Patent Description:
The present disclosure involves a computer-implemented method of generating a three-dimensional (3D) bone model of a patient leg and foot and modifying the 3D bone.

Total ankle replacement ("TAR") procedures involve replacement of the ankle joint with an artificial implant that is designed to treat a particular condition, such as arthritis or fracture of a bone forming the joint. A conventional TAR procedure may include scanning the damaged foot and leg of the patient with medical imaging machine (e.g., CT machine, MRI machine) while the patient is in a supine position. The individual bones in each of the scans or images of the foot and leg are then segmented. A three-dimensional ("3D") bone model of the bones is generated from the segmented images, and then the surgeon may plan the surgical procedure using the patient specific 3D bone models. Surgical planning may include determining implant size and position, resection depths and positions relative to the bones, and surgical approaches, among other parameters. Once planning is complete, the surgery is then performed according to the plan.

One particular error-factor in TAR procedures is valid ankle pose estimation during the surgical planning steps of the procedure given the image scans forming the basis of the 3D bone models are not performed underweight bearing conditions. More particularly, the image scans performed on a non-standing, supine patient depict the bones of the foot and leg (e.g., tibia, fibula, talus, calcaneus) in an un-weighted state or condition. That is, the weight of the patient body is not acting on the bones of the leg and foot during the imaging scans. Thus, the 3D models of the bones of the leg and foot are modeled as if the bones are un-weighted. In this way, any surgical planning that takes place based on 3D models does not take into account a standing or weighted position of the bones relative to each other or relative to the floor. This can result in less than desirable surgical outcomes.

Accordingly, there is a need in the art for system and methods that address these shortcomings, among others.

<CIT> discloses a user-guided shape morphing in bone segmentation for medical imaging.

<CIT> discloses a method for generating a graphical 3D computer model of anatomical structures in a selectable pre-, intra-, or postoperative status that includes: (A) receiving a preoperative first medical 3D image data set of anatomical structures to be treated of a patient; (B) generating a first graphical 3D computer model of the anatomical structures to be treated in the form of a digital data set using the data received in step (A); (C) receiving a second medical 2D or 3D image data set of the anatomical structures to be treated; (D) generating a second graphical 2D or 3D computer model of the anatomical structures to be treated in the form of a digital data set using the data received in step (C); and (E) carrying out an image registration process of the first graphical 3D computer model using the second graphical 2D or 3D computer model. <CIT> discloses a method for superimposing digitalized representations and reference marker device.

Further embodiments are provided in the dependent claims.

Aspects of the present disclosure are directed to improving TAR procedures and planning for the same by providing methods of mapping weight bearing conditions of a foot from standing X-ray images to bone models generated from medical images (e.g., computed tomography ("CT") images, magnetic resonance images ("MRI"), among others) of a supine patient.

In certain instances, the method may include the following steps: (<NUM>) surface projection of 3D bone model to form 2D image of bone model: project the surface of the 3D bone model (generated from non-standing CT images) in lateral and anteroposterior views to form 2D images of bone model; (<NUM>) contour extraction of X-ray images: segment the object region in the X-ray images in lateral and anteroposterior views as defined by the bone boundary; (<NUM>) shape match (<NUM>) and (<NUM>): register or map the 2D images of the bone models with the segmented X-ray images in one or both of the lateral and anteroposterior views; (<NUM>) pose update: use the point correspondences from the shape matching step (<NUM>) to update pose of the 2D images of the bone model; (<NUM>) iterate: repeat steps (<NUM>) to (<NUM>) until convergence.

According to an aspect of the disclosure, there is provided a computer-implemented method comprising: receiving first patient bone data of a patient leg and foot in a first pose, the first patient bone data generated via a first imaging modality, the first pose comprising a position and orientation of the patient leg relative to the patient foot as defined in the first patient bone data, the first pose comprising a non-weighted condition of the patient leg and the patient foot; receiving second patient bone data of the patient leg and foot in a second pose, the second patient bone data generated via a second imaging modality that is different from the first imaging modality, the second pose comprising a position and orientation of the patient leg relative to the patient foot as defined in the second patient bone data, the second pose comprising a weighted condition of the patient leg and the patient foot; generating a three-dimensional (3D) bone model of the patient leg and foot from the first patient bone data, the 3D bone model comprising a plurality of 3D bone models arranged in the first pose, wherein the 3D bone models represent individual bones of the patient leg and foot; and modifying the 3D bone model of the patient leg and foot such that the plurality of 3D bone models are reoriented into a third pose that matches a particular arrangement of bones in the weighted condition of the patient leg and relative to the patient foot in the second pose.

In certain instances, the first imaging modality may be computed tomography.

In certain instances, the second imaging modality may be X-ray.

In certain instances, the first pose may include a non-standing position and orientation of the patient leg relative to the patient foot.

In certain instances, the second pose may include a standing position and orientation of the patient leg relative to the patient foot.

In certain instances, the modifying the 3D bone model may include causing first bone contour lines of the plurality of 3D bone models to align with second bone contour lines of the second patient bone data.

In certain instances, the one or more tangible computer-readable storage media may further include importing the 3D bone model and the second patient bone data into a common coordinate system.

In certain instances, the second patient bone data may include a lateral X-ray image of the patient leg and foot in the second pose, a medial X-ray image of the patient leg and foot in the second pose, and an anteroposterior X-ray image of the patient leg and foot in the second pose.

In certain instances, the modifying the 3D bone model may include aligning the plurality of 3D bone models with corresponding bones of the patient leg and foot in the second patient bone data, wherein the aligning may be done in lateral, medial, and anteroposterior views of the plurality of 3D bone models so as to match the orientation of the patient leg and foot in the lateral X-ray image and the anteroposterior X-ray image.

In certain instances not falling under the wording of the claims, the modifying the 3D bone model of the patient leg and foot may be performed manually.

In certain instances, the modifying the 3D bone model of the patient leg and foot may be performed automatically.

In certain instances, the modifying the 3D bone model of the patient leg and foot may be performed automatically by positionally matching landmarks in the plurality of 3D bone models and the second patient bone data.

In certain instances, the first patient bone data and the second patient bone data are the results of two different imaging events.

In certain instances, the second imaging modality may be X-ray, and the second patient bone data may include X-ray images, the computer process further may include: segmenting bones of the patient leg and foot in the X-ray images; and generating bone contour lines along a perimeter of at least some of the bones in the X-ray images.

In certain instances, the one or more tangible computer-readable storage media may further include: generating a plurality of poses for each of the plurality of 3D bone models; generating a plurality of two-dimensional (2D) projections from the plurality of poses for each of the plurality of 3D bone models; and comparing the bone contour lines to the plurality of 2D projections, and identifying particular 2D projections from the plurality of 2D projections that most closely match the bone contour lines.

In certain instances, the one or more tangible computer-readable storage media may further include: arranging the plurality of 3D bone models according to particular orientations of the particular 2D projections associated with each of the bones.

In certain instances, the one or more tangible computer-readable storage media may further include: preoperatively planning a total ankle replacement procedure using the plurality of 3D bone models being reoriented into the third pose.

In certain instances, the one or more tangible computer-related storage media may further include: limiting a number of the plurality of poses that are generated to only such poses that are permissible given bio-kinematics of the bones making up the plurality of 3D bone models.

Aspects of the present disclosure which do not fall within the scope of the claims may involve a system for processing patient data. In certain instances, the system may include: a network interface configured to receive one or more sets of patient data; a processing device in communication with the network interface; and a computer-readable medium in communication with the processing device configured to store information and instructions that, when executed by the processing device, performs the operations of: receiving first patient data may include at least one two-dimensional (2D) image of a patient leg and foot in a weighted pose; receiving second patient data may include computed tomography (CT) images of the patient leg and foot in a non-weighted pose, the first patient data and the second patient data being the result of separate imaging events; generating a three-dimensional (3D) bone model of the patient leg and foot from the CT images, the 3D bone model may include a plurality of 3D bone models representing individual bones of the patient leg and foot; and rearranging the plurality of 3D bone models to mimic the weighted pose of the patient leg and foot in the at least one 2D image.

In certain instances, the system may further include: generating a plurality of 2D projections of poses of the plurality of 3D bone models; comparing the plurality of 2D projections to contour lines outlining perimeters of bones of the patient leg and foot in the at least one 2D image; and identifying particular 2D projections from the plurality of 2D projections that best-fit a shape and size of the contour lines.

Example embodiments are illustrated in referenced figures of the drawings. It is intended that the embodiments and figures disclosed herein are to be considered illustrative rather than limiting.

Aspects of the present disclosure involve mapping weight bearing conditions of the foot and leg from X-ray images obtained with a standing patient to the 3D bone model of the foot and leg obtained from CT images of the patient in a non-standing, supine position. Such mapping is beneficial in aligning the foot along its natural or weighted position with respect to the tibia.

And while the disclosure describes mapping weight bearing conditions of the foot from standing images to 3D bone models of the foot obtained from non-standing images, although not within the scope the claims, the disclosure also encompasses mapping weight bearing conditions of other bones and joints of the body including hips and knees, among other joints and bones making up the joints without limitation. For example, standing X-ray images of a patient's hip region or knee region may be acquired, as well as non-weight bearing images of the patient's hip region or knee region, respectively. 3D bone models may be generated of the patient's hip region or knee region, and the pose of the bones of the 3D bone models may be modified based on the poses of the bones in the standing X-ray images. In the case of mapping weight bearing conditions of an X-ray to a 3D bone model of a patient's hip, the standing and non-standing images may show different relationships between the ilium and the femur and tibia. Similarly, in the case of mapping weight bearing conditions of an X-ray to a 3D bone model of a patient's knee, the standing and non-standing images may show different relationships between the femur and tibia.

The following discussion includes three methods of positionally modifying the 3D bone models generated from CT images based on information from weighted pose of the foot and leg bones in the X-ray images. The three methods are: pose estimation via comparison of 2d image and 3d bone model in common coordinate system; pose estimation via 2d comparison of x-ray and plurality of bone model projections; and augmented pose estimation.

The manual pose estimation method is where the 3D bone models of a patient's leg and foot bones (e.g., tibia, fibula, talus, calcaneus) obtained from segmenting the foot and leg from the CT images are imported into a 3D coordinate system of a computer along with X-ray images of the same patient's leg and foot. As discussed previously, however, the X-ray images depict a standing pose or orientation of the bones of the foot and leg. In certain instances, the 3D bone model in a first pose may be overlaid or superimposed on top of the X-ray image, which depict the bones in a different (standing) pose. Since the X-ray images are lateral views and anteroposterior views, the 3D bone models may be interchangeably shown in lateral views and anteroposterior views to match the X-ray images in the same lateral views and anteroposterior views, respectively. And the orientation of the individual bones of the bone model may be altered to match the pose of the bones in the standing X-ray images.

To begin, reference is made to <FIG>, which is a flowchart listing steps of an exemplary method <NUM> of pose estimation of three-dimensional bone models in surgical planning a total ankle replacement procedure. As seen in the figure, step <NUM> of the method <NUM> includes generating images (in the case of sequential images) or an image dataset (in the case of a continuous volumetric dataset produced, for example, via a helical CT scan) of a patient's foot in a non-weight bearing condition.

Corresponding to step <NUM> of <FIG>, reference is made to <FIG>, which is an overhead view of a patient <NUM> laying supine (i.e., on his or her back) on an imaging table <NUM> proximate an image scanning machine <NUM> (e.g., computed tomography (CT), magnetic resonance imaging (MRI), ultrasound) in communication with a computer <NUM>. As seen in the figure, the imaging table <NUM> may be a motorized platform that is aligned with an opening <NUM> in a ring <NUM> of the image scanning machine <NUM>. The imaging table <NUM> may be translated so a portion of the table <NUM> supporting the patient <NUM> extends into the opening <NUM> of the ring <NUM> of the machine <NUM>. For example, in the case of an imaging procedure on the patient's leg and foot <NUM>, the table <NUM> may be translated until the patient's leg and foot <NUM> is within the ring <NUM> of the machine <NUM>. In the case of a CT image scanning machine <NUM>, a scan of the patient may produce a volumetric dataset or individual slices (e.g., axial, coronal, sagittal). Most conventional CT image scanning machines <NUM> are helical computed axial tomography machines where the x-ray beam traces a helical path relative to the patient, as the patient is translated relative to the machine. The helical CT machines <NUM> produce a volumetric scan that can be reconstructed into sequential images with a defined spacing, which is a similar result to sequential scanning acquisition machines <NUM> that perform scans at a pre-defined spacing as the gantry moves the patient sequentially through the ring <NUM> of the machine <NUM>. Helical CT machines <NUM> may be advantageous because a helical scan of the patient can be performed in seconds, whereas a sequential scan can take tens of minutes.

As the patient's leg and foot <NUM> is scanned via the scanning machine <NUM>, the computer <NUM> stores the raw data obtained from the image scan, and may process the data so it is useable by a user (e.g., radiologist, engineer, surgeon). The raw data may be processed and stored as a Digital Imaging and Communications in Medicine ("DICOM") file. The DICOM file is a communication protocol and a file format for storing medical information, such as the volumetric dataset of the patient's foot, for example. Using a DICOM viewer program, the DICOM file can be opened and the data can be viewed in various forms. For example, volumetric data from a helical scan can be reconstructed into various two-dimensional ("2D") views such as axial, sagittal, and coronal views. The data may additionally or alternatively be processed into Digitally Reconstructed Radiographs ("DRR").

Upon processing of the raw data from the image scanning machine <NUM> via the computer <NUM>, exemplary 2D images can be seen in <FIG>, which specifically shows a stack <NUM> of scan or slice images <NUM> of the patient's leg and foot <NUM>. In this example, the images <NUM> may be 2D images reconstructed from a volumetric dataset acquired via a helical CT machine <NUM> or from a CT machine <NUM> that acquired sequential images. As seen in <FIG>, the images <NUM> are sagittal image slices <NUM> of the leg and foot <NUM>. The entire stack <NUM> of image slices <NUM> may make up the entirety of the patient's leg and foot <NUM> from a medial side to a lateral side.

The images <NUM> of the patient's leg and foot <NUM> are with the patient lying on the imaging table <NUM> in a supine position. That is, the patient's leg and foot <NUM> is unweighted, or in a non-weight bearing condition. Thus, the images <NUM> taken with the imaging machine <NUM> show the bones of the leg and foot <NUM> in an uncompressed or non-load bearing fashion.

Step <NUM> may be described as a computer process that includes a step of receiving first patient bone data <NUM>, <NUM> of a patient leg and foot <NUM> in a first pose. The first patient bone data <NUM>, <NUM> may be generated via a first imaging modality such as CT or MRI. The first pose includes a position and orientation of the patient leg relative to the patient foot <NUM> as defined in the first patient bone data <NUM>, <NUM>.

The images <NUM> of the patient's leg and foot <NUM> in a non-weight bearing condition are in contrast to X-ray images of the patient's foot in a weight bearing condition. Referring back to <FIG>, step <NUM> of the method <NUM> may include generating two-dimensional ("2D") images of the patient's leg and foot in a weight bearing condition. To that end, reference is made to <FIG>, which depict, respectively, the patient <NUM> having a medial and an anteroposterior X-ray of the leg and foot <NUM> with the patient <NUM> in a standing position. As seen in <FIG>, a generator <NUM> and a detector <NUM> of an X-ray machine <NUM> are positioned on either side of the foot <NUM> of the patient <NUM>. The generator <NUM> is positioned on the medial side of the foot <NUM>, and the detector <NUM> is positioned on the lateral side of the foot <NUM> in <FIG>. Thus, the resulting 2D X-ray image <NUM> of the foot <NUM> in a medial or lateral view can be seen in <FIG>. In certain instances, a lateral 2D X-ray image of the patient foot <NUM> may also be generated (not shown).

As seen in <FIG>, the generator <NUM> is positioned in front of the foot <NUM> and the detector <NUM> is positioned behind the foot <NUM> so as to produce an anteroposterior ("AP") X-ray image of the foot <NUM>. The resulting 2D X-ray image <NUM> of the foot <NUM> in the anteroposterior position can be seen in <FIG>. The images <NUM>, <NUM> of the patient's foot <NUM> produced via the X-ray machine <NUM> depict the bones of the foot in a weighted condition since the X-rays were performed with the patient <NUM> standing. While X-rays of the foot <NUM> are described as being performed in the medial, lateral, and anteroposterior views, X-rays may be taken of the foot <NUM> in additional or alternative views without departing from the teachings of the present disclosure.

Step <NUM> may be described as a computer process that includes a step of receiving second patient bone data <NUM>, <NUM> of the patient leg and foot <NUM> in a second pose. The second patient bone data <NUM>, <NUM> may be generated via a second imaging modality that is different from the first imaging modality such as X-ray. The second pose includes a position and orientation of the patient leg relative to the patient foot <NUM> as defined in the second patient bone data <NUM>, <NUM>.

Referring back to the method <NUM> in <FIG>, step <NUM> may include segmenting the bones of the patient's foot in the images of the foot in the non-weight bearing condition. As seen in <FIG>, the individual bones (tibia <NUM>, talus <NUM>, calcaneus <NUM>, navicular <NUM>, cuneiforms <NUM>, metatarsals, phalanges, fibula, etc.) of the leg and foot <NUM> may segmented along their respective bone boundaries in each of the images <NUM> of the stack <NUM>. While <FIG> illustrates the calcaneus <NUM> and the talus <NUM> segmented along their respective bone boundaries (i.e., the white line around the perimeter of the bones) in a single image <NUM>, all bones or only the bones relevant to the TAR procedure may be segmented in all of the images <NUM> of the stack <NUM>. Thus, after the segmentation process of step <NUM> of the method <NUM> of <FIG>, all images <NUM> of the stack <NUM> may include the bones of the foot <NUM> segmented along their respective bone boundaries.

Next, step <NUM> of the method <NUM> of <FIG> includes generating a three-dimensional ("3D") bone model of the patient's foot <NUM> from the segmented images <NUM> of the stack <NUM>. An exemplary bone model <NUM> of the patient's foot <NUM> formed from the individually segmented bones in the images <NUM> of the stack <NUM>, and displayed on a display screen <NUM> of a display device <NUM>, may be seen in <FIG>. Generation of the bone model <NUM> may be done via the computer <NUM> by interpolating a surface mesh between the spaces between the individually segmented images <NUM> to form a 3D surface profile approximating the surface contours of the bones of the patient's foot. As seen in <FIG>, the bones of the foot <NUM> and the leg, including the tibia and fibula are at least partially generated into 3D form.

Exemplary computer programs for generating the 3D bone model <NUM> from the images <NUM> may include: Analyze from AnalyzeDirect, Inc. , Overland Park, Kans. ; Insight Toolkit, an open-source software available from the National Library of Medicine Insight Segmentation and Registration Toolkit ("ITK"), www. org; 3D Slicer, an open-source software available from www. org; Mimics from Materialise, Ann Arbor, Mich. ; and Paraview available at www. org, among others.

Step <NUM> may be described as a computer process including a step of generating a three-dimensional (3D) bone model <NUM> of the patient leg and foot <NUM> from the first patient bone data <NUM>, <NUM>, where the 3D bone model includes a plurality of 3D bone models arranged in the first pose.

Referring back to <FIG>, step <NUM> of the method <NUM> may include importing the 3D bone model <NUM> of <FIG> and the 2D images of <FIG> into a common coordinate system. <FIG> illustrates the 3D bone model <NUM> and the 2D image <NUM> (medial or lateral view) of the patient's foot <NUM> in a common coordinate system (x,y,z). Since the 2D image <NUM> is a medial or lateral view in this example (planar views of the bones of the foot <NUM>), the 3D bone model <NUM> may also be oriented in the same medial or lateral view. As seen in <FIG>, the 3D bone model <NUM> is oriented in a lateral view to match a lateral 2D image <NUM>.

Referring to <FIG>, which is a continuation of the method <NUM> of <FIG>, step <NUM> includes orienting the 3D bone model <NUM> in a matching orientation with one or both of the 2D images <NUM>, <NUM>. For example, as seen in <FIG>, the 3D bone model <NUM> is oriented in a lateral view that matches the lateral 2D X-ray image <NUM>. The 3D bone model <NUM> and the 2D image <NUM> are displayed on the display screen <NUM> of the display device <NUM> (e.g., computer <NUM>, tablet). The 2D image <NUM> of the foot <NUM> in the anteroposterior view, of <FIG>, may not be shown in <FIG> because the image <NUM>, which lies in a plane (y, z plane), is perpendicular to the plane (x, y plane) of the image <NUM> in <FIG>. But, the orientation of the bone model <NUM> may be changed to the y, z plane such that the 2D X-ray image <NUM> of the foot <NUM> in the anteroposterior view is shown.

Step <NUM> of the method <NUM> of <FIG> may also include scaling at least one of the 3D bone model <NUM> and the 2D images until the scales of the bones are the same. Since the 2D images <NUM>, <NUM> are already the same size, the bone model <NUM> may be scaled to match the size of the bones in the 2D images <NUM>, <NUM> with a single step. Alternatively, the scale of the 2D images <NUM>, <NUM> individually or in combination may be scaled to match the size of the 3D bone model <NUM>. The scaling may be performed manually (which does not fall under the scope of the claims) or automatically. The scale of the 3D bone model <NUM> and the 2D image <NUM> are the same in <FIG>.

Step <NUM> of the method <NUM> of <FIG> also includes orienting the individual bones of the 3D bone model <NUM> to match the orientation of the individual bones of in the 2D images <NUM>, <NUM>. This step may include step <NUM>, which may include translating and/or rotating the individual bones of the 3D bone model <NUM> in medial, lateral, and anteroposterior views to match the orientation of the bones in the corresponding 2D images <NUM>, <NUM>. This step may be described as a computer process including a step of modifying the 3D bone model <NUM> of the patient leg and foot <NUM> such that the plurality of 3D bone models are reoriented into a third pose that matches a particular arrangement of bones in the patient leg and foot in the second pose. In certain instances, the step of modifying the 3D bone model <NUM> may be manual, automatic, or partially manual and partially automatic, wherein only the computer-implemented embodiments fall under the scope of the claims.

As seen in <FIG>, which is a view of the 2D image <NUM> of the foot <NUM> in the weighted condition overlaid with the 3D bone model <NUM> of the foot in the non-weighted condition, the bones of the foot in the bone model <NUM> have been rotated clockwise and translated in the y-direction until a proximal surface of the talus in the bone model <NUM> is coextensive or overlaps with the proximal surface of the talus in the 2D X-ray image <NUM>. As seen in <FIG>, the individual bones of the 3D bone model <NUM> have not yet been moved relative to each other. Instead, the entire set of bones forming the 3D bone model <NUM> have been roughly aligned with the bones of the foot <NUM> in the X-ray image <NUM>.

Reference is made to <FIG>, which is the same views of the 3D bone model <NUM> and 2D X-ray image <NUM> displayed on the display screen <NUM> of the display device <NUM> of <FIG>, except the individual bones of the 3D bone model <NUM> have been translated and/or rotated relative to each other so as to match the pose of the bones of the foot <NUM> in the X-ray image. As seen in <FIG>, an outline or projection of the bones of the bone model <NUM> have been adjusted or reoriented relative to each other so as to match the positioning/spacing orientation of the bones in the 2D X-ray image <NUM> in the medial view. The same process may take place for adjusting the orientation of the bones in the y, z plane with respect to the 2D X-ray image <NUM> of the foot <NUM> in the anteroposterior view, as well as other views including but not limited to the medial view, dorsal view, etc..

The 3D bone model <NUM> of the foot <NUM>, in <FIG>, may be encircled by a rotation tool (not shown) indicating the computer <NUM> may rotate the selected 3D bone model of the foot within the particular plane to match the particular pose of the foot <NUM> in the X-ray image <NUM>. The rotation tool may rotate the 3D bone models <NUM> about any axis (e.g., x,y,z) to align the models with the X-ray images. The GUI of the computer <NUM> may include a translation tool (not shown) for translating any of the bones of the bone model <NUM> in a particular direction (e.g., x, y, z). Particularly, the GUI may permit the translation tool <NUM> to move the bones of the bone model <NUM> in an x-direction or y-direction. On the GUI of the computer <NUM>, there may be a selection drop down for switching each of the 3D bone models <NUM> between cuneiform, cuboid, metatarsals, calcaneus etc. Additionally, the GUI of the computer may also allow the switching between rotation, scale and translation modes.

The 3D bone models <NUM> and X-ray images <NUM>, <NUM> may be iteratively translated, rotated, and/or scaled till the bone contour lines (outer most boundary as projected on a plane) align with each other. Additionally or alternatively, certain bone landmarks on the bone surface may be identified in each of the 3D bone models <NUM> and X-ray images <NUM>, <NUM> and the landmarks may be positionally matched such that they align with each other. Instead of surface landmarks, a centroid of the 3D bone models may be identified and similarly identified in the lateral and anteroposterior views of the X-rays <NUM>, <NUM>, and the centroids can be matched so the models and X-rays align with each other.

In certain instances, accuracy of mapping the 3D bone models to the X-ray images may be improved by introducing pick able landmarks on the X-ray and the Bone mesh for correspondence.

In the automated pose estimation, the mapping of the 3D bone models to the X-ray images may be fully or partially automated. One such method <NUM> for automated pose estimation of a 3D bone model <NUM> may be seen in the flowchart of <FIG>. Referring to <FIG>, the method <NUM> may include steps <NUM>, <NUM>, and <NUM>, which are identical to steps <NUM>, <NUM>, and <NUM> of the method <NUM> described in reference to <FIG>, among others. Thus, steps <NUM>, <NUM>, and <NUM> will not be described in detail; instead, please refer to the previous discussion of steps <NUM>, <NUM>, and <NUM> for a detailed description. Generally, step <NUM> may include generating images or an image dataset (e.g., CT images, MRI, ultrasound images) of the patient's foot <NUM> in a non-weight bearing condition such as, for example, with the patient <NUM> laying supine on a an imaging table <NUM>. At step <NUM>, the bones of the foot <NUM> as seen in the images or image dataset may be segmented (e.g., along the bone contour lines in sagittal, axial, or coronal images). At step <NUM>, a 3D bone model <NUM> of the patient's foot <NUM> may be generated from the segmented images.

Step <NUM> of <FIG> is the same as step <NUM> of <FIG>; therefore, a detailed discussion of this step will not be included for the method <NUM> in <FIG>. Please refer to the details of step <NUM> regarding the specifics of step <NUM>. Generally, step <NUM> may include generating 2D X-ray images <NUM> (as seen in the lateral X-ray view of <FIG> and the medial X-ray view of <FIG>) of the patient's foot <NUM> in a weight bearing condition (e.g., standing X-rays). Medial, lateral, and anteroposterior views (seen in <FIG>), among others, may be generated. The X-ray images <NUM> of <FIG> are illustrative and may be labeled as: right foot, lateral-to-medial view, and right foot medial-to-lateral view; or left foot, medial-to-lateral view, and left foot lateral-to-medial view, respectively, as it can be difficult or impossible to determine the views of X-rays without labeling.

Step <NUM> of <FIG> may include segmenting the individual bones of the foot <NUM> in the 2D X-ray images <NUM>. As seen in <FIG>, the talus <NUM> and the calcaneus <NUM> are segmented from the 2D X-ray image <NUM> in the lateral view, and the same bones are segmented from the 2D X-ray image <NUM> in the medial view of <FIG>. Segmentation may also take place with a 2D X-ray image <NUM> in an anteroposterior view (not shown), among others.

In <FIG>, only the talus <NUM> and calcaneus <NUM> are shown segmented, but other bones of the foot <NUM> including the tibia, navicular, cuneiforms, metatarsals, and phalanges, among others, may be segmented. The bones of the talus <NUM> and calcaneus <NUM> are exemplary illustrations, but the method <NUM> is intended to include the segmentation or additional or alternative bones of the foot <NUM> depending on the bones desired to be estimated in their pose.

Following segmentation in step <NUM>, step <NUM> of <FIG> may include generating a bone contour line along the perimeter of each of the segmented bones of the foot <NUM>. As seen in <FIG>, beneath the segmented talus <NUM> is a contour line <NUM> defining a perimeter of the segmented talus <NUM>, and beneath the segmented calcaneus <NUM> is a contour line <NUM> defining a perimeter of the segmented calcaneus <NUM>. The perimeter contour lines <NUM>, <NUM> represent an outer shape of the bones of the talus <NUM> and calcaneus <NUM> in their particular pose (position and orientation) when standing in the X-ray image <NUM>.

Turning back to the method <NUM> as seen in <FIG>, and continuing from step <NUM>, step <NUM> may include generating a plurality of poses of each of the individual bones making up the 3D bone model <NUM>. <FIG> illustrate a 3D object <NUM> such as the individual bones making up the 3D bone model <NUM> of the foot in various poses, each having a different pose. <FIG> shows the object 210a in a first pose, <FIG> shows the object 210b in a second pose, and <FIG> shows the object 210c in a third pose. As described herein, pose refers to the position and orientation of an object in space. Therefore, it can be seen in <FIG> that the 3D objects 210a, 210b, 210c are in different rotation orientations relative to each other, each having been rotated along various axes. The 3D objects 210a, 210b, 210c may represent the individual bones of the 3D bone model <NUM> having been generated in a plurality of poses. Since there are many bones of the foot <NUM>, and there are a near infinite number of poses for each of the bones of the foot <NUM>, it is most efficient to describe the objects <NUM>0a, <NUM>0b, 210c as representing the plurality of poses of the bones of the foot <NUM>.

In certain instances, a certain number of finite poses of each of the bones of the foot <NUM> may be generated. In certain instances, one hundred different poses of each of the bones of the foot <NUM> may be generated. In certain instances, five hundred different poses of each of the bones of the foot <NUM> may be generated. In certain instances, one thousand different poses of each of the bones of the foot <NUM> may be generated. In certain instances, the poses of each of the bones of the foot <NUM> can be changed in any one or multiple of the six degrees of freedom (three translations and three rotations). The smaller the differences among the poses (e.g., a change of <NUM> degree of rotation on an axis for each different pose), the higher the number of poses that will be generated. In contrast, the larger the differences between the poses (e.g., a change of <NUM> degrees of rotation on an axis for each different pose), the fewer the number of poses that will be generated.

Step <NUM> of <FIG> may include generating a plurality of projections from the plurality of poses of each of the bones of the foot <NUM>. As seen in <FIG>, a projection (i.e., in a plane, in 2D) or outline of the perimeter <NUM> of each of the plurality of poses of the bones (or object <NUM> as seen in <FIG>) is generated. As seen in <FIG>, the projections 212a, 212b, 212c of the poses of the objects 210a, 210b, 210c are different for each pose, where the first pose of the object 210a in <FIG> yields the projection 212a in <FIG>. Similarly, the second pose of the object 210b in <FIG> yields the projection 212b in <FIG>, and the third pose of the object 210c in <FIG> yields the projection 212c in <FIG>.

Referring back to the method <NUM> of <FIG>, step <NUM> may include a comparison step that includes comparing, for each bone of the foot <NUM> of interest, the bone contour line as determined from the 2D X-ray images <NUM> (contour line <NUM> for the talus, and contour line <NUM> for the calcaneus in <FIG>) to the plurality of projections (projections 212a, 212b, and 212c in <FIG>) as determined from the 3D bone model <NUM>, and identifying a particular projection from the plurality of projections 212a, 212b, 212c that best-fits or most closely matches the bone contour line.

<FIG> illustrates step <NUM> of the method <NUM>. As seen in the figure, the talus contour lines <NUM> as identified from the 2D X-ray images <NUM> is compared to an example plurality of projections 212d, 212e, 212f, and <NUM>, and a particular one of the plurality of projections <NUM> is identified as being the best-fit or closest match in shape and orientation. This comparison step <NUM> may include a shape matching algorithm that compares the shape and area within its boundary of each of the plurality of projections 212d-g to the shape and area within the boundary of the talus contour line <NUM>. The particular projection <NUM> of the plurality of projections 212d-g that most closely matches the values of the talus contour line <NUM> is identified as the best-fit or most closely matching.

Each of the plurality of projections <NUM> may be sampled radially in the form of a shape context. And the data for each of the plurality of projections <NUM> may be compared with the shape context of the contour line <NUM>.

The comparison and identification step <NUM> may include employing a Jaccard similarity coefficient for comparing the similarity and diversity contour line as determined from the 2D X-ray image to each of the plurality of projections as determined from the 3D bone model <NUM>. In comparing the contour line <NUM> to each of the projections 212d-g, as seen in <FIG>, a Jaccard similarity coefficient may be assigned to each comparison. The assigned coefficient can be used to determine which pair is the most similar. An example Jaccard similarity measurement may be defined as: Jaccard Similarity J (A, B) - I Intersection (A, B) I / I Union (A, B) I.

An example shape matching algorithm that may be employed in the method <NUM> for comparing and identifying the particular projection that most closely matches the contour line as determined from the 2D X-ray images <NUM> may be seen in the following document: "A Comparison of Shape Matching Methods for Contour Based Pose Estimation" by Bodo Rosenhahn, Thomas Brox, Daniel Cremers, and Hans-Peter Seidel (https://vision. de/media/spezial/bib/rosenhahn_iwcia06.

Step <NUM> may be employed for each individual bone of interest. That is, while <FIG> only depicts the talus, step <NUM> may be employed for additional or alternative bones including the tibia, calcaneus, navicular, cuneiforms, metatarsals, phalanges, etc..

Referring back to <FIG>, the method <NUM> at step <NUM> may include error checking via an iterative process to determine if there are any additional poses that provide a better-fit than the particular pose identified from the plurality of poses. Step <NUM> may include modifying the plurality of poses <NUM> and projections <NUM>, and running the comparison and identification step of step <NUM> again to see if there are better-fit poses.

Once the iterative process has ran and the best-fitting projections have been determined, the individual bones of the bone model <NUM> may be arranged according to the orientation of the identified particular projection for each of the bones, and the individual bones of the bone model <NUM> may be arranged relative to each other according to their spacing in the 2D X-ray images <NUM>, as seen in step <NUM> of <FIG>.

Stated differently, each of the particular projections identified as being the best fit to the contour lines determined from the 2D X-ray images <NUM> determines the orientation of the individual bones of the bone model <NUM>. Step <NUM> may include arranging the individual bones of the bone model <NUM> according to their respective particular projection that was identified as the best fit with the contour lines determined from the 2D X-ray images <NUM>.

Once the bones of the bone model <NUM> are arranged according to step <NUM>, the bone model <NUM> is in a pose that matches or replicates a weighted condition of the foot <NUM> as it appeared in the 2D X-ray images <NUM>.

Referring to <FIG>, the method <NUM> may additionally include preoperatively planning the TAR procedure, at step <NUM>. This step may include determining resection placement, resection depth, implant placement, implant depth, implant type and size, and surgical approach, among other parameters.

Step <NUM> of the method <NUM> may then include performing the TAR procedure according to the preoperative plan at step <NUM>. This may include sedating the patient, creating an incision into the patient's skin, resecting bone, implanting a fixation device or implant, and closing the incision, among other steps of a TAR procedure.

Referring back to <FIG>, the method <NUM> may include, at step <NUM>, applying bio-kinematic constraints to the generation of the plurality of poses for each of the bones making up the 3D bone model <NUM>. The bio-kinematic constraints may limit the number of poses generated or the number of projections that are ultimately generated at step <NUM>. The bio-kinematic constraints may limit the poses to those that are bio-kinematically relevant, whereas such poses that are not bio-kinematically relevant will not be generated, or will be discarded.

In certain instances, the bio-kinematic constraints may include orientation guidelines for each bone as it relates to surrounding bones given a known view (e.g., lateral, medial, anteroposterior). That is, as seen in <FIG>, which is a lateral-to-medial or medial-to-lateral X-Ray image <NUM> of the right foot <NUM>, the talus <NUM>, for example, includes a convex head <NUM> that articulates with a concave articular surface portion <NUM> on the superior surface of the navicular <NUM>. The talus <NUM> also includes a large posterior facet <NUM> abutting the calcaneus <NUM>, and a large superior articular surface (talar dome) <NUM> for abutting the inferior aspect of the tibia <NUM>. Thus, if the view is known (e.g., lateral, medial, AP), certain constraints can be built into the system that exclude poses that are not relevant. For example, in a lateral view of the foot as seen in <FIG>, certain parts of the bones may be identified in relevant bones. For the calcaneus <NUM>, the calcaneal tuberosity <NUM>, the posterior facet <NUM>, and facet for cuboid <NUM> may be identified in the X-Ray image <NUM> or the segmented image of the calcaneus (not shown). For the talus <NUM>, the talar dome <NUM>, and head <NUM> may be identified in the X-Ray image <NUM> or the segmented image of the talus (not shown). For the navicular <NUM>, the superior articular surface <NUM> for abutting the talus head <NUM> may be identified in the X-Ray image <NUM> or the segmented image of the navicular (not shown).

Once these points are identified on the relevant bones, certain poses can be eliminated that do not meet the bio-kinematics of the foot. For instance, the calcaneal tuberosity <NUM> must be at a left-most position in a lateral view of the right foot. The calcaneal posterior facet <NUM> generally faces oppositely of the calcaneal tuberosity <NUM>, and abuts the talus <NUM>. The calcaneal facet for the cuboid <NUM> is generally in a far right position in the lateral view of the right foot. For the talus <NUM>, the talar dome <NUM> is generally oriented upwards, facing the distal tibia <NUM>. And the talar head <NUM> generally faces to the right in the lateral view of the right foot. For the navicular <NUM>, the superior articular surface <NUM> generally faces to the left in the lateral view of the right foot.

All this information can be used to constrain the poses generated at step <NUM> by eliminating poses that have, for example: the calcaneal tuberosity <NUM> at a far right position in a lateral view of the right foot <NUM>; the calcaneal facet for the cuboid <NUM> that faces left; talar dome <NUM> facing downward or to the right; talar head <NUM> facing left; and superior articular surface <NUM> of the navicular <NUM> facing right; among others.

In certain instances, the 3D bone model <NUM> may be modeled using landmarks. For instance, the articular surfaces of the bones may be identified and the poses from step <NUM> may be limited to orientations that require the articular surfaces to oppose each other and be a certain distance from each other. Certain motion of the joints may also be used as constraints. For instance, the forefoot may be modeled as a hinge joint, and the talocrural joint can be modeled as a hinge joint with rotation axis about the line on the superior point of lateral and medial malleolus. Thus, certain poses that do not permit such rotation about the rotation axis may be eliminated.

<FIG> is a flowchart depicting an exemplary method <NUM> for constraining the poses of step <NUM> using bio-kinematics (step <NUM>). The method <NUM> may include, at step <NUM>, identifying landmarks in each of the individual bones of the 3D bone model <NUM>. This step may include performing a topological data analysis ("TDA") to extract information from the dataset associated with each bone of the bone model <NUM>. Next, at step <NUM>, the method <NUM> may include building coordinate frames for each of the bones in the bone model <NUM> from the identified landmarks of step <NUM>. At step <NUM>, the method <NUM> may further include identifying permissible relationships (e.g., rotational orientation, translational orientation) between the coordinate frames for each bone of the bone model <NUM>. At step <NUM>, the method <NUM> may include providing a plurality of orientations of the bones within permissible rotational and translational relationships for the generation of poses at step <NUM>.

Another method of mapping the 3D bone models <NUM> to the X-ray images may include an augmented pose estimation, which may be a combination of manual and automated procedures. For instance, instead of running a contour matching algorithm as described in Section II. on a complete set of the bones of the leg and foot, the contour matching algorithm may be limited to certain bone structures, such as the fibula, tibia, talus, and, calcaneus. The remaining bones of the foot may be extrapolated from the resulting pose of the fibula, tibia, talus and calcaneus.

In certain instances, a user may manually map bone contour surfaces or landmarks on the individual bones of the 3D bone model <NUM> to corresponding points on the X-ray images, as described in Section I. Then, the user may perform an automation step (as in Section II. ) to optimize the pose further on particular bone structures. In this way, the user provides a "rough" estimate of pose, and the automation process fine-tunes the original "rough" estimate of pose.

In sum, the above described techniques may be used to estimate absolute pose of a foot <NUM> in anteroposterior, lateral, and medial views with respect to tibia and relative bone positions in the foot <NUM>. These methods may improve accuracy in deformity assessment and hence correction for TAR procedures.

Referring to <FIG>, a detailed description of an example computing system <NUM> having one or more computing units that may implement various systems and methods discussed herein is provided. The computing system <NUM> may be applicable to any of the computers or systems utilized in the planning of the TAR procedure, and other computing or network devices. It will be appreciated that specific implementations of these devices may be of differing possible specific computing architectures not all of which are specifically discussed herein but will be understood by those of ordinary skill in the art.

The computer system <NUM> may be a computing system that is capable of executing a computer program product to execute a computer process. Data and program files may be input to the computer system <NUM>, which reads the files and executes the programs therein. Some of the elements of the computer system <NUM> are shown in <FIG>, including one or more hardware processors <NUM>, one or more data storage devices <NUM>, one or more memory devices <NUM>, and/or one or more ports <NUM>-<NUM>. Additionally, other elements that will be recognized by those skilled in the art may be included in the computing system <NUM> but are not explicitly depicted in <FIG> or discussed further herein. Various elements of the computer system <NUM> may communicate with one another by way of one or more communication buses, point-to-point communication paths, or other communication means not explicitly depicted in <FIG>.

The processor <NUM> may include, for example, a central processing unit (CPU), a microprocessor, a microcontroller, a digital signal processor (DSP), and/or one or more internal levels of cache. There may be one or more processors <NUM>, such that the processor <NUM> comprises a single central-processing unit, or a plurality of processing units capable of executing instructions and performing operations in parallel with each other, commonly referred to as a parallel processing environment.

The computer system <NUM> may be a conventional computer, a distributed computer, or any other type of computer, such as one or more external computers made available via a cloud computing architecture. The presently described technology is optionally implemented in software stored on the data stored device(s) <NUM>, stored on the memory device(s) <NUM>, and/or communicated via one or more of the ports <NUM>-<NUM>, thereby transforming the computer system <NUM> in <FIG> to a special purpose machine for implementing the operations described herein. Examples of the computer system <NUM> include personal computers, terminals, workstations, mobile phones, tablets, laptops, personal computers, multimedia consoles, gaming consoles, set top boxes, and the like.

The one or more data storage devices <NUM> may include any non-volatile data storage device capable of storing data generated or employed within the computing system <NUM>, such as computer executable instructions for performing a computer process, which may include instructions of both application programs and an operating system (OS) that manages the various components of the computing system <NUM>. The data storage devices <NUM> may include, without limitation, magnetic disk drives, optical disk drives, solid state drives (SSDs), flash drives, and the like. The data storage devices <NUM> may include removable data storage media, non-removable data storage media, and/or external storage devices made available via a wired or wireless network architecture with such computer program products, including one or more database management products, web server products, application server products, and/or other additional software components. Examples of removable data storage media include Compact Disc Read-Only Memory (CD-ROM), Digital Versatile Disc Read-Only Memory (DVD-ROM), magneto-optical disks, flash drives, and the like. Examples of non-removable data storage media include internal magnetic hard disks, SSDs, and the like. The one or more memory devices <NUM> may include volatile memory (e.g., dynamic random access memory (DRAM), static random access memory (SRAM), etc.) and/or non-volatile memory (e.g., read-only memory (ROM), flash memory, etc.).

Computer program products containing mechanisms to effectuate the systems and methods in accordance with the presently described technology may reside in the data storage devices <NUM> and/or the memory devices <NUM>, which may be referred to as machine-readable media. It will be appreciated that machine-readable media may include any tangible non-transitory medium that is capable of storing or encoding instructions to perform any one or more of the operations of the present disclosure for execution by a machine or that is capable of storing or encoding data structures and/or modules utilized by or associated with such instructions. Machine-readable media may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more executable instructions or data structures.

In some implementations, the computer system <NUM> includes one or more ports, such as an input/output (I/O) port <NUM> and a communication port <NUM>, for communicating with other computing, network, or other devices. It will be appreciated that the ports <NUM>-<NUM> may be combined or separate and that more or fewer ports may be included in the computer system <NUM>.

The I/O port <NUM> may be connected to an I/O device, or other device, by which information is input to or output from the computing system <NUM>. Such I/O devices may include, without limitation, one or more input devices, output devices, and/or environment transducer devices.

In one implementation, the input devices convert a human-generated signal, such as, human voice, physical movement, physical touch or pressure, and/or the like, into electrical signals as input data into the computing system <NUM> via the I/O port <NUM>. Similarly, the output devices may convert electrical signals received from computing system <NUM> via the I/O port <NUM> into signals that may be sensed as output by a human, such as sound, light, and/or touch. The input device may be an alphanumeric input device, including alphanumeric and other keys for communicating information and/or command selections to the processor <NUM> via the I/O port <NUM>. The input device may be another type of user input device including, but not limited to: direction and selection control devices, such as a mouse, a trackball, cursor direction keys, a joystick, and/or a wheel; one or more sensors, such as a camera, a microphone, a positional sensor, an orientation sensor, a gravitational sensor, an inertial sensor, and/or an accelerometer; and/or a touch-sensitive display screen ("touchscreen"). The output devices may include, without limitation, a display, a touchscreen, a speaker, a tactile and/or haptic output device, and/or the like. In some implementations, the input device and the output device may be the same device, for example, in the case of a touchscreen.

In one implementation, a communication port <NUM> is connected to a network by way of which the computer system <NUM> may receive network data useful in executing the methods and systems set out herein as well as transmitting information and network configuration changes determined thereby. Stated differently, the communication port <NUM> connects the computer system <NUM> to one or more communication interface devices configured to transmit and/or receive information between the computing system <NUM> and other devices by way of one or more wired or wireless communication networks or connections. Examples of such networks or connections include, without limitation, Universal Serial Bus (USB), Ethernet, Wi-Fi, Bluetooth®, Near Field Communication (NFC), Long-Term Evolution (LTE), and so on. One or more such communication interface devices may be utilized via the communication port <NUM> to communicate one or more other machines, either directly over a point-to-point communication path, over a wide area network (WAN) (e.g., the Internet), over a local area network (LAN), over a cellular (e.g., third generation (<NUM>) or fourth generation (<NUM>)) network, or over another communication means. Further, the communication port <NUM> may communicate with an antenna or other link for electromagnetic signal transmission and/or reception.

In an example implementation, patient data, bone models, transformation, mapping and shape matching software, tracking and navigation software, registration software, and other software and other modules and services may be embodied by instructions stored on the data storage devices <NUM> and/or the memory devices <NUM> and executed by the processor <NUM>. The computer system <NUM> may be integrated with or otherwise form part of a surgical system for planning and performing a TAR procedure.

The system set forth in <FIG> is but one possible example of a computer system that may employ or be configured in accordance with aspects of the present disclosure. It will be appreciated that other non-transitory tangible computer-readable storage media storing computer-executable instructions for implementing the presently disclosed technology on a computing system may be utilized.

In the present disclosure, the methods disclosed herein may be implemented as sets of instructions or software readable by a device. Further, it is understood that the specific order or hierarchy of steps in the methods disclosed are instances of example approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the method can be rearranged while remaining within the disclosed subject matter. The accompanying clauses to a method present elements of the various steps in a sample order, and are not necessarily meant to be limited to the specific order or hierarchy presented.

The described disclosure including any of the methods described herein may be provided as a computer program product, or software, that may include a non-transitory machine-readable medium having stored thereon instructions, which may be used to program a computer system (or other electronic devices) to perform a process according to the present disclosure. A machine-readable medium includes any mechanism for storing information in a form (e.g., software, processing application) readable by a machine (e.g., a computer). The machine-readable medium may include, but is not limited to, magnetic storage medium, optical storage medium; magneto-optical storage medium, read only memory (ROM); random access memory (RAM); erasable programmable memory (e.g., EPROM and EEPROM); flash memory; or other types of medium suitable for storing electronic instructions.

An example system for processing patient data so as to map weight bearing considerations from standing X-ray images to bones of a 3D bone model may include the following components: a network interface configured to receive one or more sets of patient data; a processing device in communication with the network interface; and a computer-readable medium in communication with the processing device configured to store information and instructions that, when executed by the processing device, performs the operations of: receiving first patient data <NUM>, <NUM> comprising at least one two-dimensional (2D) image <NUM>, <NUM> of a patient leg and foot <NUM> in a weighted pose. Additional operations may include receiving second patient data <NUM>, <NUM> comprising computed tomography (CT) images <NUM>, <NUM> of the patient leg and foot <NUM> in a non-weighted pose, where the first patient data <NUM>, <NUM> and the second patient data <NUM>, <NUM> are the result of separate imaging events. Additional operations may include generating a three-dimensional (3D) bone model <NUM> of the patient leg and foot <NUM> from the CT images <NUM>, <NUM>, where the 3D bone model <NUM> may include a plurality of 3D bone models representing individual bones of the patient leg and foot <NUM>. Additional operations may include rearranging the plurality of 3D bone models <NUM> to mimic the weighted pose of the patient leg and foot <NUM> in the at least one 2D image <NUM>, <NUM>.

In certain instances, additional operations may include: generating a plurality of 2D projections of poses <NUM> of the plurality of 3D bone models <NUM>; comparing the plurality of 2D projections <NUM> to contour lines <NUM>, <NUM> outlining perimeters of bones of the patient leg and foot <NUM> in the at least one 2D image <NUM>, <NUM>; and identifying particular 2D projections <NUM> from the plurality of 2D projections <NUM> that best-fit a shape and size of the contour lines <NUM>, <NUM>.

While the present disclosure has been described with reference to various implementations, it will be understood that these implementations are illustrative and that the scope of the present disclosure is not limited to them. Many variations, modifications, additions, and improvements are possible. More generally, embodiments in accordance with the present disclosure have been described in the context of particular implementations. Functionality may be separated or combined in blocks differently in various embodiments of the disclosure or described with different terminology. These and other variations, modifications, additions, and improvements may fall within the scope of the disclosure as defined in the claims.

Claim 1:
A computer-implemented method (<NUM>, <NUM>) comprising:
receiving first patient bone data (<NUM>, <NUM>) of a patient leg and foot (<NUM>) in a first pose, the first patient bone data (<NUM>, <NUM>) generated via a first imaging modality, the first pose comprising a position and orientation of the patient leg relative to the patient foot (<NUM>) as defined in the first patient bone data (<NUM>, <NUM>), the first pose comprising a non-weighted condition of the patient leg and the patient foot (<NUM>);
receiving second patient bone data (<NUM>, <NUM>) of the patient leg and foot (<NUM>) in a second pose, the second patient bone data generated via a second imaging modality that is different from the first imaging modality, the second pose comprising a position and orientation of the patient leg relative to the patient foot as defined in the second patient bone data, the second pose comprising a weighted condition of the patient leg and the patient foot (<NUM>);
generating a three-dimensional (3D) bone model (<NUM>) of the patient leg and foot (<NUM>) from the first patient bone data (<NUM>, <NUM>), the 3D bone model (<NUM>) comprising a plurality of 3D bone models arranged in the first pose, wherein the 3D bone models represent individual bones of the patient leg and foot (<NUM>); and
modifying the 3D bone model (<NUM>) of the patient leg and foot (<NUM>) such that the plurality of 3D bone models are reoriented into a third pose that matches a particular arrangement of bones in the patient leg and foot (<NUM>) in the second pose.