Patent Description:
In arthroplasty and some sports medicine surgeries, a damaged joint, such as a knee joint, is replaced with prosthetic implants. Prior to implantation of the implant, the damaged region of the joint is typically prepared by resecting or otherwise treating regions of the bones to provide surfaces that can align with and therefore accommodate the implant.

One of the predictors of an orthopedic arthroplasty outcome is appropriate selection and positioning of the prosthetic components. During orthopedic procedures, various tools and instruments are used to assist with prosthetic component selection and placement, including the use of templates as well as provisional or trial implant prosthetics. Such conventional tools and instruments may lack precision as they may rely on the user's judgment to assess proper positioning of the devices. In addition, each patient's anatomy being different, proper component sizing may be required for optimizing the outcome of the surgery. Still, conventional components may only allow patient customization to a certain degree.

<CIT> relates to improved and/or patient-adapted (e.g., patient-specific and/or patient-engineered) orthopedic implants and guide tools, as well as related methods, designs and models.

<CIT> relates to systems and methods for optimizing parameters of orthopaedic procedures, such as systems and methods for optimizing the biomechanic and anatomic fit of an orthopaedic implant into a particular patient's joint.

Example systems and methods for determining a patient specific soft tissue location within a joint of a patient are described. Based at least in part on the patient-specific soft tissue location, the example systems and methods can also be utilized in preoperative planning, to aid in selection or create a surgical jig and/or to aid in selection of a prosthesis. According to some examples, the systems and methods can be used in preoperative planning to provide the user with instructions, visual aids, information, recommendations, automated measurements, and so on as to the location, size, and other properties of soft tissue, bone, and/or prostheses relevant to the procedure. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of examples provided. It will be evident, however, to one skilled in the art that examples of the present invention may be practiced without these specific details or details may be modified to a degree. It will also be evident that the systems and methods discussed are not limited to the examples provided and may include other scenarios not specifically discussed. For example, the methodologies discussed herein with respect to a knee arthroscopy can be similarly applied to other procedures (e.g., reconstructing the PCL or other ligamentous structures for sports medicine surgery, a total hip or total shoulder replacement, among others).

Understanding the location of soft tissue structures is important in many orthopedic procedures. For example, soft tissue location can affect the size and shape of prosthetic implant installed, as well as the shape of a jig used to ensure accurate position and orientation of finishing instruments during bone resection of a knee arthroscopy. For example, understanding the location and shape of the anterior cruciate ligament (ACL) and/or posterior cruciate ligament (PCL) can be beneficial when placing both medial and lateral femoral and tibia implants in a unicompartmental knee arthroscopy, making a tibial cut and placing the tibial component in a cruciate retaining total knee arthroscopy, and making bone cuts and placing implants in a bi-cruciate sparing total knee arthroscopy.

Preoperative planning including templating can be performed in clinical practice to determine the size and shape of implants and jigs likely to best fit the anatomy of the individual. In order to further refine preoperative practice, the current inventors recognized that understanding the location of a patient's soft tissue can be instructive. Current planning technology does not seek such understanding as it can be expensive to gather, inaccurate, and time consuming. Accordingly, the inventors preformed cadaver and imaging studies on hundreds of knee joints to determine the size and location of soft tissue (e.g., ACL, PCL) relative to the morphology (e.g., size and/or features) of the tibia and femur. From such measurements, the inventors derived various databases, methodologies, and systems as disclosed herein. According to some examples provided herein, the disclosed databases, methodologies, and systems can be used to locate soft tissue for preoperative planning when used in combination with radiographic or similar medical images of a patient's joint.

The present invention is defined by the features of the independent claims.

The drawings illustrate generally, by way of example, but not by way of limitation, various examples discussed in the present document.

Example systems and methods for determining a patient specific soft tissue location within a joint of a patient are described. Based at least in part on the patient specific soft tissue locations, the example systems and methods can also be utilized in preoperative planning, to aid in selection or creation of a surgical jig and/or to aid in selection of a prosthesis based on the patient specific soft tissue locations. In the example of <FIG>, a physician or other personnel can use a system, such as system <NUM>, to locate soft tissue in the applicable joint of a patient. The system <NUM> can also be utilized by the user to aid in selection of a prosthesis and/or aid in creation of a jig. Such selection or creation can be based upon the location and other characteristics of the soft tissue, for example. In <FIG>, the system <NUM> can include a location system <NUM> receiving data from one or more of a soft tissue database <NUM>, a medical imaging system <NUM>, or one or more additional databases <NUM>. In some examples, the location system <NUM> can include a user-interface module <NUM>, an image retrieval module <NUM>, a selection module <NUM>, an anatomical geometries module <NUM>, and an image processing engine <NUM>.

In an example, the user-interface module <NUM> can receive input from a user and provide feedback on the resulting measurements, soft tissue locations, calculations, and resection locations, for example. According to some examples, the user-interface module <NUM> can provide guidance for jig and/or prosthesis selection in view of the soft tissue size, location, bone morphology, etc. In one example, the user-interface module <NUM> can process inputs such as the selection of joint morphology (e.g., bone size, bone features, soft tissue location) on a medical image of a region of interest within the medial image of the j oint. Additionally, the user-interface module <NUM> can process inputs and provide output associated with other aspects of the location system <NUM>. According to some examples, the user-interface module <NUM> can interface with user-interface components, such as a display and user-input mechanism (e.g., mouse, keyboard, or touch screen).

In an example, the image retrieval module <NUM> can retrieve a medical image for processing by the location system <NUM> from sources, such as the soft tissue database <NUM> or the medical imaging system <NUM>, among others. The image retrieval module <NUM> can communicate directly with the medical imaging system <NUM> to receive a radiographic (or similar) medical image of a patient's joint for processing by the location system <NUM>. Medical image processed by the location system <NUM> can be of any type of medical image that depicts internal structures of a patient's joint and soft tissue. Technologies such as X-Ray, Fluoroscopy, Computerized Tomography (CT), True size imaging (EOS ™), and MRI can all produce usable images. Other imaging technologies can be used with the methods and systems discussed herein.

The image processing engine <NUM> can run various imagine processing algorithms on the medical images retrieved by the image retrieval module <NUM>. The image processing engine <NUM> can use image processing algorithms such as thresholding, edge detection, contrast detection, contrast-edge detection, and other known image processing techniques to perform the automated measurements discussed in more detail below.

According to the example of <FIG>, the anatomical geometries module <NUM> can use data generated by the image processing engine <NUM> and/or the location system <NUM> to perform calculations to describe or characterize the geometry of one or more bones of the joint. These calculations can determine, for example, anterior/posterior and/or medial/lateral bone dimensions, bone axes/landmarks/positions, relative positions between bones, curvature and surface topography of the bone or articular surface, and/or soft tissue attachment size and/or location, and the like. Bone landmarks can include the size, shape, position of the medial and lateral condyle, medial and lateral epicondyle, tibial tuberosity, trochlear groove, intercondylar notch, PCL facet, tibial eminence, and/or tibular head, for example. According to some examples, the selection module <NUM> can use the calculations generated by the anatomical geometries module <NUM> and the location system <NUM> to select an appropriate patient specific prosthetic implant and/or to prototype or select a patient specific jig. For example, the prosthesis selection module <NUM> can utilize the soft tissue location data as supplied by the location system <NUM> and the dimensions of the available prosthetic implants to optimize fit. Such calculation can be based on the location of the soft tissue relative to the measured geometries of the patient's distal femur and/or proximal tibia in the case of a knee arthroplasty, for example. According to some examples, the prosthesis selection module <NUM> can look up prosthetic implant sizing information from the database <NUM>.

According to one example, a method is disclosed that utilizes imaging data from a patient and performs calculations from the imaging data including determining locations of relevant soft tissue structures. From the calculations, surgical decisions including the size of implants and the placement of the implant on the patient can be determined. The surgical decisions can be visualized electronically prior to being implemented. Based upon the visualization, the surgeon can alter his or her decision as desired.

<FIG> illustrates a natural femur <NUM> and tibia <NUM>. The femur <NUM> can include medial <NUM> and lateral <NUM> condyles at a distal end of the femur <NUM>. Various soft tissues (e.g., ligaments) can be attached to the femur <NUM> and/or the tibia <NUM>. For example, the anterior cruciate ligament (ACL) <NUM> can extend from an anterior side of the tibia <NUM> to the femur <NUM>, and the posterior cruciate ligament (PCL) <NUM> can extend from a posterior side of the tibia <NUM> to the femur <NUM>. <FIG> is a top view of the tibia <NUM> and further illustrates some of these soft tissues as well as a medial meniscus <NUM> and a lateral meniscus <NUM> that are located between the tibia <NUM> and the medial <NUM> and lateral <NUM> condyles.

<FIG> illustrates a sensor <NUM> that can be adapted to sense the morphology (e.g., bone size, bone features, soft tissue shape and location) in a patient's joint <NUM>. The sensor <NUM> can be part of a portable coordinate measurement machine (CMM). It is used to measure the three dimensional location of a point with respect to the base of the machine. It can be used to map the surface of a bone and also the location of soft tissue structures on that bone. The information gathered by the sensor <NUM> can be used in the soft tissue database <NUM> and/or databases <NUM> as described in <FIG>. In the example of <FIG>, the sensor <NUM> is illustrated measuring a size and other geometry of a distal femur <NUM>. According to some examples, the sensor <NUM> can be used to measure a location of where the ACL and PCL connect to the distal femur <NUM>. The size and geometry of features of the distal femur (e.g., medial femoral condyle <NUM>, lateral femoral condyle <NUM>, patellar sulcus <NUM>, and the size and geometry of features of the proximal tibia (e.g. medial and lateral plateau, medial and lateral eminence peaks, tubercle, lateral and medial epicondyle, popliteal sulcus, and so on) can also be measured and located using the sensor <NUM> and that data, along with the associated soft tissue location data can be stored for access. As will be discussed subsequently, the data can be used to generate algorithms that are adapted to predict a location and/or shape of a patient's soft tissue based upon medical images of the patient's joint taken preoperatively. According to some examples, data regarding bone size and bone features can be cross-referenced to associated soft tissue size, location, and shape. Algorithms can be generated that can utilize bone size and/or bone features as ascertained by medical images to aid in the prediction of associated soft tissue size, location, and shape.

<FIG> shows an image of a proximal portion of a tibia <NUM>. According to <FIG>, the image can be generated from image data collected using imaging technology and can be overlaid with data collected during various tests using the sensor <NUM> (<FIG>). As shown in <FIG>, the collected data can include a location of the ACL and PCL on the proximal tibia and can include a location of the lateral and medial intercondyle eminences.

<FIG> is a flowchart illustrating a method for determining a patient specific soft tissue location according to an example of the present disclosure. The method can create <NUM> an average ACL and/or PCL contour for an average femur and/or tibia. The contour can include one or more of attachment location, size, and shape of the ACL and/or PCL. The creation of the average ACL and/or PCL contour can be derived from the database of femoral and tibial data with identified (known) ACL and/or PCL attachment location information as discussed previously. The method can image <NUM> the patient's knee and measure <NUM> the morphology of one or more of the bones (including size, shape, curvature, bone features, etc.) from the image. The data can be derived from medical images using the imaging technologies as described previously. According to the example of <FIG>, the method changes <NUM> (e.g., fits) the appropriate femur and/or tibia model in the database of step <NUM> to match that of the patient's femur and/or tibia. According to some examples, different models can be utilized for different sizes, genders, ethnicities, and so on. For example, this step can change the bone size and/or bone features of the selected knee model in the database to match the bone size and/or bone features of the patient's knee. In some examples, the method can morph geometry as desired, for example by performing a linear transformation. An example of such transformation can include, breaking the object (e.g., bone) into several two dimensional images, taking one of the two dimensional images and comparing it to another of the two dimensional images from the database, making both the images have the same number of points, moving the points in the second two dimensional image from the database to be equal to that of the initial two dimensional image from the patient, performing the prior activity with several of the two dimensional images, creating a mathematical transformation that describes the differences between the two shapes in three dimensions, and transforming any other information (e.g., bony information, soft tissue attachment site) from the database to the specific patient image(s).

Thus, the method can change <NUM> the PCL and/or ACL contour with the change in the average femur and/or tibia of step <NUM>. According to the illustrated method, the attachment location (and/or other ACL and/or PCL information) can be ascertained <NUM> and such information can be utilized by the user for preoperative planning.

<FIG> is a flowchart of a method for among other things preoperative planning, selecting a prosthesis, and/or selecting or fabricating a jig based on the soft tissue location and other patient morphology, according to an example of the present disclosure. The jig can be for arthroplasty or for perform ACL and PCL reconstruction as desired. According to the method of <FIG>, the patient's knee can be imaged <NUM> and the morphology of the knee can be measured <NUM> as previously described. The ACL and/or PCL of the patient can be located <NUM> relative to the tibia and/or femur. The surgeon can preoperatively plan the patient's surgery <NUM> using the location of the ACL and/or PCL and other patient morphology (e.g., bone size, bone features). According to one example, preoperative planning can include providing instructions, visual aid, information, recommendation, and automated measurement to the surgeon. An example of preoperative planning software having such functionality is illustrated and further discussed in reference to <FIG>. Examples of software, modules, and techniques that can be utilized with those disclosed herein are disclosed in <CIT> owned by the Applicant.

According to another example, the preoperative planning can visually display the location of the ACL and/or PCL as illustrated in <FIG> and <FIG> and can allow the surgeon to virtually place tibial and/or femoral implants. According to further examples, the preoperative planning can visually display the location of the ACL and/or PCL for femoral jig sizing and placement as illustrated in <FIG>. According to further examples, the method can create <NUM> or select a jig used to ensure accurate position and orientation of finishing instruments during bone resection as shown in <FIG>. Examples of creation of a jig are described in <CIT> and <CIT> owned by the Applicant. In <FIG>, the method can allow the user to select <NUM> appropriate tibial and/or femoral implants based upon the location of the ACL and/or PCL and other patient morphology (e.g., bone size, bone features). As illustrated in <FIG>, virtual placement of the tibial and/or femoral implants, virtual display of the ACL and/or PCL, selection of implant(s), and creation of the jig can be interrelated or can be performed independent or semi-independent of one another.

<FIG> uses a virtual representations of the location of a patient's soft tissues <NUM> relative to a tibia <NUM>, a femur <NUM>, and a prosthesis <NUM>, according to an example of the present disclosure. Such virtual representations can aid the surgeon in preoperative planning. For example, the surgeon can alter the size or brand of the implant and see the effects on the arthroplasty including any effects on the soft tissues <NUM>. According to another example, the surgeon can change other aspects such as the location of one or more resections utilized for the knee arthroplasty and see the effects on the arthroplasty including any effects on the soft tissues <NUM>. <FIG> uses a virtual representations of the location of a patient's soft tissues <NUM> for sizing and locating a jig <NUM> on a femur <NUM>, according to an example of the present disclosure. Such virtual representations can aid the surgeon in preoperative planning.

<FIG> is a screen shot image of a preoperative planning tool <NUM> including a visual display generated for a surgeon for preoperative planning, according to an example of the present disclosure. As discussed, the planning tool <NUM> can include various functions such as providing instructions, visual aid, information, recommendation, and automated measurement to the surgeon.

In the example of <FIG>, the planning tool <NUM> can allow the surgeon to visualize and alter resections (indicated by yellow lines) and can allow the surgeon to virtually install the implant(s) for review. The planning tool <NUM> can estimate a size of a femoral implant and tibial implant and display such size to the surgeon. The planning too <NUM> can also allow for the virtual selection and display of various implants according to size and/or brand. Furthermore, the planning tool <NUM> can virtually display the soft tissues of the patient according to the estimated location or actual as discussed herein.

Certain examples are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute either software modules (e.g., code embodied on a machine-readable medium or in a transmission signal) or modules. A module is tangible unit capable of performing certain operations and may be configured or arranged in a certain manner. In examples, one or more computer systems (e.g., a standalone, client or server computer system) or one or more modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a module that operates to perform certain operations as described herein.

In various examples, a module may be implemented mechanically or electronically. For example, a module may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special-purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) to perform certain operations. A module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.

Accordingly, the term "module" can be understood to encompass a tangible entity, such as hardware, that can be that an entity that is physically constructed, permanently configured (e.g., hardwired) or temporarily configured (e.g., programmed) to operate in a certain manner and/or to perform certain operations described herein. Considering examples in which modules are temporarily configured (e.g., programmed), each of the modules need not be configured or instantiated at any one instance in time. For example, where the modules comprise a general-purpose processor configured using software, the general-purpose processor may be configured as respective different modules at different times. Software may accordingly configure a processor, for example, to constitute a particular module at one instance of time and to constitute a different module at a different instance of time.

Modules can provide information to, and receive information from, other modules. Accordingly, the described modules may be regarded as being communicatively coupled. Where multiple of such modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the modules. In examples in which multiple modules are configured or instantiated at different times, communications between such modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple modules have access. For example, one module may perform an operation, and store the output of that operation in a memory device to which it is communicatively coupled. A further module may then, at a later time, access the memory device to retrieve and process the stored output. Modules may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information).

The modules referred to herein may, in some examples, comprise processor-implemented modules.

Similarly, the methods described herein may be at least partially processor-implemented. In some example examples, the processor or processors may be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other examples the processors may be distributed across a number of locations.

The one or more processors may also operate to support performance of the relevant operations in a "cloud computing" environment or as a "software as a service" (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., Application Program Interfaces (APIs).

Examples may be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. Examples may be implemented using a computer program product, e.g., a computer program tangibly embodied in an information carrier, e.g., in a machine-readable medium for execution by, or to control the operation of, data processing apparatus, e.g., a programmable processor, a computer, or multiple computers.

A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, subroutine, or other unit suitable for use in a computing environment.

In examples, operations may be performed by one or more programmable processors executing a computer program to perform functions by operating on input data and generating output. Method operations can also be performed by, and apparatus of examples may be implemented as, special purpose logic circuitry, e.g., a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC).

In examples deploying a programmable computing system, it will be appreciated that both hardware and software architectures require consideration. Specifically, it will be appreciated that the choice of whether to implement certain functionality in permanently configured hardware (e.g., an ASIC), in temporarily configured hardware (e.g., a combination of software and a programmable processor), or a combination of permanently and temporarily configured hardware may be a design choice. Below are set out hardware (e.g., machine) and software architectures that may be deployed, in various examples.

Claim 1:
A method comprising:
imaging a target location for an orthopedic implant to collect image data regarding a morphology of the patient, the morphology including at least one of bone size and bone feature wherein the method further comprises;
accessing stored soft tissue data and bone data corresponding to the target location of the orthopedic implant;
determining the location of a soft tissue of the patient based at least in part upon the soft tissue data and bone data and the image data wherein the soft tissue comprises at least one of an ACL and a PCL and determining the location of the soft tissue comprises:
creating an average of one or more of an ACL and PCL contour for one or more of an average femur and tibia from the soft tissue data and bone data corresponding to the target location of the orthopedic implant;
altering one or more of the average femur and tibia to match one or more of a femur and tibia of the patient; and
altering one or more of an ACL and PCL contour of the patient with the step of altering one or more of the average femur and tibia;
displaying data including the location of the soft tissue of the patient; and
recommending a prosthesis for selection from a database of prostheses to best fit the target location of the patient based at least in part upon the determined location of the soft tissue of the patient as well as at least one of the bone size and the bone feature,
wherein the database of prostheses comprises information on implants of various brands and predetermined sizes.