WO2024044169A1

WO2024044169A1 - Method for determining optimal bone resection

Info

Publication number: WO2024044169A1
Application number: PCT/US2023/030796
Authority: WO
Inventors: Stuart Simpson
Original assignee: Think Surgical Inc
Current assignee: Think Surgical Inc
Priority date: 2022-08-24
Filing date: 2023-08-22
Publication date: 2024-02-29
Anticipated expiration: 2025-02-24

Abstract

A computerized method for determining a bone resection level and implant pairing is provided. A first resection level and a second resection level are selected to define first and second bone perimeter data relative to bone image data of a subject bone, respectively. Perimeter data of an implant is virtually positioned at locations for the first resection level and separately at the second resection level. The best fit position of the implant at the first and second resection levels are determined. The best fit position of the implant at the first resection level and the second resection level are compared to determine which resection level is a best pairing for the implant.

Description

METHOD FOR DETERMINING OPTIMAL BONE RESECTION

RELATED APPLICATIONS

[0001] This application claims priority benefit of US Provisional Application Serial Number 63/400,424 filed 24 August 2022; the contents of which are hereby incorporated by reference.

TECHNICAL FIELD

[0002] The present invention generally relates to computer-assisted surgery, and more particularly to a surgical planning system and method to determine an optimal location for resecting a bone for implant selection and placement in joint arthroplasty procedures.

BACKGROUND

[0003] Throughout a lifetime, bones and joints become damaged and worn through normal use, disease, and traumatic events. Arthritis is a leading cause of joint damage that over time leads to cartilage degradation, pain, stiffness, and bone loss. Arthritis can also cause the muscles articulating the joints to lose strength and become painful.

[0004] If the pain associated with the dysfunctional joint is not alleviated by less-invasive therapies, a joint arthroplasty procedure is considered as a treatment. Joint arthroplasty is an orthopedic procedure in which an arthritic or dysfunctional joint surface is replaced with prosthetic components, also referred to herein as implants or implant components.

[0005] The accurate placement and alignment of an implant is a large factor in determining the success of joint arthroplasty. Computer- assisted surgical systems are particularly useful for joint arthroplasty procedures requiring this dexterity, precision, and accuracy. For example, the TSolution One® Surgical System (THINK Surgical, Inc. Fremont, CA) as shown in prior art FIG. 1, aids in the planning and execution of total joint arthroplasty illustratively including total hip arthroplasty (THA) and total knee arthroplasty (TKA). The Tsolution One® preoperative planning software permits a user to pre-operatively plan the position and orientation (POSE) of a desired bone implant (e.g., hip or knee implants) relative to three-dimensional (3- D) bone models of the patient. In the operating room, the surgical robot 100 precisely removes bone to form surfaces (“resected surfaces” or “resection surfaces”) on the remaining bone that contact one or more contact surfaces of the implant in the POSE planned by the surgeon. The surgical robot 100 generally includes a base 102, a manipulator arm 104 attached to the base, and an end-effector 106 which is actuated or controlled by the manipulator arm 104 as instructed by cutting instructions, which may be provided as part of the surgical plan. The manipulator arm 104 includes various links, joints, and sensors (e.g., encoders) to accurately actuate the end-effector 106, where the sensors can further provide feedback as to the exact position of the end-effector 106 in space. The end-effector 106 may be, for example, a tool having a tool tip 108, such as a burr, saw, or end mill cutter. The surgical robot 100 may further include a mechanical digitizer arm 1 10 for registering the bone, a monitor 1 12 to display a graphical user interface to provide workflow instructions to the user, as well as input mechanisms (not shown) for the user to interact with surgical robot 100.

[0006] Total knee arthroplasty (TKA) is a surgical procedure in which the articulating surfaces of the distal femur and proximal tibia of the knee joint are replaced with implant components. FIG. 2 A illustrates an example of a tibial implant component 114, and a tibia T with bone removed forming a proximal resection surface 123 and keel receiving features (124, 126). The tibial implant component 114 generally includes a base plate 116 and a keel 118, where the keel 118 includes a keel post 120 and keel wings 122. The contact surface on the underside of the base plate 116 contacts the proximal resection surface 123, and the keel post 120 and keel wings 122 are placed in the keel receiving features (124, 126) formed on the tibia T to mount the tibial implant component 114 on the remaining bone of the tibia T. FIG. 2B illustrates an example of a femoral implant component 130, and a femur F with bone removed to form an anterior resection surface 132, an anterior chamfer resection surface 134, a distal resection surface 136, a posterior chamfer resection surface 138, and a posterior resection surface 140. The femoral implant component 130 includes an articulating surface 142, and a plurality of contact surfaces (e.g., a posterior contact surface 144) for contacting the resection surfaces (132, 134, 136, 138, and 140) to mount the femoral implant component 130 on the remaining bone of the femur ‘F’. The femoral implant component 130 may include additional features (e.g., pegs, box) for placement in corresponding features formed on the remaining bone of the femur F. One of the overall goals of a TKA procedure is to position the tibial implant components (i.e., tibial implant component 114 and tibial liner) and femoral implant component 130 on the remaining bone of the tibia T and femur F, respectively, to restore the mechanical axis or kinematics of the patient’s leg while preserving the balance of the surrounding knee ligaments.

[0007] Conventionally, surgeons plan and form the resection surfaces based on their experience and personal review of images of the patient’s bone. A surgeon chooses a resection level, removes bone at the resection level to form the resection surface, and then selects an implant having a particular implant size that is best suited for that resection level. However, as the bone has already been irreversibly cut, this approach can lead to a situation where the surgeon chooses between an implant that is too small or too big, both of which are not optimal and have significant consequences for the patient. Even with the use of conventional preoperative planning software as described above, the surgeon planned resection level can be rather subjective and not necessarily “optimal,” which can lead to misalignment, poor patient outcomes, implant wear, and the possibility for revision surgery. Even small implant alignment errors outside of clinically acceptable ranges correlate to significantly worse outcomes and increased rates of revision surgery. In still other instances, there may be regions of the target bone that have imperfections, such as healed fractures or subnormal density, which is a factor not typically considered in planning the placement and sizing of an implant. Furthermore, given that there are so many variables to consider when determining an “optimal” resection for a bone, including at least the factors of depth of cuts, angle of cuts, amount of cortical bone to be cut, amount of spongious bone to be cut, symmetry of the resected bone, etc., it is impossible for a surgeon to efficiently consider every possible resection option and determine the optimal plan.

[0008] Thus, there exists a need for a system and method to determine an optimal location for resecting a bone to ensure an optimal fit between the resulting resected surface and an implant in a time efficient manner. There is a further need to account for remaining cortical bone and spongy bone when determining the optimal location for resecting the bone.

SUMMARY OF THE INVENTION

[0009] A computerized method for determining a bone resection level and implant pairing is provided. A first resection level is selected to define a first bone perimeter data relative to bone image data of a subject bone. A first implant is selected having a first characteristic. Perimeter data of the first implant is virtually positioned at a first plurality of locations of the first bone perimeter data of the first resection level. The perimeter data of the first implant is compared to the first bone perimeter data at the first plurality of locations of the first bone perimeter data of the first resection level to determine a first best fit position of the perimeter data of the first implant relative to the first bone perimeter data. A second resection level is selected to define a second bone perimeter data relative to the bone image data of the subject bone. The perimeter data of the first implant is virtually positioned at a second plurality of locations the of the second bone perimeter data of the second resection level. The perimeter data of the first implant is compared to the second bone perimeter data at the second plurality of locations of the second bone perimeter data of the second resection level to determine a second best fit position of the perimeter data of the first implant relative to the second bone perimeter data. The first best fit position is compared to the second best fit position to determine which of the first resection level and the second resection level is a best pairing for the first implant.

BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The present invention is further detailed with respect to the following drawings that are intended to show certain aspects of the present of invention, but should not be construed as limit on the practice of the invention, wherein:

[0011] FIG. 1 depicts a prior art surgical robot configured to assist with total joint replacement procedures;

[0012] FIG. 2A shows a prior art example of a tibial implant component and a tibia with bone removed for mounting contact surfaces of the tibial implant component on the remaining bone of the tibia;

[0013] FIG. 2B shows a prior art example of a femoral implant component and a femur with bone removed for mounting contact surfaces of the femoral implant component on the remaining bone of the femur;

[0014] FIG. 3 shows a flowchart of a method for determining an optimal location to form a resection surface on a bone for mounting an implant thereon, according to inventive embodiments of the present invention;

[0015] FIG. 4 depicts a bone model of a tibia and a plurality of resection levels according to certain embodiments of the present invention;

[0016] FIGS. 5A-5C show a first, second, and third bone perimeter data, respectively, resulting from resections made at each of the plurality of resection levels of FIG. 4; [0017] FIGS. 6A-6C show a first implant perimeter data positioned and oriented at a plurality of locations relative to a first bone perimeter data according to certain embodiments of the present invention;

[0018] FIGS. 7A-7C show a second implant perimeter data positioned and oriented at a plurality of locations relative to a first bone perimeter data according to certain embodiments of the present invention;

[0019] FIG. 8 depicts an embodiment of a surgical planning system according to certain embodiments of the present invention, where the dashed lines denote display boundaries;

[0020] FIG. 9 shows an example of an output of the inventive method when two optimal pairing solutions are determined, where the dashed lines denote display boundaries;

[0021] FIG. 10 illustrates implant perimeter data for two different implants sizes being compared to bone perimeter data at two different resection levels and fitness scores for different configurations of implant sizes, locations, and resection levels according to certain embodiments of the present invention;

[0022] FIG. 11 shows a flowchart of a method for determining an optimal location to form a resection surface on a bone for mounting an implant thereon, according to inventive embodiments of the present invention;

[0023] FIG. 12 shows a hip joint to which embodiments of the inventive method are applied;

[0024] FIG. 13 depicts a model of a femoral implant component positioned related to a femoral bone model according to certain embodiments of the present invention.

[0025] FIG. 14 depicts a bone model of a hip joint and a plurality of resection levels according to certain embodiments of the present invention;

[0026] FIGS. 15A-15C show cross sectional views of a plurality of resection levels of a femoral canal; [0027] FIG. 16 depicts a collared femoral hip implant according to certain embodiments of the present invention;

[0028] FIG. 17 depicts a collared femoral hip implant model positioned with respect to a femoral bone model according to certain embodiments of the invention.

DETAILED DESCRIPTION

[0029] The present invention has utility as an improved system and method to optimally plan an arthroplasty procedure, and more particularly to determine an optimal location for resecting a patient’s bone to form a resection surface that ensures an optimal fit between the resected surface and an implant prior to irreversible cuts made to the bone. In still other embodiments, the determination of the optimal location for the resection surface accounts for exposed cortical bone and spongious bone to further improve the fit between the resected surface and the implant. Accordingly, the present invention represents a paradigm shift in that rather than fitting the implant to the resected surface, the present invention identifies an optimal location for forming a resection surface, and may further identify the best implant and implant location for mounting on the resection surface. Some advantages identified in the present invention relative to the prior art include: providing one or more optimal resection levels for a patient’s unique bone geometry, structure (e.g., location of cortical bone vs. spongious bone), and quality (e.g., bone density, porosity); the best implant from among a library of implants as to implant size and/or brand; and a location for mounting the implant on the one or more optimal resection levels that results in the best fit. The inventive benefits manifest as superior initial placement and longevity of the implant, compared to the conventional procedure of planning and resection.

[0030] The present invention will now be described with reference to the following embodiments. As is apparent by these descriptions, this invention can be embodied in different forms and should not be construed as limited to the embodiments set forth herein. For example, features illustrated with respect to one embodiment can be incorporated into other embodiments, and features illustrated with respect to a particular embodiment may be deleted from the embodiment. In addition, numerous variations and additions to the embodiments suggested herein will be apparent to those skilled in the art in light of the instant disclosure, which do not depart from the instant invention. Hence, the following specification is intended to illustrate some particular embodiments of the invention, and not to exhaustively specify all permutations, combinations, and variations thereof.

[0031] Furthermore, it should be appreciated that although the systems and methods described herein provide examples with reference to total knee arthroplasty, the systems and methods of the present invention are applicable to other surgical procedures that may benefit from the identification of an optimal location for an implant with respect to a bone. Examples of such surgical procedures include: total and partial joint replacement (e.g., replacement of the hip, shoulder, and ankle joints) as well as revision of initial repair or replacement of any joints or bones; unicompartmental arthroplasty; bone fracture repair; maxillofacial; oral surgery; craniotomies; and spinal reconstruction.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0033] All publications, patent applications, patents and other references mentioned herein are incorporated by reference in their entirety.

[0034] Unless indicated otherwise, explicitly or by context, the following terms are used herein as set forth below. [0035] As used in the description of the invention and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0036] Also, as used herein, “and/or” refers to and encompasses any and all possible combinations of one or more of the associated listed items, as well as the lack of combinations when interpreted in the alternative (“or”).

[0037] As used herein, the term “bone image data” refers to two-dimensional (2-D) or three- dimensional (3-D) images of a bone, which may include one or more of the following: an image data set of one or more bones (e.g., an image data set acquired via computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, x-ray, laser scan, etc.); three-dimensional (3- D) bone models, which may include a virtual generic 3-D model of the bone, a physical 3-D model of the bone, a virtual patient-specific 3-D model of the bone generated from an image data set of the bone; and a set of data collected directly on the bone intra-operatively commonly used with imageless CAS devices (e.g., laser scanning the bone, painting the bone with a digitizer to generate a point cloud of the bone).

[0038] As used herein, the term “implant perimeter data” refers to any representation of the geometric perimeter, periphery, or boundary of at least a portion of an implant. The implant perimeter data may include a numerical value of the perimeter and may also include the shape of the perimeter (e.g., a 2-D perimeter, or a 2-D perimeter of a 3-D shape (e.g., a 2-D perimeter of a 3-D virtual model of an implant provided in the software)). In particular embodiments, the “implant perimeter data” corresponds to the two-dimensional perimeter of a contact surface of an implant configured to mount to a resection surface. For example, the implant perimeter data may be the 2-D perimeter of the contact surface residing on the underside of the base plate 116 of the tibial implant component 114 shown in FIG. 2A or the posterior contact surface 144 of a femoral implant component 130 shown in FIG. 2B. [0039] As used herein, the term “bone perimeter data” refers to the geometric perimeter, periphery, or boundary of a resection surface of a bone. The bone perimeter data may include a numerical value of the perimeter and may also include the shape of the perimeter. In particular embodiments, the “bone perimeter data” corresponds to a 2-D perimeter of a bone resection surface defined by a resection level of a bone. The “bone perimeter data” may be obtained via 2-D images from an image data set of the bone (e.g., 2-D CT slices), or by virtually resecting a 3-D bone model using traditional computer-aided design (CAD) techniques. If a 3-D bone model is virtually resected, then the perimeter of the resulting resection surface may be used in the computations described herein. It should be appreciated, that the remaining 3-D bone model following the virtual resection may be cropped, discarded, and/or an outline software tool may be used to outline or identify the perimeter of the resection surface to obtain the bone perimeter data, any of which may be used to reduce computational load.

[0040] As used herein, the terms “computer-assisted surgical device” and “CAS device” refer to devices used in surgical procedures that are at least in part assisted by one or more computers. Examples of CAS devices illustratively include tracked/navigated instruments and surgical robots. Examples of a surgical robot illustratively include robotic hand-held devices, serial-chain robots, bone mounted robots, parallel robots, or master-slave robots, as described in U.S. Patent Nos.: 5,086,401; 6,757,582; 7,206,626; 8,876,830; and 8,961,536; U.S. Patent Publication No. 2013/0060278; and PCT Patent Publication Nos.: PCT/US2021/031703; and PCT/US2020/062686; which patents and patent application are incorporated herein by reference. The surgical robot may be active (e.g., automatic/autonomous control), semi-active (e.g. a combination of automatic and manual control), haptic (e.g., tactile, force, and/or auditory feedback), and/or provide power control (e.g., turning a robot or a part thereof on and off). The terms “computer-assisted surgical system” and “CAS system” refer to systems utilizing a CAS device and any other computers, software, or devices to assist in a surgical procedure. An example of a CAS system may include: i) one or more CAS devices; a CAS device and software used by the CAS device (e.g., cutting instructions); iii) planning software for determining an optimal location for resecting a bone; and iii) any of the aforementioned with additional devices or software (e.g., a tracking system, tracked/navigated instruments, tracking arrays, bone pins, a rongeur, an oscillating saw, a rotary drill, manual cutting guides, manual cutting blocks, manual cutting jigs, etc.).

[0041] As used herein, the term “cutting instructions” refer to software instructions that direct a CAS device during formation of one or more resection surfaces on a bone. Cutting instructions may further include other instructions, such as instructions for directing the CAS device during formation of bone cuts for stabilizing features of implants (e.g., pegs, boxes, keels). Examples of “cutting instructions” include a cut-file, virtual boundaries, or virtual paths. A “cut-file” may include instructions (e.g., end-effector paths, points, orientations, feed rates, or spindle speeds, and any combination thereof as well as other factors) that direct the CAS device during the formation of the resection surfaces on the bone automatically. For example, a surgical robot may execute instructions in a cut-file to automatically control movement of an end-effector (e.g., an end-mill, cutter, burr, oscillating saw, reciprocating saw, laser, bone pin, waterjet, forceps, reamer, impactor, or any other tool that interacts (e.g., applies energy) with a workpiece). It should be appreciated that cut-files may be generated with the aid of computer-aided manufacturing (CAM) software using geometry data of an implant, a bone, or a combination thereof to assist in defining the locations for instructions (e.g., the locations of the cut paths). Alternatively, the “cutting instructions” may be virtual boundaries defined relative to the bone which direct a CAS device to provide feedback (e.g., active, semiactive, haptic, or power control) to a user to assist in the prevention of cutting bone beyond the virtual boundary while the user maneuvers an end-effector of the CAS device during the formation of the resection surfaces. The “cutting instructions’ may be virtual paths defined relative to the bone position, which direct a CAS device to provide feedback (active, semiactive, haptic, or power control) to a user to assist in maintaining an end-effector of the CAS device along the virtual path while the user maneuvers the end-effector during the formation of the resection surfaces. In other embodiments, a CAS device may be controlled by “operating instructions” to maintain alignment of an axis of a tool (e.g., bone pin, burr) or an end of the tool aligned with virtual coordinates (e.g., virtual planes, axes, points, surfaces, curves) for forming a resection surface or for aligning a cutting guide with respect to the bone as described in U.S. Pat. No. 11,284,946 and U.S. Pat. App. No. 15/778,811.

[0042] Also referenced herein is a “surgical plan”. A surgical plan is generated, either pre- operatively or intra-operatively, using planning software. The planning software may be programmed to execute embodiments of the methods described herein. The planning software may be used to generate three-dimensional (3-D) models of the patient’s bones from image data sets of the patient’s bone acquired from computed tomography (CT), magnetic resonance imaging (MRI), x-ray, ultrasound, or from a set of points collected on the bone intra- operatively. The planning software may further include various software tools to allow a user to: (i) plan or adjust a POSE of an implant model with respect to a bone model; (ii) compare surgical plans; (iii) review patient data; and (iv) perform other actions as further described below. The generated surgical plan may include the planned POSE for one or more implant components with respect to one or more bone (e.g., a planned POSE of an implant model with respect to a bone model), registration data (e.g., a 3-D bone model with digitizing landmarks to facilitate the registration), the locations of one or more optimal resection surfaces to be formed on the bone as determined by the methods described herein, and optionally include cutting instructions or virtual coordinates for a CAS device to assist in the formation of the one or more resection surfaces on the bone such that those resection surfaces are made in the determined/planned POSE. [0043] As used herein, the term “optimal” refers to a resection plane (or other bone cuts) and/or placement of an implant that approaches an idealized position based on a user defined standard that illustratively includes a subjective standard, a best fit per linear regression techniques based on Ist-order and/or 2nd-order polynomials, output of a machine learning model, or 3D parametric intensity models, or a combination thereof; regardless of whether it is weighted for certain parameters or unweighted.

[0044] Embodiments of the present invention utilize an iterative process to compare perimeter data of different implant sizes and/or implant brands to bone perimeter data defined at different resection levels. The comparison may include a computation of coverage parameters for each implant perimeter data positioned relative to the bone perimeter data defined at each resection level to determine a goodness of fit between the implant perimeter data and the bone perimeter data. The computed coverage parameters with the best results for each resection level are compared to identify the optimal resection level. In certain embodiments an optimal resection level is identified to optimize the final placement for an implant with respect to the patient’s joint. According to some inventive embodiments, the optimal resection level is determined for a tibia bone to provide an optimal location for mounting a tibial implant component on a resection surface formed at the optimal resection level. According to other inventive embodiments, one or more optimal resection levels are determined for a femur to provide an optimal location for mounting a femoral implant component on a resection surface formed at the optimal resection level.

[0045] With reference now to the figures, FIG. 3 shows a flowchart of a method 200 for determining an optimal location for a resection surface to be formed on a bone for mounting an implant thereon, according to some inventive embodiments of the present invention. The method 200 includes software for receiving bone image data at step 202, such as CT data of the patient’s bone or a 3-D model of the patient’s bone generated from the CT data. At step 204, a first resection level 300 is selected at a location on the bone image data to define a first bone perimeter data 306, where the first resection level 300 and first bone perimeter data 306 are shown in FIG. 4 and FIG. 5A. Alternate resection levels 302 and 304 are shown in FIG. 4 and correspond to bone perimeter data 308 and 310 shown in FIGs. 5B and 5C, respectively. The selection of a resection level is done automatically by the software as further described below. The resection level may be selected using 2-D images (e.g., CT image slices) from an image data set of the patient’s bone or by virtually resecting a 3-D virtual bone model of the patient’s bone at the selected resection level. If 2-D images are used to make the selection, the perimeter of the resection surface corresponding to the selected resection level may be determined using conventional image processing techniques such as image segmentation.

[0046] The method 200 continues at step 206 by comparing perimeter data 312 of a first implant, also referred to herein as first implant perimeter data 312, to the first bone perimeter data 306 defined by the first resection level 300 at a plurality of locations. The first implant perimeter data 312 may correspond to the geometric perimeter of at least a portion of a first implant, where the first implant has a particular implant geometry, implant size, and/or is a particular implant brand (e.g., a tibial implant component, size ‘x’, manufactured by company ‘y ’). The positioning of the first implant perimeter data 312 relative to the first bone perimeter data 306 at a plurality of locations is illustrated with reference to FIGs. 6A-6C, where each location for the implant perimeter data positioned relative to the bone perimeter data may be referred to as a “location configuration”. FIG. 6A shows a first location configuration with the first implant perimeter data 312 positioned at a first location relative to the first bone perimeter data 306, FIG. 6B shows a second location configuration with the first implant perimeter data 312 positioned at a second location relative to the first bone perimeter data 306, and FIG. 6C shows a third location configuration with the first implant perimeter data 312 positioned at a third location relative to the first bone perimeter data 306. The virtually positioning may include translation (medial-lateral “M-L” translation, anterior-posterior “A-P” translation) and rotation (internal-external “I-E” rotation) of the implant perimeter data relative to the bone perimeter data to compute the best coverage for the implant relative to a resection surface in all of the relevant degrees-of-freedom. The software may be programmed to virtually position the first implant perimeter data 312 relative to the first bone perimeter data 306 at each of the plurality of locations, which may be performed by the software in the background or shown visually on a graphical user interface (GUI). It is appreciated that virtual positioning occurs in silico and that besides a GUI, the visual display may also be provided on a computer or television (TV) monitor, a holographic display, a mobile display, a smartphone display, a video wall, a head-mounted display, a heads-up display, a virtual reality headset, a broadcast reference monitor, any of the aforementioned with a touchscreen capability, and a combination thereof. One or more computers comprising a processor may be operatively coupled to the display for controlling the output of the display.

[0047] In particular embodiments, the software may be programmed to automatically position the first implant perimeter data 312 relative to the first bone perimeter data 306 so as to optimize the position of the first implant perimeter data 312 with respect to the first bone perimeter data 306. The automatic positioning may utilize a best fit or least square algorithm to minimize the sum of errors 314 between the two perimeters, as shown in FIGS. 6A-6C. An exemplary least square method operative in the present invention includes that detailed in V. Ibero-American Symposium in Computers Graphics - S1ACG 2011, pgs. 201-207. A 3D parametric intensity modelling method operative herein includes that detailed in S. Worz et al. Medical Image Analysis, 10(1), 2006, pages 41-58, For each location configuration, a comparison is made to compute one or more coverage parameters. The coverage parameters may include at least one of the following computations: (a) an amount (e.g., percentage) of overlap between the area of the first implant perimeter data 312 and the area of the first bone perimeter data 306; (b) the minimized sum of errors 314 between the first implant perimeter data 312 and the bone perimeter data 306, where each error (314a, 314b) from a plurality of errors 314 may be calculated as a distance/difference between a first point on the first implant perimeter data 312 and a second point on the first implant perimeter data 306, where a line connecting the first point and the second point is normal to the curvature of at least one of the first implant perimeter data 312 or the first bone perimeter data 306; (c) the standard deviation of the errors 314 between the first implant perimeter data 312 and the first bone perimeter data 306; (d) the average of the errors 314 between the first implant perimeter data 312 and the bone perimeter data 306; and/or (e) other statistical parameters (e.g., sum of squared errors) indicating a goodness of fit. The computed coverage parameters are used to evaluate how well the first implant, and a particular location for the first implant (in both translation and rotation), will fit with respect to a resection surface that will be formed at a particular resection level. One or more computed coverage parameters either alone, or in combination, may be used as a fitness score for this evaluation. The output of the comparison at step 206 may include: a plurality of location configurations and the corresponding computed coverage parameters for each location configuration. The outputs may further include those exemplified and described with reference to FIG. 9.

[0048] In specific embodiments, the software may automatically position the first implant perimeter data 212 at a plurality of locations relative to the first bone perimeter data 206 using a best fit algorithm while also being constrained by optional criteria 218 provided as an input in step 206 of the method 200 shown in FIG. 3. For example, a first criterion may include constraining the internal-external rotation T-E’ of the first implant perimeter data 312 relative to the first bone perimeter data 306 from 0° - 5° from the medial tibial tuberosity ‘TT’. Another criterion may restrict or limit the amount of overhang of the first implant perimeter data 312 in the anterior-posterior direction ‘ A-P’ or medial-lateral direction ‘M-L’ relative to the first bone perimeter data 306. A third criterion may limit the positioning of the geometric center or center of mass of the first implant perimeter data 312 relative to the geometric center or center of mass of the first bone perimeter data 306. For example, the geometric center of the first implant perimeter data 312 may not deviate by more than a threshold amount from the geometric center of the first bone perimeter data 306. A fourth criterion may account for an amount and location of cortical bone vs. spongious bone. The perimeter data may include bone density information that approximates cortical (shaded) relative to spongious bone as shown in FIG. 5A. Such shading is omitted from FIGs. 5B and 5C for visual clarity. The criteria 218 may be selected by a user or pre-defined in the software.

[0049] Examples of other criteria 218 that may be provided at step 206 includes: (a) internalexternal rotational constraint as described above; (b) anterior-posterior translational constraints (e.g., implant perimeter data should not overhang more than ‘x’ mm in anterior-posterior direction ‘A-P’ relative to the bone perimeter data); (c) medial-lateral translational constraints (e.g., implant perimeter data should not overhang more than ‘x’ mm in medial-lateral direction ‘M-L’ relative to bone perimeter data); (d) locations where overhang or underhang is acceptable or unacceptable (e.g. overhang of implant perimeter data is acceptable posterior of the lateral condyle of the bone perimeter data, or overhang of the implant perimeter data is unacceptable anterior of the medial condyle of the bone perimeter data); (e) geometric center or center of mass constraints as described above; (f) interaction with bone type or quality (e.g., at least 20% of the area of the implant perimeter data must overlap with cortical bone; a specified area of the implant perimeter data, such as particular load-bearing area, overlaps with at least 80% cortical bone as shown in the shaded area of FIG. 5A); (g) the identification and avoidance of particular honey structures or soft tissue (e.g., no area of the implant perimeter data can overlap with a specified region on the bone perimeter data due to ligament location, presence of osteophytes, or particularly weak or low density bone); and (h) implant type (e.g., mobile bearing implants, implants with particular fixation features), implant brand, and/or implant placement constraints to account for patient specific factors (e.g., selection of a particular implant or limits to the implant placement due to a patient’s gender, body mass index (BMI), age, previous surgeries, bone quality, bone type, and medical history including the presence/absence of a disease).

[0050] For criterion having a range of values or thresholds, the software determines each location configuration by iterating through each value in the range and computes the corresponding coverage parameters. For example, if a criterion limits internal-external rotation ‘I-E’ from 0° - 5° from the medial tibial tuberosity ‘TT’ , the software determines a first location configuration with the first implant perimeter data 312 oriented at 0° from the tibial tuberosity ‘TT’, a second location configuration with the first implant perimeter data 312 oriented at 1° from the tibial tuberosity ‘TT’, and so on up to 5° from the tibial tuberosity ‘TT’. At each of these location configurations, the coverage parameters are computed.

[0051] For each location configuration, the first implant perimeter data 312 is compared to the first bone perimeter data 306 to compute one or more of the aforementioned coverage parameters. After the coverage parameters for each location configuration is computed, the software identifies which location configuration has the best computed coverage parameters, or those coverage parameters that satisfy pre-defined coverage criteria (e.g., coverage area is >99%, sum of errors < “x” mm), and that which further satisfies all of the criteria 218. For example, a first location configuration for implant ‘A’ may have a coverage area of 90% with a minimized sum of errors of 10 mm, and a second location configuration for implant ‘A’ may have a coverage area of 92% with a minimized sum of errors of 6 mm, where the planning software compares the two coverage parameters and selects the second location configuration as having the best computed coverage parameters since the coverage area and minimized sum of errors for the second location configuration is better than the computed coverage parameters for the first location configuration. The best location configuration (e.g., the second location configuration from the preceding example) and the corresponding computed coverage parameters are saved for a future comparison at step 210, along with any other outputs as described with respect to FIG. 9. In the instance where multiple solutions are found, that is the computed coverage parameters for multiple location configurations are the same, or satisfy the pre-defined coverage criteria, then all of those solutions may be saved for a future comparison at step 210.

[0052] Returning to FIG. 3, at step 208, step 206 is iteratively repeated for different implants at the first resection level 300. Each implant may differ by implant size, shape, or brand, and may be referred to herein as an implant “characteristic”. For example, implant 1 (n=0) may correspond to an implant manufactured by company ‘x’ in size 3, implant 2 (n=l) corresponds to an implant manufactured by company ‘x’ in size 4, and implant 3 (n=2) corresponds to an implant manufactured by company ‘y’ in size 3. It should be appreciated that the computational power provided by the systems described herein allows for a vast array of implant sizes and brands to be efficiently passed through these iterative processes. As described in step 206, the perimeter data of the various implants are compared to the first bone perimeter data 206 defined at the first resection level 300 to calculate the aforementioned coverage parameters. For example, after the comparison is done for a first implant size, the system steps up or down to a second implant size, and then a third and so on to iterate through various implants having different implant sizes and/or is a different implant brand. In particular embodiments, the software may stop iterating for certain implant sizes when the computed coverage parameters start trending away from an optimal solution, or the optional criteria 218 can no longer be satisfied. For example, the minimized sum of errors may continue to increase with each step- up in implant size regardless of the position of the implant perimeter data relative to the bone perimeter data, where the software may recognize this trend and forgo step 206 for any larger implant sizes. In another example, the software may recognize that a step-up in implant size, and any future iterations with a larger implant size, will result in too much overhang of the implant perimeter data relative to the bone perimeter data. In this instance, the software identifies the trend and forgoes step 206 for any larger implant sizes.

[0053] To further illustrate the process of step 208, FIGS. 7A-7C show a second implant perimeter data 316 for a second implant being positioned and compared to the first bone perimeter data 306 defined at the first resection level 300 for a plurality of locations, where FIG. 7A shows a first location configuration with the second implant perimeter data 316 positioned at a first location relative to the first bone perimeter data 306, FIG. 7B shows a second location configuration with the second implant perimeter data 316 positioned at a second location relative to the first bone perimeter data 306, and FIG. 7C shows a third location configuration with the second implant perimeter data 316 positioned at a third location relative to the first bone perimeter data 306. The same positioning and coverage parameter computations are performed as described above with reference to FIGS. 6A-6C. In FIG. 7C, the second implant perimeter data 316 is shown overhanging the first bone perimeter data 306 at the circled region 318, which may or may not be an acceptable location for the second implant perimeter data 316 depending on the optional criteria 218 selected by a user or predefined in the software. After coverage parameters are computed for each location configuration for the second implant perimeter data 316, the software identifies which location configuration has the best computed coverage parameters, or those coverage parameters that satisfy pre-defined coverage criteria (e.g., coverage area is >99%, sum of errors < “x” mm), and that which further satisfies all of the criteria 218. The best location configuration and the corresponding coverage parameters with the best results are saved for a future comparison at step 210, along with any other outputs as described with respect to FIG. 9. In the instance where multiple solutions are found, that is the computed coverage parameters for multiple location configurations are the same, or satisfy the pre-defined coverage criteria, then all of those solutions may be saved for a future comparison at step 210.

[0054] As shown in FIG. 3, at step 210, the software then compares the computed coverage parameters for the best location configurations for each implant, and then selects the implant having the best overall coverage parameters. For example, the software may have identified the first location configuration shown in FIG. 6A as having the best computed coverage parameters “CP-1” for the first implant, and identified the second location configuration shown in FIG. 7B as having the best computed coverage parameters “CP-2” for the second implant, and now the software at step 210 compares these two computed coverage parameters, “CP-1” and “CP-2”, and identifies/saves the implant and the corresponding implant configuration with the best computed coverage parameters, “CP-1” or “CP-2”, for a future comparison at step 214. In the instance where multiple solutions are found, that is two or more implants at their best location configuration have the same computed coverage parameters, or all satisfy pre-defined coverage criteria, then all of those solutions may be saved for a future comparison at step 214. [0055] Next, at step 212, the software repeats steps 204 - 210 for different resection levels 302, 304, as shown in FIG. 4. That is, at step 212 the software virtually resects the bone image data at a second 302, third 304, fourth (not shown), and Nth number resection levels to define respective bone perimeter data, such as second bone perimeter data 308 defined by the second resection level 302 and third bone perimeter data 310 defined by the third resection level 304. For each resection level (302, 304, etc.), steps 206, 208, and 210 are repeated as described above with reference to FIGs. 6 A - 7C. Optional criteria 216 may be provided during the iteration of step 204 to limit the location of the virtual resections, the number of virtual resections, the spacing between virtual resections, and the orientation (e.g., varus-valgus rotation, anterior-posterior slope) of a virtual resection. This optional criteria 216 may be constraints defined by a user (e.g., user preferences) or pre-defined in the software: Examples of the optional criteria 216 include: (i) varus-valgus angle relative to an anatomical axis of the bone (e.g., mechanical axis of the tibia, mechanical axis of the femur, mechanical axis of the hip-knee-ankle); (ii) varus-valgus angle to restore kinematics of a patient’s knee, commonly referred to as native knee alignment; (iii) anterior-posterior angle or slope (e.g., 2-4° posterior tibial slope); (iv) the size, geometry, or location of an implant component on an opposing bone of the joint (e.g., limiting the resection level in a proximal-distal location on the tibia to maintain the knee joint line when considering the geometry and location of a femoral implant component positioned on the femur); (v) a specified range in the proximal-distal (P-D) direction, which may be a user preference or pre-defined in the software (e.g., between 1 millimeter (mm) and 20 mm from the native tibial plateau at 1 mm intervals; between 1 millimeter (mm) and 20 mm from the most proximal portion of the fibula at 2 mm intervals); (vi) a specified upper and/or lower boundary (e.g., from the native tibial plateau to the most proximal portion of the fibula at 2 mm intervals); (vii) amount and/or location of cortical bone, or bone having a certain density, present at a resection level (e.g., if a resection level has less than 10% cortical bone as determined by a user or automatically by the software, then forgo this resection level and proceed to the next resection level); (viii) presence of abnormalities or soft tissues, such as osteophytes, weak bone, or ligament insertion locations (e.g., if a resection level has particularly weak bone or a particularly large osteophyte, then forgo this resection level and proceed to the next resection level); and (ix) an amount of cortical bone relative to spongious bone (e.g., if the amount of cortical bone to spongious bone is predominantly spongious bone then forgo this resection level and proceed to next resection level); and (x) implant type (e.g., mobile bearing implants, implants with particular fixation features), implant brand, and/or implant placement constraints to account for patient specific factors (e.g., selection of a particular implant or limits to the implant placement due to a patient’s gender, body mass index (BMI), age, previous surgeries, bone quality, bone type, and medical history including the presence/absence of a disease). It should be appreciated, that the software, through image processing techniques and/or machine learning, may automatically perform one or more of the aforementioned, particularly the detection of abnormalities, detection of types and quality of bone, which may be determined using Hounsfield Units from CT data or other density data derived from other imaging modalities (e.g., DEXA scans), and the identification of anatomical landmarks, planes, or other geometrical bone data (e.g., identify the native tibial plateau, identify the most proximal potion of the fibula). In instances where the software is configured to detect abnormalities, the software may include an abnormality threshold, beyond which the software will not virtually position implant perimeter data in the region of the abnormality on the bone perimeter data. This abnormality threshold may be adjusted (e.g., turned up or down) by the user based on their comfortability when dealing with such abnormalities. It is appreciated that abnormality weighting is added to the fitting algorithm when assessing optimal fit.

[0056] The output of step 212 of the method 200 may include a plurality of resection levels from the iteration, and for each resection level (300, 302, 304, etc.) there is at least one identified implant and location configuration having the best computed coverage parameters. For example, the software may determine that: (a) implant ‘A’ (e.g., implant size 4 manufactured by company ‘z’), positioned at location configuration ‘x’, with computed coverage parameters “CP-3” has the best overall fit on the first bone perimeter data 306 defined by the first resection level 300; and (b) implant ‘B’ (e.g., implant size 5 manufactured by company ‘h’), positioned at location configuration ‘y’, with computed coverage parameters “CP-4” has the best overall fit on a second bone perimeter data defined by the second resection level 302; and so on for each resection level. Accordingly, the inventive method is iterative, finding the best implant and implant location configuration, for each of the plurality of resection levels. The optimal resection level is then determined at step 214. The software is programmed to compare the computed coverage parameters from the output of step 212 and identify/select the resection level, implant, and implant configuration with the best overall coverage parameters. Continuing with the above example, the software compares the computed coverage parameters “CP-3” for implant ‘A’ on the first resection level 300 and the computed coverage parameters “CP-4” for implant ‘B’ on the second resection level 302, and identifies/selects the resection level and corresponding implant having the best overall computed coverage parameters. If, for instance, the computed coverage parameters “CP-4” for implant ‘B’ on the second resection level 304 is superior to that computed coverage parameters “CP-3”, then the output of step 214 may include the recommendation to resect the tibia T at the second resection level 302 and to mount implant ‘B’ on the resulting resection surface. In the instance where multiple solutions are found, that is two or more computed coverage parameters are the same, or all satisfy a pre-defined coverage criteria, then the method provides both options to a user on a user interface (UI), such as a graphical user interface (GUI) for the user to review and make the ultimate selection as further described with reference to FIG. 9.

[0057] In certain embodiments, the optimal fit is considered to be a resection level and implant pairing with the best coverage, i.e. best coverage of the bone perimeter data with implant perimeter data that satisfies all of the constraints selected or pre-defined in the software, including the optional criteria 216 and 218. According to other inventive embodiments, a best pairing of an implant to a resection level includes a consideration of the amount of cortical and spongious bone that will be cut away at a given resection level and how much of the cortical bone and spongious bone will remain after cutting of the bone at a given resection level and how that remaining bone will interact with a selected implant. An additional optimal factor is a balance of cortical bone on opposite sides of a given implant position to promote uniform load bearing and ultimately wear-related longevity of the implant. [0058] With reference to FIG. 8, a GUI is shown as part of a software planning system that operates surgical planning software configured to execute the inventive methods described herein. FIG. 8 depicts an embodiment of a surgical planning system 400. The planning system 400 includes a computer 402, user-peripherals 404, and a monitor displaying a graphical user interface (GUI) 406. The computer 402 includes a processor 408 operatively coupled to nontransient memory 410 and operating planning software to execute the inventive methods described herein. The user peripherals 404 allow a user to interact with the GUI 406 and may include user input mechanisms such as a keyboard and mouse, or the monitor may have touchscreen capabilities. The GUI 406 may include a three-dimensional (3-D) view window 412, a view options window 314, a patient information window 416, an implant library window 418, a workflow- specific tasks window 420, and constraint selections or planning preferences window 422. Each GUI window can be summarized as follows. The 3-D view window 412 allows the user to view and interact with images (e.g., 3-D bone models, 3-D implant models). The view options window 414 provides widgets to allow the user to quickly change the view of the images, anatomical landmarks, or anatomical references (e.g., mechanical axes, distal condylar plane, posterior condylar plane, tibial plateau plane). The patient information window 516 displays the patient’s information such as name, identification number, gender, surgical procedure, and operating side (e.g., left femur, right femur). The implant library window 418 may provide a drop-down menu to allow the user, or planning software, to select a particular implant (e.g., manufacturer A’s implant in implant size 2) from a library of implants, and upon selection of an implant image of the selected implant is display in the 3-D view window 412. The workflow-specific tasks window 420 includes various widgets to provide several functions illustratively including: guiding the user throughout different stages of the planning procedure; providing tools to adjust the POSE of an implant image with respect to a bone image in desired clinical directions (e.g., medial-lateral direction, flexion-extension rotation, proximal-distal direction); displaying measured values of the alignment and position of the implant image with respect to the bone image (e.g., implant image is 2° from the mechanical axis); and displaying a summary of the surgical plan. The constraints selections or planning preferences window 422 allows the surgeon to input optional criteria 216 and/or 218 to be used by the software when positioning the implant perimeter data relative to bone perimeter data and the locations/orientation for virtually resecting the bone image data. It should be appreciated that not all of the above windows be present in the planning software or viewable on the GUI 406. [0059] FIG. 9 depicts an example of the output from the software provided on the GUI 406 where two solutions were determined to be equally optimal. For example, the software may have outputted surgical plan A and surgical plan B from step 214 of method 200 because the two surgical plans have the same computed coverage parameters (e.g., 95% coverage area with a minimized sum of errors of 10 mm) while satisfying all the other constraints 216, 218. Examples of outputs (504a, 504b) are shown for each surgical plan. The outputs (504a, 504b) may include: (a) the optimal resection level identified by the inventive method 200, which may be provided as an amount of proximal-distal resection to be made on the medial and lateral sides of the bone; (b) the internal-external rotation angle, which may correspond to the best location configuration identified in the method 200; (c) the coronal alignment (e.g., neutral mechanical axis, native alignment) or varus-valgus angle, which may have been provided as criteria 216 or automatically identified by the software; (d) the computed coverage parameters (e.g., implant coverage area and minimized sum of errors); (e) the optimal implant identified by the method 200 described herein, which may be represented by an implant model 500 positioned with respect to a bone model, such as the tibia bone model “TM”, and may further include information regarding the implant manufacturer and implant size; (f) a suggested type, size, or brand for a second implant component that interacts with the optimal implant (e.g., an optimal tibial liner size that mounts onto the base plate of a tibial implant, where the optimal tibial liner size is chosen to maintain the joint line of the knee when considering a planned placement of a femoral implant component positioned with respect to the opposing femoral bone; a type, size, or brand of a femoral implant component opposing the optimal tibial implant component and a placement location for the femoral implant component on the distal femur); and (g) other computed parameters, which may include other computer coverage parameters or parameters associated with one or more criterion 216, 218 (e.g., an amount of overhang/underhang and the location of that overhang/underhang). The surgeon can then select surgical plan A or surgical plan B according to their preference with the aid of the provided outputs (504a, 504b). For example, a surgeon may prefer surgical plan B over surgical plan A due to their preference of using an implant manufactured by company “B” and the position for the implant results in no overhang.

[0060] The identified or selected surgical plan as a result of the inventive methods 200 may be saved and transferred to a computer-assisted surgical (CAS) device. The surgical plan may include cutting instructions to direct a CAS device during the formation of a resection surface located at the identified optimal resection level. The surgical plan may alternatively include operating instructions, such as the location for one or more virtual coordinates (e.g., a virtual plane), where a CAS device is controlled to maintain alignment of an axis of a tool (e.g., a bone pin) with the virtual coordinates for aligning a cut guide relative to the bone.

[0061] FIG. 10 illustrates another example of embodiments of the method 200 shown and described with reference to FIG. 3. At step 204, the software selects a first resection level (resection level 1 shown in column 1). The software, at step 206, compares implant perimeter data of a first implant (implant size 1 shown in row 1) relative to the bone perimeter data at the first resection level at a plurality of locations to compute coverage parameters for each location. The software then repeats the same procedure for a second implant (implant size 2 shown in row 2) at the first resection level (resection level 1 shown in column 1). This may be repeated for several different implants at step 208. The software, at step 212, selects a second resection level (resection level 2 shown in column 2) and steps 206 and 208 are repeated to compute coverage parameters for the second resection level. Column 3 is a summary of the computed coverage parameters for each location configuration and resection level, which is provided as a fitness score. The fitness score may be a single computed coverage parameter, or calculated (e.g., a weighted average) using a combination of two or more computed coverage parameters. For example, implant size 1 positioned on resection level 1 at location ‘x’ has a fitness score of 0.68. Implant size 1 positioned on resection level 2 at location ‘y’ has a fitness score of 0.62, etc. At step 214 of method 200, the software would select implant size 2 positioned on resection level 2 at location ‘y’ as the most optimal configuration since it has the best overall fitness score.

[0062] With reference to FIG. 11 , a particular embodiment of a method 220 for determining an optimal resection level is shown. The method 220 has similar steps to method 200 as shown and described with reference to FIG. 3. Specifically, steps 202, 204, 216, and 218 may be the same between method 200 and method 220. In a particular embodiment, steps 226 and 228 of method 220 are the same as described with respect to steps 206 and 208 of method 200. In a another embodiment, for steps 226 and 228 or in the optional criteria of step 218, the software may have an option for a user to select a particular implant brand and/or implant size(s) from a library of implants, or select and compare different implant brands and/or implant size(s). The software may further provide tools for the user to virtually position a selected implant at a desired location on the resection surface selected at step 204. The tools may include directional adjustment tools (e.g., up-down arrows, rotation arrows, number/text box, to move/adjust the position of the implant perimeter data relative to the bone perimeter data) and/or clinical alignment tools (e.g., a drop down menu or text/number box with options to select/provide a clinical alignment goal (e.g., neutral mechanical axis, varus-valgus alignment, etc.)). The software, at step 226, may compute coverage parameters for each user selected implant brand and implant size at each user designated location to allow the user to quickly compare the best fit for a given user configuration. At step 230, the software, or the user providing input into the software, selects the implant (e.g., a particular implant brand and implant size) having the best fit comparison. The software, or user, may also select the best implant location for the selected implant having the best fit comparison. The best fit comparison may be based off: (i) the computed coverage parameters determined in step 226; (ii) a user’ s visual assessment of the fit; or (hi) a combination thereof. Then, with the selected implant and implant location determined in step 230, the implant perimeter data for the selected implant and implant location is compared to bone perimeter data at different resection levels at step 232. The software may provide an option for the user to adjust the implant location or implant size at each resection level. For each resection level, computed coverage parameters may be calculated to determine the optimal resection level for the selected implant and implant location that has the best fit comparison at step 234. Method 200 differs slightly from method 220, in that in method 220, an optimal implant and implant location is determined first and then that optimal implant and implant location is compared to the different resection levels to determine the optimal resection level.

[0063] In a specific embodiment, after an optimal resection level, optimal implant size, and/or optimal implant location has been determined, the software may display a 2-D image (e.g., CT slice) of the resection surface at the optimal resection level(s) and either an image of the implant(s) or an image of the implant perimeter data positioned at the optimal implant location. This allows the user to visually assess the overlap of the implant with the cortical bone at the resection level to ensure there is enough overlap for the bone to fully support the implant when mounted on the resection surface. The software may further calculate this overlap and provide an overlap percentage on the display. Hip Arthroplasty

[0064] According to further inventive embodiments, the software may be configured to determine the optimal resection level for other joints, such as an optimal femoral neck resection level 606 on a femoral bone ‘F’ subject to total hip arthroplasty (THA) as shown in FIG. 12. In THA, the neck of the femur ‘F’ is resected and the femoral canal is reamed to prepare the femur ‘F’ for the insertion of the stem 602 of the femoral implant component 600. The acetabulum of the pelvis ‘P’ is reamed to prepare the acetabulum for the insertion of an acetabular cup implant component 608. In planning and preparing a hip in THA, one of the primary objectives is to maintain the HCOR 610. However, this can be particularly difficult because the remaining bone located at a femoral neck resection level can inhibit the seating of the stem 602 in the femur, which can shift the final location of the HCOR 606. While differently sized femoral head implant components 604 can be assembled to the neck of a femoral implant component 600 to re-adjust the HCOR 606, these sizes are limited, where a step in size may he too large to optimize the final location of the HCOR 606. Thus there exists a need to identify an optimal femoral neck resection level and corresponding implant components to achieve an optimal location for the HCOR 606.

[0065] Embodiments of the present invention includes the identification of an optimal location for the HCOR 610 and a neck resection level that will maintain the optimal location of the HCOR 610 when the implant components are placed in the bone. In certain embodiments, the method uses an iterative process to virtually assess the fit of different femoral implant components when virtually positioned within the femoral canal. For the implant components with the best fit, the process identifies an optimal femoral neck resection level from a plurality of different femoral neck resection levels that provides a good fit without inhibiting the seating of the stem 602 within the femoral canal. The results of the method may include an optimal neck resection level, optimal implant components, and locations for placing those respective implant components in their respective bones.

[0066] The optimal location for the HCOR 610 may be determined using bone image data and clinically established anatomical landmarks, planes, and axes. Alternatively the optimal location for the HCOR 610 may be identified intra-operatively by articulating the femur ‘F’ with respect to the acetabulum and tracking their relative positions during the articulation.

[0067] Next, with respect to FIG. 13, the method includes virtually positioning a model of a femoral hip implant component, or femoral hip implant model 612, relative to bone image data representing the femur and femoral canal, such as a femoral bone model ‘FM’. The femoral hip implant model 612 may be positioned relative to the femoral bone model ‘FM’ to satisfy one or more clinically establish alignment goals and maintain the optimal location for the HCOR 610 when the articulating surfaces of the acetabular cup implant 608 and femoral implant component 600 are coupled. Clinically established alignment goals may include clinically acceptable parameters for the anteversion angle, inclination angle, offset, and proximal-distal location affecting leg length, which may apply to the femoral implant component position, the acetabular cup implant component position, or a combination thereof. In particular embodiments, where a range of clinically acceptable parameters are provided, the software may be programmed to virtually position the implant component models at a plurality of locations relative to the bone image data and compare the goodness of fit for each location, similar to that described with respect to FIGs. 6A - 7C where the range of clinically acceptable parameters are used as optional criteria. The software may be further programmed to iterate through different sizes of femoral implant components to identify an optimal size based on a goodness of fit computation. The femoral implant model 612 may be initially positioned relative to the femoral bone model ‘FM’ to maintain the optimal location of the HCOR 610 for a neutral femoral head component 604. A neutral femoral head implant component 604 may refer to the median size of a femoral head implant component when a range of sizes are available (e.g., head implant size 5 available among a range of head implant sizes ranging from 0 to 10). For each size and location of the femoral implant model 612 positioned relative to the femoral bone model ‘FM’, a goodness of fit is computed. The goodness of fit may include one or more of the following computations: (i) an amount of fill of the femoral hip implant model 612 inside the femoral canal; (ii) minimized sum of errors between the outer surface of the femoral hip implant model 612 and the inner surfaces of the femoral canal, the errors of which are illustrated by line 614; (iii) overlap between the femoral hip implant model 612 and bone directly adjacent to the femoral canal, which may provide an indication of a good interaction fit between the femoral implant component and the bone; (iv) finite element analysis to calculate the amount and location of loads or stresses that may be experience by the bone with a particular femoral implant component and location; and (v) any other statistical parameters representing a good fit for a femoral implant component within the femoral canal. The software may be programmed to compare the goodness of fit computation for different implant sizes and/or implant brands, and for different implant locations, as described in the method 200 of FIG. 3, to identify an optimal femoral implant component and optimal implant location.

[0068] Next, with reference to FIGs. 14 - 15C, the method includes identifying an optimal neck resection level that allows the optimal femoral implant component to be fully seated in the optimal implant location relative to the bone and maintains the optimal location for the HCOR 610. One particular problem in THA is following the resection of the femoral neck, the remaining bone in the anterior-posterior direction has an indentation or concave shape that can inhibit the seating of the femoral stem in the prepared cavity. Accordingly, in some inventive embodiments, the software is programmed to virtually resect (or select a 2-D image from an image data set) the femoral neck at different resection levels (620, 622, 624) and compare the cross-section of the canal at each resection level with a cross-section of the femoral implant component located at that resection level to identify if the canal shape may inhibit the seating of the femoral implant component in the bone. FIGS. 15A - 15C show examples of the crosssection of the canal (626, 628, 630) at different resection levels (620, 622, 624), where FIG. 15A is a cross-section of the canal 626 located at resection level 620, FIG. 15B is a crosssection of the canal 628 located at resection level 622, and FIG. 15C is a cross-section of the canal 630 located at resection level 624. As seen in FIGs. 15B and 15C, the cross-section of the canal in the anterior-posterior direction (as shown by the arrows 632a and 632b) can interfere and inhibit the seating of the femoral stem 602 due to the concave shape of the canal when resecting the femoral neck farther down the bone (i.e., laterally and/or distally). The software compares the cross-sections and determines if any such interference will occur. In particular embodiments, the comparison may include computing an amount (e.g., percentage) of overlap between the cross-sections, where too much overlap of the cross-section of the femoral implant component beyond the cross-section of the canal indicates a potential seating issue. The software may include a pre-defined threshold amount of acceptable overlap, or a user may define and/or adjust the threshold. In a particular embodiment, the patient’s bone quality may also evaluated. If the patient has very hard cortical bone surrounding the crosssection of the canal, then the threshold amount of acceptable overlap may by decreased since the hard cortical bone is more likely to inhibit seating. If the bone quality is poor, then the threshold amount of overlap may be increased since the femoral implant component may crush the bone surrounding the cross-section of the canal and create a better interaction/interference fit. The above computations may be done for a plurality of resection levels, where the software iterates through each resection level and performs the cross-section comparisons. Optional criteria may be provided by a user or pre-defined in the software for defining the locations, orientations, and spacing between resection levels as described above. The software may be further programed to stop iterating for new resection levels when the computations are trending away from an optimal solution. It should be appreciated that if multiple solutions are found, then those solutions may be displayed for selection by a user, similar to that as described with reference to FIG. 9.

[0069] With reference to FIG. 16, a collared femoral hip implant 600 is shown having a collar 607 extending from the base of the neck 605. Collared femoral hip implants 600 may provide better loading on the femur to reduce stress shielding and/or osteoporosis that may otherwise cause implant loosening or implant subsidence. The ideal loading of the implant 600 on the femur is achieved when the final position of the femoral hip implant 600 when placed in the femoral cavity results in the collar being positioned against the remaining bone located at the neck resection level. This is difficult to achieve with traditional planning and surgical techniques. For one, the size of the femoral hip implant 600 can’t be too large, which might inhibit the seating of the stem in the femoral canal (i.e., too much interference between the stem and the surrounding bone in the femoral canal) resulting in a gap between the collar 607 and the remaining bone at the neck resection level. If the femoral hip implant 600 is too small, then the resulting fit of the stem 602 in the femoral canal is suboptimal (i.e., poor contact between the outer surface of the stem 602 and the surrounding bone in the femoral canal), which can result in poor clinical outcomes, implant subsidence, implant loosening, and an increased risk for revision surgery. Further, the remaining bone at the neck resection level can also inhibit the seating of the femoral hip implant 600 in the femur as previously described with reference to FIGs. 15A to 15C (i.e., the cross-section of the canal at a particular neck resection level can inhibit seating of the femoral hip implant 600), which may result in a gap between the collar 607 and the remaining bone at the neck resection level.

[0070] To ensure the collar 607 is optimally positioned against the remaining bone at the neck resection level, the planning software may be further configured to identify and output at least one of an optimal location and/or size of a collared femoral hip implant 600 for placement in the femur, and/or an optimal neck resection level to be formed on the femoral neck. With reference to FIG. 17, the software may execute an iterative process using image data of the bones (e.g., a femoral bone model “FM”) and image data of an implant (e.g., a collared femoral hip implant model 612’) to iterate through different sized collared femoral hip implants, different location configurations for each collared femoral hip implant size, and different neck resection levels as previously described. For each size, location configuration, and neck resection level, the software computes a goodness of fit, which may include one or more of the following: (i) an amount of fill of the femoral hip implant model 612 inside the femoral canal; (ii) minimized sum of errors between the outer surface of the femoral hip implant model 612 and the inner surfaces of the femoral canal, the errors of which are illustrated by line 614; (iii) overlap between the femoral hip implant model 612 and bone directly adjacent to the femoral canal, which may provide an indication of a good interaction fit between the femoral implant component and the bone; (iv) finite element analysis to calculate the amount and location of loads or stresses that may be experience by the bone with a particular femoral implant component and location; and (v) any other statistical parameters representing a good fit for a femoral implant component within the femoral canal. For each iteration, the software also checks if the resulting cross-section from a resection made at a resection level corresponding to the contact surface of the collar 613, as shown in FIG. 17 as neck resection level 623 (note how the neck resection level 623 aligns with the bottom contact surface of the collar 613), will inhibit the seating of the collared femoral hip implant model 612’ in the femur model FM. The software may compute a goodness of fit (e.g., overlap) using the cross-sections of the canal at the neck resection level 623 and the corresponding cross-section of the collared femoral hip implant model 612’ at the resection level 623 as previously described with reference to FIGs. 14-15C. The software compares the goodness of fit computations for different collared femoral hip implant sizes and/or brands, and for different location configurations, as described in the method 200 of FIG. 3, to identify an optimal collared femoral hip implant (e.g., collared femoral hip implant in size ‘x’ manufactured by company ‘y’), an optimal location for that collared femoral hip implant with respect to the femur, and neck resection level that ensures the collar 613 is optimally positioned against the remaining bone at the neck resection level. It should be appreciated that the result also ensures the optimal location for the HCOR is maintained.

[0071] The output of the aforementioned methods may include: (i) the optimal location for the femoral neck resection; (ii) an optimal femoral implant component (e.g., femoral implant component size 6 manufactured by company ‘z’), acetabular cup implant component, and/or associated components (e.g., optimal femoral head implant size, acetabular cup liner size); anteversion & inclination angles for the femoral implant component, acetabular cup implant component, or a combination thereof; (iii) the optimal location for the HCOR; and (iv) the computed goodness of fit parameters and/or other computed values (e.g., amount of crosssection overlap). The output (e.g., a surgical plan) may be saved and transferred to a computer- assisted surgical (CAS) device. The output may include cutting instructions to direct a CAS device during the formation of a resection surface located at the identified optimal resection level. The surgical plan may alternatively include operating instructions, such as the location for one or more virtual coordinates (e.g., a virtual plane), where a CAS device is controlled to maintain alignment of an axis of a tool (e.g., a bone pin) with the virtual coordinates for aligning a cut guide relative to the bone to assist in the formation of a resection surface located at the identified optimal resection level.

Other Embodiments

[0072] While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that a vast number of variations exist. It should also be appreciated that the exemplary embodiment or exemplary embodiments are only examples, and are not intended to limit the scope, applicability, or configuration of the described embodiments in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient roadmap for implementing the exemplary embodiment or exemplary embodiments. It should be understood that various changes may be made in the function and arrangement of elements without departing from the scope as set forth in the appended claims and the legal equivalents thereof.

Claims

1. A computerized method for determining a bone resection level and implant pairing, comprising:

(a) selecting a first resection level to define a first bone perimeter data relative to bone image data of a subject bone;

(b) selecting a first implant having a first characteristic;

(c) virtually positioning a perimeter data of the first implant at a first plurality of locations of the first bone perimeter data of the first resection level;

(d) comparing the perimeter data of the first implant to the first bone perimeter data at the first plurality of locations of the first bone perimeter data of the first resection level to determine a first best fit position of the perimeter data of the first implant relative to the first bone perimeter data;

(e) selecting a second resection level to define a second bone perimeter data relative to the bone image data of the subject bone;

(f) virtually positioning the perimeter data of the first implant at a second plurality of locations of the second bone perimeter data of the second resection level;

(g) comparing the perimeter data of the first implant to the second bone perimeter data at the second plurality of locations of the second bone perimeter data of the second resection level to determine a second best fit position of the perimeter data of the first implant relative to the second bone perimeter data; and

(h) comparing the first best fit position to the second best fit position to determine which of the first resection level and the second resection level is a best pairing for the first implant.

2. The computerized method of claim 1 wherein the bone image data is twodimensional (2-D) image data of the subject bone or a three-dimensional (3-D) bone model of the subject bone.

3. The computerized method of claim 1 wherein the subject bone is a tibia.

4. The computerized method of any one of claims 1 to 3 further comprising creating a 3-D model of the subject bone based on 2-D image data of the subject bone.

5. The computerized method of claim 1 wherein the first resection level and the second resection level are determined based on a first plurality of criteria.

6. The computerized method of claim 5 wherein the first plurality of criteria are supplied by a user.

7. The computerized method of claim 5 wherein the first plurality of criteria are pre-programed into software.

8. The computerized method of claim 5 wherein the first plurality of criteria include at least one of a proximal-distal resection range, a varus- valgus angle, an amount of cortical bone present, or a presence of an abnormality.

9. The computerized method of claim 8 wherein the proximal-distal resection range is based on a user specified range from a tibial plateau, a user specified range from a top of a fibula, an upper boundary, or a lower boundary.

10. The computerized method of claim 8 wherein the varus-valgus angle is relative to a mechanical axis of a tibia.

11. The computerized method of claim 8 wherein the varus-valgus angle is selected to restore a mechanical axis of a knee.

12. The computerized method of claim 8 wherein the amount of cortical bone is maximized.

13. The computerized method of claim 8 wherein the abnormality presence includes avoiding a presence of osteophytes or weak bone.

14. The computerized method of claim 1 wherein avoiding the presence of osteophytes or weak bone includes maintaining a threshold distance from the abnormality at the resection level.

15. The computerized method of claim 14 wherein the threshold distance is set by a user.

16. The computerized method of any one of claims 1, 2, 3, or 5 to 1515 wherein the first implant is selected from an implant library.

17. The computerized method of any one of claims 1, 2, 3, or 5 to 15 wherein the first characteristic of the first implant is an implant size, an implant shape, or an implant brand.

18. The computerized method of any one of claim 1 wherein virtually positioning the perimeter data of the first implant relative to the first bone perimeter data defined by the first resection level and the second bone perimeter data of the second resection level is based on a second plurality of criteria.

19. The computerized method of claim 18 wherein the second plurality of criteria are supplied by a user.

20. The computerized method of claim 18 wherein the second plurality of criteria are pre-programed in software.

21. The computerized method of claim 18 wherein the second plurality of criteria includes at least one of an internal-external rotation angle and a threshold amount of overhang or underhang in an anterior-posterior direction or a medial-lateral direction.

22. The computerized method of claim 1 further comprising:

(a) selecting a second implant having a second characteristic;

(b) virtually positioning a perimeter data of the second implant at a third plurality of locations of the first bone perimeter data of the first resection level;

(c) comparing the perimeter data of the second implant to the first bone perimeter data at the third plurality of locations of the first bone perimeter data of the first resection level to determine a third best fit position of the perimeter data of the second implant relative to the first bone perimeter data; (d) virtually positioning the perimeter data of the second implant at a fourth plurality of locations of the second bone perimeter data of the second resection level;

(e) comparing the perimeter data of the second implant to the second bone perimeter data at the fourth plurality of locations of the second bone perimeter data of the second resection level to determine a fourth best fit position of the perimeter data of the second implant relative to the second bone perimeter data;

(f) comparing the third best fit position to the fourth best fit position to determine which of the first resection level and the second resection level is a best pairing for the second implant; and

(g) comparing the best pairing for the first implant to the best pairing for the second implant to determine an optimal bone resection level and implant pairing.

23. The computerized method of claim 22 wherein the second implant is selected from an implant library.

24. The computerized method of claim 22 wherein the second characteristic of the second implant is an implant size, an implant shape, or an implant brand.

25. The computerized method of any one of claims 22 to 24 wherein the second characteristic of the second implant different from the first characteristic of the first implant.

26. The computerized method of claim 1 wherein at least one location from the first plurality of locations and the second plurality of locations is a shared location.

27. The computerized method of any one of claims 22 to 24 wherein at least one location from the third plurality of locations and the fourth plurality of locations is a shared location.

28. The computerized method of any one of claims 1, 2, 3, 5 to 15, 18 to 24, or 26wherein the selecting of a resection level comprises selecting a 2-D image from and image data set or virtually resecting a 3-D virtual bone model at the resection level.