM> may use an image recognition technique or auto-segmentation approach to identify one or more areas in the CT images associated with high bone density and associate those areas with soft tissue attachment points. The processing circuit <NUM> may thereby automatically identify soft tissue attachment points based on bone density information captured by CT imaging.

At step <NUM>, implant size and placement are determined based on the identified soft tissue attachment points. For example, the processing circuit <NUM> may generate a virtual implant model of a tibial implant and a femoral implant. The processing circuit <NUM> may then overlay the virtual implant model on the virtual bone model to assess sizing, positioning, orientation, alignment, etc. An example of a graphical user interface showing a virtual implant model overlaid on the virtual bone model is shown in <FIG> and described in detail with reference thereto.

The processing circuit <NUM> may ensure that the implant model does not interfere with the one or more soft tissue attachment points by covering the attachment point, requiring removal or weakening of the attachment points, or causing impingement of a tendon by the implant. As one example, the processing circuit <NUM> may determine the size, position, and/or rotation of a tibial component of a knee implant based on the PCL attachment point on the tibia <NUM>.

In some embodiments, the processing circuit <NUM> determines a rotation or orientation of the tibial component based attachment points corresponding to the PCL and the patellar ligament. More particularly, a line connecting the PCL attachment point and the patellar ligament attachment point may be used to set tibial component rotation. In some embodiments, one or more soft tissue attachment points are used as landmarks around which rotation may be defined or manipulated.

At step <NUM>, the processing circuit <NUM> generates a patient-specific control object based on the implant size and placement determined at step <NUM> and based on one or more soft tissue attachment points. The control objects are configured to facilitate cuts or other modifications to the femur <NUM> and tibia <NUM> by the surgical tool <NUM> to prepare the femur <NUM> and tibia <NUM> to receive the femoral and tibial implants in the positions determined at step <NUM>. The control object may be shaped and position to avoid intersection with or other interference with the soft tissue attachment points.

At step <NUM>, the surgical tool <NUM> is control and/or constrained based on the control object. In embodiments where the robotic device <NUM> is an autonomous robotic system, the surgical tool <NUM> controlled to move autonomously through one or more control objects to modify the femur <NUM> and/or the tibia <NUM>. Because the control object is shaped to avoid one or more soft tissue attachment points on the virtual bone model, the robotic device is controlled such that it does not contact the corresponding attachment sites on the real femur <NUM> or tibia <NUM> or the ligament or other tissue attached to said attachment sites. In embodiments where the robotic device <NUM> is a haptic device, the processing circuit <NUM> controls the robotic device <NUM> to constrain the surgical tool <NUM> within the control object. The surgical tool <NUM> is thereby constrained from contacting one or more attachment sites the ligament or other tissue attached to said attachment sites. A series of surgical cuts may thereby be carried out while protecting ligaments or other soft tissue from iatrogenic damage and limiting the risks of complications associated with weakened attachment points.

Referring now to <FIG>, a process <NUM> for preventing impingement of a patient's ACL and/or PCL by implant components is shown, according to an exemplary embodiment. The process <NUM> may be carried out by the surgical system <NUM> of <FIG> with the processing circuit <NUM> shown in <FIG>. Although described herein in an application for total knee arthroplasty, process <NUM> may also be used for partial knee arthroplasty, early intervention knee surgeries, etc. Process <NUM> may be particularly well suited for bicruciate-retaining knee arthroplasty procedures. Process <NUM> may be an example of step <NUM> of process <NUM> shown in <FIG>.

At step <NUM>, the processing circuit <NUM> predicts the ACL/PCL line of action based on CT images. Based on the CT images, the virtual bone model, and/or identified ACL and PCL attachment points on the femur <NUM> and tibia <NUM>, the processing circuit <NUM> predicts the space through which the ACL and PCL extend through a full range of motion of the knee. For example, the processing circuit <NUM> may generate a model of the ACL and/or PCL to add to the virtual bone model.

At step <NUM>, the processing circuit <NUM> determines potential impingements of the ACL or PCL by the implant. The processing circuit <NUM> may generate virtual implant models and overlay the virtual implant models on the virtual bone model as described above with reference to <FIG>. The ACL/PCL model may also be included. The processing circuit <NUM> may thereby obtain a virtual model that includes a virtual bone model, virtual implant model, and virtual ligament model. The processing circuit <NUM> may test the virtual model through a full range of motion and detect any overlap between the virtual implant model and the virtual ligament model indicative of potential impingements. Overlap between the virtual implant model and the virtual ligament model indicates that impingement is likely if the implant is installed as represented in the model.

At step <NUM>, the processing circuit <NUM> chooses the implant size and position based on potential impingements. That is, the processing circuit <NUM> chooses the implant size and position to eliminate potential impingements or minimize the risk of impingement. For example, the processing circuit <NUM> may alter the implant size and position such that the virtual model may be tested through a full range of motion without creating any overlap between the virtual implant model and the virtual ligament model. The processing circuit <NUM> may use imaging, as described above with respect to <FIG>, to determine the proper size and position of the implant to avoid impingement. In embodiments where a graphical user interface is provided that allows a user to alter the planned implant size and position, the processing circuit <NUM> may generate a warning or alert that indicates to a user that a proposed planned implant position is predicted to cause impingement. The processing circuit <NUM> may also prevent the user from selecting an option in which an impingement is predicted or for which the risk of impingement exceeds a threshold.

At step <NUM>, the processing circuit <NUM> validates the virtual model and the impingement predictions using MRI images. The processing circuit <NUM> may receive MRI images from a preoperative imaging system <NUM>. The MRI images may be segmented to distinguish the femur, tibia, patella, cartilage, and ligaments (e.g., ACL, PCL, patellar ligament) in the knee. One or more three-dimensional models of the bones, ligaments, and cartilage may be generated, for example using a similar approach as described for CT images above. Virtual models of the planned implants may be positioned in the MRI-based three-dimensional models. These models may then be used to validate that the CT-based models correctly predicted impingement or non-impingement. For example, if the MRI-based model predicts non-impingement and the CT-based model predicts non-impingement, then the MRI-based model validates the CT-based model and the planned position of the implant may be approved. The process may continue with the generation of patient-specific control objects based on the implant size and position selected to prevent potential soft tissue impingements, as described above with respect to steps <NUM> and <NUM> of process <NUM>.

Referring now to <FIG>, various views of a graphical user interface <NUM> generated by the processing circuit <NUM> (e.g., by the user interface circuit <NUM>) and displayed by the input/output device <NUM> (i.e., shown on display <NUM>) are shown, according to exemplary embodiments. In each of <FIG>, the graphical user interface <NUM> includes an upper row <NUM> showing visualizations of a virtual bone model of the femur <NUM> (virtual femur model <NUM>) and a lower row <NUM> showing visualizations of a virtual bone model of the tibia <NUM> (virtual tibia model <NUM>). The graphical user interface <NUM> also includes three columns corresponding to three pairs of visualizations of the virtual femur model <NUM> and the virtual tibia model <NUM>. The first column <NUM> shows front views of the virtual femur model <NUM> and virtual tibia model <NUM>, the second column <NUM> shows distal views of the virtual femur model <NUM> and virtual tibia model <NUM>, and the third column <NUM> shows side views of the virtual femur model <NUM> and virtual tibia model <NUM>. In addition, <FIG>, <FIG>, <FIG>, and <FIG> include visualizations of a virtual femoral implant <NUM> and visualizations of a virtual tibial implant <NUM>. The graphical user interface <NUM> thereby displays a planned implant placement to a user.

In the configuration shown in <FIG>, the graphical user interface <NUM> shows cross-sectional views of the virtual femur model <NUM>, the virtual femoral implant <NUM>, the virtual tibia model <NUM>, and the virtual tibial implant <NUM>. A circle <NUM> encircles (e.g., is centered on) the PCL attachment point on the virtual tibia model <NUM>. In some embodiments, the position of the circle <NUM> is adjustable by a user to alter the definition of the position of the PCL attachment point. A dashed line <NUM> indicates a height or plane associated with the PCL attachment point. In some embodiments, the dashed line <NUM> is adjustable by a user to move the position of the PCL attachment point. As shown in <FIG>, the virtual tibial implant <NUM> is positioned to not interfere with the PCL attachment point.

In the configuration shown in <FIG>, the graphical user interface <NUM> shows three-dimensional renderings of the virtual femur model <NUM>, the virtual femoral implant <NUM>, the virtual tibia model <NUM>, and the virtual tibial implant <NUM>. The virtual femur model <NUM> and the virtual tibia model <NUM> are modified to show the effects of the planned cuts to femur <NUM> and tibia <NUM>. That is, portions of the femur <NUM> and tibia <NUM> to be removed during surgery have also been removed from the virtual femur model <NUM> and the virtual tibia model <NUM>. A user may then check whether the planned cuts will alter, damage, intersect, interfere, or otherwise affect one or more soft tissue attachment points. The graphical user interface <NUM> includes arrow buttons <NUM> that allow a user to adjust the position, size, rotation, etc. of the virtual femoral implant <NUM> and the virtual tibial implant <NUM>.

In the configuration shown in <FIG>, the graphical user interface <NUM> shows cross-sectional views of the virtual femur model <NUM>, the virtual femoral implant <NUM>, the virtual tibia model <NUM>, and the virtual tibial implant <NUM>. In <FIG>, a virtual boundary <NUM> illustrates a boundary of a control object. The virtual boundary <NUM> defines a boundary that the surgical tool <NUM> is confined from crossing by the control object and the robotic device <NUM>. As shown in <FIG>, the virtual boundary <NUM> includes a concave notch <NUM> where the virtual boundary <NUM> curves around the PCL attachment point indicated by highlighting <NUM>. The virtual boundary <NUM> thereby indicates that the control object is configured to confine the surgical tool <NUM> from reaching the PCL attachment point.

In the configuration shown in <FIG>, the graphical user interface <NUM> shows cross-sectional views of the virtual femur model <NUM> and the virtual tibia model <NUM> with the virtual boundary <NUM> corresponding to a planned tibial cut. <FIG> shows a view for right knee while <FIG> shows a view for a left knee. As shown in <FIG>, the virtual femur model <NUM> and the virtual tibia model <NUM> may be visualized to include CT images and/or other medical images that show the bone density in various areas of the patient's femur and tibia. This may be useful for a user in identifying one or more soft tissue attachment points, identifying strong areas of bone suitable for engagement with an implant, and/or for other planning or diagnostic purposes. As shown in <FIG>, the virtual femoral implant <NUM> and the virtual tibial implant <NUM> are hidden from the graphical user interface <NUM>, presenting a simplified view that allows a user to clearly view a planned cut facilitated by the virtual boundary <NUM>.

As shown in <FIG>, bone regions having a higher density may be shown as color-coded regions, shaded regions, or regions indicated by other demarcation on the graphical user interface <NUM>. In the example shown, demarcation <NUM> indicates a bone dense region. Point <NUM> indicates the centroid of the bone dense region. Region <NUM> corresponds to the PCL and/or the PCL attachment point. The demarcation <NUM>, the point <NUM>, and/or the region <NUM> may facilitate implant planning. For example, a surgeon (user) could align the sulcus of the virtual tibial implant <NUM> to the point <NUM> (as shown in <FIG>) to optimize internal/external rotation. As another example, a surgeon could align the varus/valgus rotation of the tibial resection to optimize the density of the cut plane. Various further alignments and planning advantageous may be facilitated by including the demarcation <NUM>, the point <NUM>, and/or the region <NUM> on the graphical user interface <NUM>.

In the configuration shown in <FIG>, the graphical user interface <NUM> shows three-dimensional renderings of the virtual femur model <NUM>, the virtual femoral implant <NUM>, the virtual tibia model <NUM>, and the virtual tibial implant <NUM>. <FIG> also shows the virtual boundary <NUM>. In the embodiment shown, the control object is planar control object, oriented such that virtual boundary <NUM> is only viewable from the distal view of the second column <NUM>. The graphical user interface <NUM> includes arrow buttons <NUM> that allow a user to adjust the position, size, rotation, etc. of the virtual femoral implant <NUM> and the virtual tibial implant <NUM>. In response to a user input to adjust the position, size, rotation, etc. of the virtual tibial implant <NUM>, the processing circuit <NUM> adjusts the control object accordingly. The virtual boundary <NUM> on the graphical user interface <NUM> is updated as well. Thus, the user may adjust the virtual boundary <NUM> as desired by adjusting the position, size, rotation, etc. of the virtual tibial implant <NUM>.

Referring now to <FIG>, a process <NUM> for determining a rotational alignment of an implant based on the attachment of the patellar ligament to the tibia is illustrated, according to an exemplary embodiment. <FIG> shows a flowchart of process <NUM>. <FIG> show illustrations useful in explanation of process <NUM>. Process <NUM> can be carried out by the processing circuit <NUM> of <FIG>. Process <NUM> may be included as part of an embodiment of process <NUM> of <FIG>, for example with steps <NUM>-<NUM> of <FIG>.

At step <NUM>, the tibial and the patellar ligament are identified from CT images to identify a patellar ligament attachment point (tibial tubercle). That is, the CT images are segmented to define pixels or coordinates on the CT images corresponding to the tibial tubercle and/or the patellar ligament. Segmenting the image to identify the patellar ligament and the tibia facilitates identification of a region where the patellar ligament attaches to the tibia (e.g., where the segmented areas abut one another). In some embodiments, the processing circuit <NUM> automatically identifies the tibial tubercle and/or the patellar ligament in the CT images (i.e., auto-segmentation). In other embodiments, the processing circuit <NUM> generates a graphical user interface that prompts a user to input an indication of a location (e.g., outline, area) of the patellar ligament and/or the tibial tubercle. The processing circuit <NUM> may receive the user input and identify the location of the tibial tubercle and/or the patellar ligament based on the user input. For example, <FIG> shows a segmentation border <NUM> identified as the outside border of the patellar ligament at the tibial tubercle.

At step <NUM>, the medial edge of the patellar ligament at the tibial tubercle is identified. In other words, the processing circuit <NUM> determines the furthest-medial point of the patellar ligament at the tibial tubercle where the patellar ligament attaches to the tibia (i.e., medial extremum point <NUM> in <FIG>). To identify this point, a CT image 'slice' at the level of the tibial tubercle is chosen that shows attachment between the patellar ligament and the tibia (e.g., CT image <NUM> of <FIG>). At that level, the furthest medial point of the tibial tubercle and/or the patellar ligament is determined based on segmentation data from step <NUM>, for example by selecting, from the segmented region corresponding to the patellar ligament, the point closest to the medial border of the CT image. The processing circuit <NUM> thereby determines coordinates associated with the medial edge of the tibial tubercle and/or patellar ligament.

At step <NUM>, the rotation of a tibial component of a prosthetic implant is determined by aligning an axis of the tibial component with the medial edge of the tibial tubercle and/or patellar ligament (i.e., with the medial extremum of the attachment region of the patellar ligament on the tibia). The processing circuit <NUM> generates a virtual implant model of the tibial component (e.g., virtual tibial implant <NUM>) to align relative to a virtual bone model (e.g., as generated at step <NUM> of process <NUM>). In some visualizations of the virtual implant model, for example as shown in <FIG>, the virtual implant model may include a representation <NUM> of two or more axes that define one or more rotations of the virtual implant model. More particularly, representation <NUM> illustrates the rotational alignment of the virtual tibial implant overlaid on a CT image <NUM> of the tibia <NUM>.

As shown in <FIG>, a first axis <NUM> of representation <NUM> may extend substantially side-to-side (i.e., medial to lateral) while a second axis <NUM> may point perpendicular to the first axis <NUM> in the plane of the CT image (i.e., a plane defined by a normal vector substantially parallel with a length of the tibia). The rotational alignment of the virtual implant model may be adjusted by rotating the first axis <NUM> and the second axis <NUM> about an intersection point <NUM> of the first axis <NUM> and the second axis <NUM> in the plane of the CT image.

In step <NUM>, the processing circuit <NUM> defines the rotation of the virtual tibia model such that the second axis <NUM> intersects the medial edge of the patellar ligament at the attachment point/region between the patellar ligament and the tibia. As shown in <FIG>, the second axis <NUM> extends through the medial extremum point <NUM> of a segmentation border <NUM> corresponding to the patellar ligament. In other words, the rotation of the virtual tibial implant is set to ensure that the second axis <NUM> shown in <FIG> passes through the medial extremum coordinates identified at step <NUM>.

At step <NUM>, the rotational alignment of the virtual tibial implant is used to determine the rotational alignment of a virtual model of the femoral component of an implant (e.g., virtual femoral implant <NUM>). For example, the rotational alignment of the virtual tibial implant and the rotational alignment of the virtual femoral implant may have a preset geometric relationship that may be used by the processing circuit <NUM> to determine the rotational alignment of the virtual femoral implant based on the rotational alignment of the virtual tibial implant. <FIG> shows a representation <NUM> of the alignment of the virtual femoral implant on the femur (i.e., overlaid on a CT image <NUM> of the femur). As shown in <FIG>, the representation <NUM> of the alignment of the virtual femoral implant matches the representation <NUM> of the alignment of the virtual tibial implant.

The processing circuit <NUM> thereby determines the rotational alignment of the implant based on the attachment point of the patellar ligament to the tibia.

As mentioned above, process <NUM> may be implemented as a sub-part of process <NUM>. Accordingly, after step <NUM> the process circuit <NUM> may determine other sizing or placement characteristics of the implant (i.e., at step <NUM>), generate a control object based on the determined implant placement and the attachment point of the patellar ligament to the tibia (i.e., at step <NUM>), and constrain or control the surgical tool <NUM> based on the control object (i.e., at step <NUM>).

In some embodiments, process <NUM> further includes a step of using sulcus density in determining tibial baseplate orientation in the axial, coronal, and sagittal planes to achieve neutral implant alignment. In some embodiments, coronal and sagittal density profiles may be used to determine good bone stock for optimal implant fixation.

Referring now to <FIG>, a graphical user interface <NUM> is shown, according to an exemplary embodiment. The graphical user interface <NUM> may be generated by the processing circuit <NUM> (e.g., by the user interface circuit <NUM>) and displayed by the input/output device <NUM> (i.e., shown on display <NUM>). In some embodiments, the graphical user interface <NUM> corresponds to a portion of graphical user interface <NUM>, for example the portion found at the second column <NUM> and the lower row <NUM> in <FIG>.

In the embodiment of <FIG>, a tool visualization <NUM> of the surgical tool <NUM> is overlaid on a cross-sectional end view of the virtual tibia model <NUM> (e.g., on a tibia shown by a CT image). The position of the tool visualization <NUM> may be updated in real-time to show the real position of the surgical tool <NUM> relative to the real tibia <NUM>, for example based on tracking/navigation data generated by the tracking system <NUM>. The graphical user interface <NUM> also includes the virtual boundary <NUM> and an indicator <NUM> that highlights the location of the PCL attachment point. A user (e.g., a surgeon) may thereby view the relative position of the surgical tool <NUM>, the virtual boundary <NUM>, and the attachment point indicated by indicator <NUM> on the graphical user interface <NUM> during the surgical operation. The user may thereby be assisted in properly positioning and/or orienting a cut made using the surgical tool <NUM> relative to the attachment point.

While the present disclosure is focused on the PCL and ACL, the systems and methods described herein may be adapted for identifying and protecting other soft tissue such as the medial collateral ligament (MCL). Various such possibilities are contemplated by the present disclosure.

As used herein, the term "circuit" may include hardware structured to execute the functions described herein. In some embodiments, each respective "circuit" may include machine-readable media for configuring the hardware to execute the functions described herein. The circuit may be embodied as one or more circuitry components including, but not limited to, processing circuitry, network interfaces, peripheral devices, input devices, output devices, sensors, etc. In some embodiments, a circuit may take the form of one or more analog circuits, electronic circuits (e.g., integrated circuits (IC), discrete circuits, system on a chip (SOCs) circuits, etc.), telecommunication circuits, hybrid circuits, and any other type of "circuit. " In this regard, the "circuit" may include any type of component for accomplishing or facilitating achievement of the operations described herein. For example, a circuit as described herein may include one or more transistors, logic gates (e.g., NAND, AND, NOR, OR, XOR, NOT, XNOR, etc.), resistors, multiplexers, registers, capacitors, inductors, diodes, wiring, and so on).

The "circuit" may also include one or more processors communicably coupled to one or more memory or memory devices. In this regard, the one or more processors may execute instructions stored in the memory or may execute instructions otherwise accessible to the one or more processors. In some embodiments, the one or more processors may be embodied in various ways. The one or more processors may be constructed in a manner sufficient to perform at least the operations described herein. In some embodiments, the one or more processors may be shared by multiple circuits (e.g., circuit A and circuit B may comprise or otherwise share the same processor which, in some example embodiments, may execute instructions stored, or otherwise accessed, via different areas of memory). Alternatively or additionally, the one or more processors may be structured to perform or otherwise execute certain operations independent of one or more co-processors. In other example embodiments, two or more processors may be coupled via a bus to enable independent, parallel, pipelined, or multi-threaded instruction execution. Each processor may be implemented as one or more general-purpose processors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), digital signal processors (DSPs), or other suitable electronic data processing components structured to execute instructions provided by memory. The one or more processors may take the form of a single core processor, multi-core processor (e.g., a dual core processor, triple core processor, quad core processor, etc.), microprocessor, etc. In some embodiments, the one or more processors may be external to the apparatus, for example the one or more processors may be a remote processor (e.g., a cloud based processor). Alternatively or additionally, the one or more processors may be internal and/or local to the apparatus. In this regard, a given circuit or components thereof may be disposed locally (e.g., as part of a local server, a local computing system, etc.) or remotely (e.g., as part of a remote server such as a cloud based server). To that end, a "circuit" as described herein may include components that are distributed across one or more locations.

Claim 1:
A surgical system (<NUM>), comprising:
a robotic device (<NUM>):
a surgical tool (<NUM>) mounted on the robotic device (<NUM>); and
a processing circuit (<NUM>) programmed to:
receive image data of a bone;
generate a virtual bone model based on the image data;
enable planning of a placement of an implant relative to the bone based on bone densities of a plurality of regions of the bone by generating, for display on a graphical user interface, a demarcation (<NUM>) or a centroid of a first region of the plurality of regions based on the first region having a higher bone density than a second region of the plurality of regions of the bone;
generate a control object based on the placement of the implant; and
control the robotic device (<NUM>) to confine the surgical tool (<NUM>) to the control object.