The present invention relates to systems and methods for manufacturing customized arthroplasty cutting jigs. More specifically, the present invention relates to automated systems and methods of manufacturing such jigs.
Over time and through repeated use, bones and joints can become damaged or worn. For example, repetitive strain on bones and joints (e.g., through athletic activity), traumatic events, and certain diseases (e.g., arthritis) can cause cartilage in joint areas, which normally provides a cushioning effect, to wear down. When the cartilage wears down, fluid can accumulate in the joint areas, resulting in pain, stiffness, and decreased mobility.
Arthroplasty procedures can be used to repair damaged joints. During a typical arthroplasty procedure, an arthritic or otherwise dysfunctional joint can be remodeled or realigned, or an implant can be implanted into the damaged region. Arthroplasty procedures may take place in any of a number of different regions of the body, such as a knee, a hip, a shoulder, or an elbow.
One type of arthroplasty procedure is a total knee arthroplasty (“TKA”), in which a damaged knee joint is replaced with prosthetic implants. The knee joint may have been damaged by, for example, arthritis (e.g., severe osteoarthritis or degenerative arthritis), trauma, or a rare destructive joint disease. During a TKA procedure, a damaged portion in the distal region of the femur may be removed and replaced with a metal shell, and a damaged portion in the proximal region of the tibia may be removed and replaced with a channeled piece of plastic having a metal stem. In some TKA procedures, a plastic button may also be added under the surface of the patella, depending on the condition of the patella.
Implants that are implanted into a damaged region may provide support and structure to the damaged region, and may help to restore the damaged region, thereby enhancing its functionality. Prior to implantation of an implant in a damaged region, the damaged region may be prepared to receive the implant. For example, in a knee arthroplasty procedure, one or more of the bones in the knee area, such as the femur and/or the tibia, may be treated (e.g., cut, drilled, reamed, and/or resurfaced) to provide one or more surfaces that can align with the implant and thereby accommodate the implant.
Accuracy in implant alignment is an important factor to the success of a TKA procedure. A one- to two-millimeter translational misalignment, or a one- to two-degree rotational misalignment, may result in imbalanced ligaments, and may thereby significantly affect the outcome of the TKA procedure. For example, implant misalignment may result in intolerable post-surgery pain, and also may prevent the patient from having full leg extension and stable leg flexion.
To achieve accurate implant alignment, prior to treating (e.g., cutting, drilling, reaming, and/or resurfacing) any regions of a bone, it is important to correctly determine the location at which the treatment will take place and how the treatment will be oriented. In some methods, an arthroplasty jig may be used to accurately position and orient a finishing instrument, such as a cutting, drilling, reaming, or resurfacing instrument on the regions of the bone. The arthroplasty jig may, for example, include one or more apertures and/or slots that are configured to accept such an instrument.
A system and method has been developed for producing customized arthroplasty jigs configured to allow a surgeon to accurately and quickly perform an arthroplasty procedure that restores the pre-deterioration alignment of the joint, thereby improving the success rate of such procedures. Specifically, the customized arthroplasty jigs are indexed such that they matingly receive the regions of the bone to be subjected to a treatment (e.g., cutting, drilling, reaming, and/or resurfacing). The customized arthroplasty jigs are also indexed to provide the proper location and orientation of the treatment relative to the regions of the bone. The indexing aspect of the customized arthroplasty jigs allows the treatment of the bone regions to be done quickly and with a high degree of accuracy that will allow the implants to restore the patient's joint to a generally pre-deteriorated state. However, the system and method for generating the customized jigs often relies on a human to “eyeball” bone models on a computer screen to determine configurations needed for the generation of the customized jigs. This “eyeballing” or manual manipulation of the bone modes on the computer screen is inefficient and unnecessarily raises the time, manpower and costs associated with producing the customized arthroplasty jigs. Furthermore, a less manual approach may improve the accuracy of the resulting jigs.
There is a need in the art for a system and method for reducing the labor associated with generating customized arthroplasty jigs. There is also a need in the art for a system and method for increasing the accuracy of customized arthroplasty jigs.
Various embodiments of a method of manufacturing an arthroplasty jig are disclosed herein. In a first embodiment, the method may include the following: generate a bone model, wherein the bone model includes a three dimensional computer model of at least a portion of a joint surface of a bone of a patient joint to undergo an arthroplasty procedure; generate an implant model, wherein the implant model includes a three dimensional computer model of at least a portion of a joint surface of an arthroplasty implant to be used in the arthroplasty procedure; assess a characteristic associated with the patient joint; generate a modified joint surface of the implant model by modifying at least a portion of a joint surface of the implant model according to the characteristic; and shape match the modified joint surface of the implant model and a corresponding joint surface of the bone model.
In a second embodiment, the method may include the following: generate a restored bone model, wherein the bone model includes a three dimensional computer model of at least a portion of a joint surface of a bone of a patient joint to undergo an arthroplasty procedure, wherein the restored bone model is representative of the bone in a pre-degenerated state; generate an implant model, wherein the implant model includes a three dimensional computer model of at least a portion of a joint surface of an arthroplasty implant to be used in the arthroplasty procedure; and shape match an articular joint surface of the restored bone model and a corresponding articular joint surface of the implant model.
In a third embodiment, the method may include the following: generate a bone model, wherein the bone model includes a three dimensional computer model of at least a portion of a knee joint surface of a patient femur to undergo an arthroplasty procedure; identify at least one of a most distal point and a most posterior point of a condyle articular surface of the bone model; generate an implant model, wherein the implant model includes a three dimensional computer model of at least a portion of a joint surface of a femoral arthroplasty knee implant to be used in the arthroplasty procedure; identify at least one of a most distal point and a most posterior point of a condyle articular surface of the implant model; and move at least one of the bone model and the implant model so the at least one of the most distal point and the most posterior point of the condyle articular surface of the bone model generally positionally correspond with the at least one of the most distal point and the most posterior point of the condyle articular surface of the implant model. In a variation of the third embodiment, the method may further include the following: shape match the condyle articular surface of the bone model and the articular condyle surface of the implant model.
In a fourth embodiment, the method may include the following: generate two-dimensional images of a joint surface of a patient bone; generate first data from the two-dimensional images, wherein the first data is representative of the joint surface in a deteriorated state; generate second data from the two-dimensional images, wherein the second data is representative of the joint surface in a non-deteriorated state; generate third data and fourth data positionally referenced to the third data, wherein the third data is representative of a joint surface of an arthroplasty implant and the fourth data is representative of a surgical cut plane associated with the arthroplasty implant; generate fifth data from the first data, wherein the fifth data is representative of a surface of the arthroplasty jig that will matingly receive the joint surface; generate sixth data by matching the second data and the third data, the resulting sixth data including a position of the fourth data when the second and third data are matched; generate seventh data by merging the fifth data and the sixth data; and employ the seventh data in manufacturing the arthroplasty jig from a jig blank.
An arthroplasty jig is also disclosed herein. In one embodiment, the arthroplasty jig may be for performing an arthroplasty procedure on a joint surface of a bone of a patient joint to receive an arthroplasty joint implant. In one embodiment, the arthroplasty jig may include: a mating surface configured to matingly receive the joint surface; a first saw guide oriented relative to the mating surface to result in a resection that allows the arthroplasty joint implant to restore the patient joint to a pre-degenerated alignment; and a second saw guide oriented relative to the mating surface to result in a resection that allows the arthroplasty joint implant to cause the patient joint to have an alignment approaching a zero degree mechanical axis alignment.
While multiple embodiments are disclosed, still other embodiments of the present invention will become apparent to those skilled in the art from the following detailed description, which shows and describes illustrative embodiments of the invention. As will be realized, the invention is capable of modifications in various aspects, all without departing from the spirit and scope of the present invention. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not restrictive.
Disclosed herein are customized arthroplasty jigs 2 and systems 4 for, and methods of, producing such jigs 2. The jigs 2 are customized to fit specific bone surfaces of specific patients. Depending on the embodiment and to a greater or lesser extent, the jigs 2 are automatically planned and generated and may be similar to those disclosed in these three U.S. patent applications: U.S. patent application Ser. No. 11/656,323 to Park et al., titled “Arthroplasty Devices and Related Methods” and filed Jan. 19, 2007; U.S. patent application Ser. No. 10/146,862 to Park et al., titled “Improved Total Joint Arthroplasty System” and filed May 15, 2002; and U.S. patent Ser. No. 11/642,385 to Park et al., titled “Arthroplasty Devices and Related Methods” and filed Dec. 19, 2006. The disclosures of these three U.S. patent applications are incorporated by reference in their entireties into this Detailed Description.
A. Overview of System and Method for Manufacturing Customized Arthroplasty Cutting Jigs
For an overview discussion of the systems 4 for, and methods of, producing the customized arthroplasty jigs 2, reference is made to
The first section, which is discussed with respect to
The second section, which is discussed with respect to
The third section, which is discussed with respect to
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As indicated in
As can be understood from
As can be understood from
As described later in this overview, point P may be used to locate the computer generated 3D models 22, 28, 36 created from the 2D images 16 and to integrate information generated via the 3D models. Depending on the embodiment, point P, which serves as a position and/or orientation reference, may be a single point, two points, three points, a point plus a plane, a vector, etc., so long as the reference P can be used to position and/or orient the 3D models 22, 28, 36 generated via the 2D images 16.
As shown in
In one embodiment, the bone surface contour lines of the bones 18, 20 depicted in the image slices 16 may be auto segmented via a image segmentation process as disclosed in U.S. Patent Application 61/126,102, which was filed Apr. 30, 2008, is entitled System and Method for Image Segmentation in Generating Computer Models of a Joint to Undergo Arthroplasty, and is hereby incorporated by reference into the present application in its entirety.
Computer programs for creating the 3D computer generated bone models 22 from the 2D images 16 include: Analyze from AnalyzeDirect, Inc., Overland Park, Kans.; Insight Toolkit, an open-source software available from the National Library of Medicine Insight Segmentation and Registration Toolkit (“ITK”), www.itk.org; 3D Slicer, an open-source software available from www.slicer.org; Mimics from Materialise, Ann Arbor, Mich.; and Paraview available at www.paraview.org.
As indicated in
In one embodiment, the restored bone models 28 are manually created from the bone models 22 by a person sitting in front of a computer 6 and visually observing the bone models 22 and their degenerated surfaces 24, 26 as 3D computer models on a computer screen 9. The person visually observes the degenerated surfaces 24, 26 to determine how and to what extent the degenerated surfaces 24, 26 surfaces on the 3D computer bone models 22 need to be modified to restore them to their pre-degenerated condition. By interacting with the computer controls 11, the person then manually manipulates the 3D degenerated surfaces 24, 26 via the 3D modeling computer program to restore the surfaces 24, 26 to a state the person believes to represent the pre-degenerated condition. The result of this manual restoration process is the computer generated 3D restored bone models 28, wherein the surfaces 24′, 26′ are indicated in a non-degenerated state.
In one embodiment, the above-described bone restoration process is generally or completely automated, as disclosed in U.S. patent application Ser. No. 12/111,924 to Park, which is entitled Generation of a Computerized Bone Model Representative of a Pre-Degenerated State and Usable in the Design and Manufacture of Arthroplasty Devices, was filed Apr. 29, 2008 and is incorporated by reference in its entirety into this Detailed Description. In other words, a computer program may analyze the bone models 22 and their degenerated surfaces 24, 26 to determine how and to what extent the degenerated surfaces 24, 26 surfaces on the 3D computer bone models 22 need to be modified to restore them to their pre-degenerated condition. The computer program then manipulates the 3D degenerated surfaces 24, 26 to restore the surfaces 24, 26 to a state intended to represent the pre-degenerated condition. The result of this automated restoration process is the computer generated 3D restored bone models 28, wherein the surfaces 24′, 26′ are indicated in a non-degenerated state.
As depicted in
In one embodiment, the POP procedure is a manual process, wherein computer generated 3D implant models 34 (e.g., femur and tibia implants in the context of the joint being a knee) and restored bone models 28 are manually manipulated relative to each other by a person sitting in front of a computer 6 and visually observing the implant models 34 and restored bone models 28 on the computer screen 9 and manipulating the models 28, 34 via the computer controls 11. By superimposing the implant models 34 over the restored bone models 28, or vice versa, the joint surfaces of the implant models 34 can be aligned or caused to correspond with the joint surfaces of the restored bone models 28. By causing the joint surfaces of the models 28, 34 to so align, the implant models 34 are positioned relative to the restored bone models 28 such that the saw cut locations 30 and drill hole locations 32 can be determined relative to the restored bone models 28.
In one embodiment, the POP process is generally or completely automated. For example, a computer program may manipulate computer generated 3D implant models 34 (e.g., femur and tibia implants in the context of the joint being a knee) and restored bone models or planning bone models 28 relative to each other to determine the saw cut and drill hole locations 30, 32 relative to the restored bone models 28. The implant models 34 may be superimposed over the restored bone models 28, or vice versa. In one embodiment, the implant models 34 are located at point P′ (X0-k, Y0-k, Z0-k) relative to the origin (X0, Y0, Z0), and the restored bone models 28 are located at point P (X0-j, Y0-j, Z0-j). To cause the joint surfaces of the models 28, 34 to correspond, the computer program may move the restored bone models 28 from point P (X0-j, Y0-j, Z0-j) to point P′ (X0-k, Y0-k, Z0-k), or vice versa. Once the joint surfaces of the models 28, 34 are in close proximity, the joint surfaces of the implant models 34 may be shape-matched to align or correspond with the joint surfaces of the restored bone models 28. By causing the joint surfaces of the models 28, 34 to so align, the implant models 34 are positioned relative to the restored bone models 28 such that the saw cut locations 30 and drill hole locations 32 can be determined relative to the restored bone models 28. A discussion of various embodiments of the automated POP process is provided later in this Detailed Description.
As indicated in
As can be understood from
Computer programs for creating the 3D computer generated arthritic models 36 from the 2D images 16 include: Analyze from AnalyzeDirect, Inc., Overland Park, Kans.; Insight Toolkit, an open-source software available from the National Library of Medicine Insight Segmentation and Registration Toolkit (“ITK”), www.itk.org; 3D Slicer, an open-source software available from www.slicer.org; Mimics from Materialise, Ann Arbor, Mich.; and Paraview available at www.paraview.org.
Similar to the bone models 22, the arthritic models 36 depict the bones 18, 20 in the present deteriorated condition with their respective degenerated joint surfaces 24, 26, which may be a result of osteoarthritis, injury, a combination thereof, etc. However, unlike the bone models 22, the arthritic models 36 are not bone-only models, but include cartilage in addition to bone. Accordingly, the arthritic models 36 depict the arthroplasty target areas 42 generally as they will exist when the customized arthroplasty jigs 2 matingly receive the arthroplasty target areas 42 during the arthroplasty surgical procedure.
As indicated in
As depicted in
In one embodiment, the procedure for indexing the jig models 38 to the arthroplasty target areas 42 is a manual process. The 3D computer generated models 36, 38 are manually manipulated relative to each other by a person sitting in front of a computer 6 and visually observing the jig models 38 and arthritic models 36 on the computer screen 9 and manipulating the models 36, 38 by interacting with the computer controls 11. In one embodiment, by superimposing the jig models 38 (e.g., femur and tibia arthroplasty jigs in the context of the joint being a knee) over the arthroplasty target areas 42 of the arthritic models 36, or vice versa, the surface models 40 of the arthroplasty target areas 42 can be imported into the jig models 38, resulting in jig models 38 indexed to matingly receive the arthroplasty target areas 42 of the arthritic models 36. Point P′ (X0-k, Y0-k, Z0-k) can also be imported into the jig models 38, resulting in jig models 38 positioned and oriented relative to point P′ (X0-k, Y0-k, Z0-k) to allow their integration with the bone cut and drill hole data 44 of [block 125].
In one embodiment, the procedure for indexing the jig models 38 to the arthroplasty target areas 42 is generally or completely automated, as disclosed in U.S. patent application Ser. No. 11/959,344 to Park, which is entitled System and Method for Manufacturing Arthroplasty Jigs, was filed Dec. 18, 2007 and is incorporated by reference in its entirety into this Detailed Description. For example, a computer program may create 3D computer generated surface models 40 of the arthroplasty target areas 42 of the arthritic models 36. The computer program may then import the surface models 40 and point P′ (X0-k, Y0-k, Z0-k) into the jig models 38, resulting in the jig models 38 being indexed to matingly receive the arthroplasty target areas 42 of the arthritic models 36. The resulting jig models 38 are also positioned and oriented relative to point P′ (X0-k, Y0-k, Z0-k) to allow their integration with the bone cut and drill hole data 44 of [block 125].
In one embodiment, the arthritic models 36 may be 3D volumetric models as generated from the closed-loop process discussed in U.S. patent application Ser. No. 11/959,344 filed by Park. In other embodiments, the arthritic models 36 may be 3D surface models as generated from the open-loop process discussed in U.S. patent application Ser. No. 11/959,344 filed by Park.
In one embodiment, the models 40 of the arthroplasty target areas 42 of the arthritic models 36 may be generated via an overestimation process as disclosed in U.S. Provisional Patent Application 61/083,053, which is entitled System and Method for Manufacturing Arthroplasty Jigs Having Improved Mating Accuracy, was filed by Park Jul. 23, 2008, and is hereby incorporated by reference in its entirety into this Detailed Description.
As indicated in
As can be understood from
As explained above, since the “saw cut and drill hole data” 44, “jig data” 46 and their various ancestors (e.g., models 22, 28, 36, 38) are matched to each other for position and orientation relative to point P and P′, the “saw cut and drill hole data” 44 is properly positioned and oriented relative to the “jig data” 46 for proper integration into the “jig data” 46. The resulting “integrated jig data” 48, when provided to the CNC machine 10, results in jigs 2: (1) configured to matingly receive the arthroplasty target areas of the patient's bones; and (2) having cut slots and drill holes that facilitate preparing the arthroplasty target areas in a manner that allows the arthroplasty joint implants to generally restore the patient's joint line to its pre-degenerated state or natural alignment state.
As can be understood from
For a discussion of example customized arthroplasty cutting jigs 2 capable of being manufactured via the above-discussed process, reference is made to
As indicated in
The interior portion 100 of the femur jig 2A is configured to match the surface features of the damaged lower end (i.e., the arthroplasty target area 42) of the patient's femur 18. Thus, when the target area 42 is received in the interior portion 100 of the femur jig 2A during the TKR surgery, the surfaces of the target area 42 and the interior portion 100 match.
The surface of the interior portion 100 of the femur cutting jig 2A is machined or otherwise formed into a selected femur jig blank 50A and is based or defined off of a 3D surface model 40 of a target area 42 of the damaged lower end or target area 42 of the patient's femur 18.
As indicated in
The interior portion 104 of the tibia jig 2B is configured to match the surface features of the damaged upper end (i.e., the arthroplasty target area 42) of the patient's tibia 20. Thus, when the target area 42 is received in the interior portion 104 of the tibia jig 2B during the TKR surgery, the surfaces of the target area 42 and the interior portion 104 match.
The surface of the interior portion 104 of the tibia cutting jig 2B is machined or otherwise formed into a selected tibia jig blank 50B and is based or defined off of a 3D surface model 40 of a target area 42 of the damaged upper end or target area 42 of the patient's tibia 20.
While the discussion provided herein is given in the context of TKR and TKR jigs and the generation thereof. However, the disclosure provided herein is readily applicable to uni-compartmental or partial arthroplasty procedures in the knee or other joint contexts. Thus, the disclosure provided herein should be considered as encompassing jigs and the generation thereof for both total and uni-compartmental arthroplasty procedures.
The remainder of this Detailed Discussion will now focus on various embodiments for performing POP.
B. Overview of Preoperative Planning (“POP”) Procedure
In one embodiment, as can be understood from [blocks 100-120] of
As indicated in
This ends the overview of the POP process. The following discussions will address each of the aspects of the POP process in detail.
C. Computer Modeling Femur and Tibia
As generally discussed above with respect to
As provided in U.S. patent application Ser. No. 12/111,924 to Park, which is entitled Generation of a Computerized Bone Model Representative of a Pre-Degenerated State and Usable in the Design and Manufacture of Arthroplasty Devices, was filed Apr. 29, 2008 and is incorporated by reference in its entirety into this Detailed Description, specialized medical converging software recognizes the anatomy of the knee and shapes the bone models 22 using mathematical algorithms, such as sequences of nth order polynomials, where n is greater than or equal to 3. A technique such as surface-rendering is then used to construct 3D restored bone models 28′, 28″ of the knee joint 14 from the bone models 22. Examples of medical imaging computer programs that may be used here include Analyze (from AnalyzeDirect, Inc., Overland Park, Kans.), open-source software such as the Insight Toolkit (ITK, www.itk.org) and 3D Slicer (www.slicer.org), and Mimics (from Materialise, Ann Arbor, Mich.).
In one embodiment, the resulting 3D restored bone models 28′, 28″ of the femur portion 200 and tibia portion 205 forming the knee joint 14 include the cortical bone of the femur 18 and the tibia 20. Depending on the embodiment, the restored bone models 28′, 28″ may includes articular cartilage attached to the distal region of the femur 18 and the proximal region of the tibia 20. The computer program may automatically exclude the rest of the soft tissue, as well as the cancellous bone, from the 3D computer models 28′, 28″, although in some variations the computer program may not automatically exclude the rest of the soft tissue and/or the cancellous bone.
The 3D computer generated femur and tibia restored bone models 28′, 28″ are repaired versions of the patient's femur 18 and tibia 20 as these bones are believed to have existed before degenerating into their current existing date, the current state of the patient's femur 18 and tibia 20 being represented by the 3D bone models 22. In other words, the femur and tibia computer generated bone models 22 resulting from the MRI scans depict the femur 18 and tibia 20 in the current deteriorated state. These models 22 are then modified or restored into restored bone models 28′, 28″ to represent the femur 18 and tibia 20 as they likely appeared before beginning to degenerate. The resulting modified or restored models 28′, 28″ can then be used for planning purposes, as described later in this Detailed Description.
For greater detail regarding the methods and systems for computer modeling joint bones, such as the femur and tibia bones forming the knee, please see the following U.S. patent applications, which are all incorporated herein in their entireties: U.S. patent application Ser. No. 11/656,323 to Park et al., titled “Arthroplasty Devices and Related Methods” and filed Jan. 19, 2007; U.S. patent application Ser. No. 10/146,862 to Park et al., titled “Improved Total Joint Arthroplasty System” and filed May 15, 2002; U.S. patent Ser. No. 11/642,385 to Park et al., titled “Arthroplasty Devices and Related Methods” and filed Dec. 19, 2006; and U.S. patent application Ser. No. 12/111,924 to Park, titled “Generation of a Computerized Bone Model Representative of a Pre-Degenerated State and Usable in the Design and Manufacture of Arthroplasty Devices” and filed Apr. 29, 2008.
The femur implant model 34′ will have a joint side 240 and a bone engaging side 245. The joint side 240 will have a condoyle-like surface for engaging a complementary surface of the tibia implant model 34″. The bone engaging side 245 will have surfaces and engagement features 250 for engaging the prepared (i.e., sawed to shape) lower end of the femur 18.
The tibia implant model 34″ will have a joint side 255 and a bone engaging side 260. The joint side 255 will have a plateau-like surface configured to engage the condoyle-like surface of the femur implant model 34′. The bone engaging side 260 will have an engagement feature 265 for engaging the prepared (i.e., sawed to shape) upper end of the tibia 20.
As discussed in the next subsection of this Detailed Description, the femur and tibia restored bone models 28′, 28″ may be used in conjunction with the implant models 34′, 34″ to select the appropriate sizing for the implants actually to be used for the patient.
D. Selecting the Sizes for the Femoral and Tibial Implants
As can be understood from
Each patient has femurs that are unique in size and configuration from the femurs of other patients. Accordingly, each femur restored bone model 28′ will be unique in size and configuration to match the size and configuration of the femur medically imaged. As can be understood from
As can be understood from
In one embodiment, there is a limited number of sizes of a candidate femoral implant. For example, one manufacturer may supply six sizes of femoral implants and another manufacturer may supply eight or another number of femoral implants. The iAP and iML dimensions of these candidate implants may be stored in a database. The bAP and bML are compared to the iAP and iML of candidate femoral implants stored in the database [block 1005]. The system 4 selects a femoral implant model 34′ corresponding to a candidate femoral implant having iAP and iML that satisfies the following two relationships: bML≧iML+ε, wherein −2 mm<ε<5 mm; and bAP≧iAP+σ, where −4 mm<σ<4 mm [block 1010]. As an alternative to [block 1010], in one embodiment, instead of selecting from a limited number of candidate femoral implants, these two relationships are used to manufacture a custom sized femoral implant.
Still referring to
As can be understood from
Each patient has tibias that are unique in size and configuration from the tibias of other patients. Accordingly, each tibia restored bone model 28″ will be unique in size and configuration to match the size and configuration of the tibia medically imaged. As can be understood from
As can be understood from
In one embodiment, there is a limited number of sizes of a candidate tibia implant. For example, one manufacturer may supply six sizes of tibia implants and another manufacturer may supply eight or another number of tibia implants. The jAP and jML dimensions of these candidate implants may be stored in a database. The cAP and cML are compared to the jAP and jML of candidate tibia implants stored in the database [block 1025]. The system 4 selects a tibia implant model 34″ corresponding to a candidate tibia implant having jAP and jML that satisfies the following two relationships: cML≧jML+ω, wherein −2 mm<ω<4 mm; and cAP≧jAP+0, where −2 mm<θ<4 mm [block 1030]. As an alternative to [block 1030], in one embodiment, instead of selecting from a limited number of candidate tibia implants, these two relationships are used to manufacture a custom sized tibia implant.
Still referring to
Femoral and tibial implants represented by the implant models 34′, 34″, such as those depicted in
E. Moving Femur and Tibia Models Towards Corresponding Implant Models Such that Femur and Tibia Models are Superimposed Over the Implant Models.
As explained above with respect to [blocks 100-115] of
As indicated by arrows A in
As mentioned above with respect to the discussion of [block 135] of
In one embodiment, the point PIM associated with the implant models 34 is located between the implant models 34′, 34″ close to their centers, near the intercondylar notch of the femur implant model 34′, and the point PRBM associated with the restored bone models 28 is located between the restored bone models 28′, 28″ close to their centers, near the intercondylar notch of the femur restored bone model 28′. Of course, depending on the embodiment, the points PIM, PRBM may be located at other locations relative to their respective models 34, 28 as long as the locations of the points PIM, PRBM relative to their respective models 34, 28 are generally coordinated with each other. For example, points PIM, PRBM could be positioned relative to their respective models 34, 28 such that the points PIM, PRBM are generally centered at the most distal point of the medial articular condylar surface of each respective model 34, 28.
The preceding example is given in the context of holding the implant models 34 in place and moving the restored bone models 28 to the implant models 34 to superimpose the restored bone models 28 over the implant models 34. However, in other embodiments, the reverse situation may be the case, wherein the restored bone models 28 are held in place and the implant models 34 are moved to the restored bone models 28 to superimpose the implant models 34 over the restored bone models 28.
In summary, as can be understood from
In one embodiment, in the image analysis of the POP, the restored bone models 28′, 28″ may be translated to near the corresponding implants models 34′, 34″ through a distance (α, β, γ) by f (x−α, y−β, z−γ), where a=X0-k−X0-j; β=Y0-k−Y0-j; and y=Z0-k−Z0-j [block 1045]. In other words, the restored bone models 28′, 28″ are moved to the implant models 34′, 34″ such that the two reference points PRBM, PIM are generally in the same location. Therefore, the restored bone models 28′, 28″ and the implants models 34′, 34″ are closely superimposed to provide the starting reference points for translational and rotational positioning of the femoral and tibial implants models 34′, 34″ with respect to the femur and tibia restored bone models 28′, 28″ for the shape matching process discussed in the following subsections of this Detailed Discussion. In other words, the above-described superimposing of the models 28, 34 may act as an initial rough positioning of the models in preparation for the following shape matching process.
F. Refining Positioning Between Bone and Implant Models
Once the bone and implant models 28, 34 are roughly positioned relative to each other via the above-described superimposing process, the positioning of the bone and implant models 28, 34 relative to each other is further refined prior to the shape matching process. The position refining process first entails the identification of landmark reference planes for the femur model, the utilization of the landmark reference planes to identify the elliptical contours of the femur restored bone model, and then the correlation of the femur elliptical contours to corresponding elliptical contours of the implant model in an approximate manner.
1. Determining Landmark Reference Planes for Femur Model.
The determination of the landmark reference planes for the femur model may be made via at least two methods. For example, a first method entails employing asymptotic lines to identify the landmark reference planes. In a second method, the landmark reference planes are identified via their relationship to a trochlear groove plane.
i. Landmark Reference Planes Identified Via Asymptotic Lines
As indicated in
ii. Landmark Reference Lines Identified Via Trochlear Groove Plane.
In one embodiment, the identification of the trochlear groove plane-GHO may be made during the verification of the accuracy of the bone restoration process as disclosed in U.S. patent application Ser. No. 12/111,924 to Park, which is entitled Generation of a Computerized Bone Model Representative of a Pre-Degenerated State and Usable in the Design and Manufacture of Arthroplasty Devices, was filed Apr. 29, 2008 and is incorporated by reference in its entirety into this Detailed Description.
As shown in
As shown in
As shown in
As illustrated in
Line HO and line AC may form a plane S, and lines GO and line BD may form a plane P that is perpendicular to plane S and forms line SR therewith. Line HO and line GO are parallel or nearly parallel to each other. Lines AC, BD and SR are parallel or nearly parallel to each other. Lines AC, BD and SR are perpendicular or nearly perpendicular to lines HO and GO and the trochlear plane GHO.
2. Determine Elliptical Contours for Condyles of Femur Restored Bone Model.
Based on plane-ef from
As indicated in
As indicated in
3. Determine Elliptical Contours for Condyles of Femur Implant and Align in an Approximate Manner Implant Condyles to Femur Model Condyles.
As indicated in
As indicated in
As indicated in
In case of a femoral implant model 34′ with symmetric condyles 515, 520, both ellipse 505, 510 on the medial side 520 and lateral side 515 are the same where plane-PS equals plane-RT and plane-SQ equals plane-UT. By the application of these planes-PS, RT, SQ, UT of the femoral implant model 34′, the femoral model condyles 430, 445 can be aligned to the proximity of the corresponding femoral implant condyles 515, 520 where plane-AE is parallel to plane-PS, plane-EB is parallel to plane-SQ, plane-CF is parallel to plane-RT, and plane-DF is parallel to plane-UT [block 1120].
In one embodiment, where the trochlear groove plane is determined with respect to the restored bone model 28′, as discussed above with respect to
The relationships between the planes-AE, EB, CF and DF of the restored bone model 28′ can be positionally correlated with the respective corresponding planes-PS, SQ, RT and UT of the femur implant model 34′ to refine the initial superimposing of the femur restored bone model 28′ over the implant model 34′ such that the condylar surfaces 465, 480 of the bone model 28′ are approximately aligned with the respective condylar surfaces 535, 540 of the implant model 34′ prior to the shape matching process described below in this Detailed Description.
G. Shape Matching Condylar Surfaces of Restored Bone Model to Condylar Surfaces of Femoral Implant Model.
In one embodiment, the POP system and method, once the position of the bone model and implant model is refined as described immediately above, then employs a shape match technique to match a model 34′ of an available femoral implant to the femoral planning or restored bone model 28′. Before employing the shape match technique, it is determined if an asymmetrically modified femoral implant is selected for the POP process, or is a symmetric femoral implant is selected for the POP process (see
1. Asymmetrically Modified Femoral Implant Model
For a discussion regarding a POP design employing an asymmetrically modified femoral implant model 34′, reference is made to
As shown in
As can be understood from
As can be understood from
Because the line-AB connecting point-A and point-B is titled relative to the reference line 600, an angle θ can be obtained where θ=tan−1(td/L) and L is the distance along the reference line 600 between points A and B [block 1140]. The value for angle θ can be stored in a database 15 of the system 4 and further applied to a symmetric femoral implant alignment after the shape matching technique described below with respect to
2. Symmetrical Femoral Implant
For a discussion regarding a POP design employing an symmetrical femoral implant model 34′, reference is made to
As indicated in
3. Determining Joint Line and Adjustment to Surface Matching that Allows Surface Matching of Implant Model Condylar Surfaces to Restored Bone Model Condylar Surfaces to Restore Joint to Natural Configuration.
In order to allow an actual physical arthroplasty implant to restore the patient's knee to the knee's pre-degenerated or natural configuration with the its natural alignment and natural tensioning in the ligaments, the condylar surfaces of the actual physical implant generally replicate the condylar surfaces of the pre-degenerated joint bone. In one embodiment of the systems and methods disclosed herein, condylar surfaces of the restored bone model 28′ are surface matched to the condylar surfaces of the implant model 34′. However, because the restored bone model 28′ may be bone only and not reflect the presence of the cartilage that actually extends over the pre-degenerated condylar surfaces, the surface matching of the modeled condylar surfaces may be adjusted to account for cartilage or proper spacing between the condylar surfaces of the cooperating actual physical implants (e.g., an actual physical femoral implant and an actual physical tibia implant) used to restore the joint such that the actual physical condylar surfaces of the actual physical cooperating implants will generally contact and interact in a manner substantially similar to the way the cartilage covered condylar surfaces of the pre-degenerated femur and tibia contacted and interacted.
Thus, in one embodiment, the implant model is modified or positionally adjusted to achieve the proper spacing between the femur and tibia implants. To achieve the correct adjustment, an adjustment value tr may be determined (see
i. Determining Cartilage Thickness and Joint Line
The space between the elliptical outlining 625′, 625″ along the cortical bone represents the cartilage thickness of the femoral condyle 615. The ellipse contour of the femoral condyle 615 can be seen on the MRI slice shown in
The system and method disclosed herein provides a POP method to substantially restore the joint line back to a “normal or natural knee” status (i.e., the joint line of the knee before OA occurred) and preserves ligaments in TKA surgery (e.g., for a total knee arthroplasty implant) or partial knee arthroplasty surgery (e.g., for a uni-knee implant).
The ACL is located in the front part of the center of the joint. The ACL is a very important stabilizer of the femur on the tibia and serves to prevent the tibia from rotating and sliding forward during agility, jumping, and deceleration activities. The PCL is located directly behind the ACL and the tibia from sliding to the rear. The system and method disclosed herein provides POP that allows the preservation of the existing ligaments without ligament release during TKA surgery. Also, the POP method provides ligament balance, simplifying TKA surgery procedures and reducing pain and trauma for OA patients.
As indicated in
As can be understood from
As indicated in
As shown in
The three-point tangent contact spot analysis may be employed to configure the size and radius of the condyle 445 of the femur restored bone model 28′. This provides the “x” coordinate and “y” coordinate, as the (x, y) origin (0, 0) shown in
As can be understood from
ii. Determining Joint Gap
As mentioned above, in one embodiment, the adjustment value tr may be determined via a joint line gap assessment. The gap assessment may serve as a primary estimation of the gap between the distal femur and proximal tibia of the restored bone model. The gap assessment may help achieve proper ligament balancing.
In one embodiment, an appropriate ligament length and joint gap may not be known from the restored bone models 28′, 28″ (see
In one embodiment, ligament balancing may also be considered as a factor for selecting the appropriate implant size. As can be understood from
In one embodiment of the implant size selection process, it may be assumed that the non-deteriorated side (i.e., the medial side 1485 in
For a discussion regarding the gap assessment, which may also be based on ligament balance off of a non-deteriorated side of the joint, reference is made to
As indicated in
As illustrated in
For calculation purposes, the restored joint line gap Gp3 may be which ever of Gp1 and Gp2 has the minimum value. In other words, the restored joint line gap Gp3 may be as follows: Gp3=MIN (Gp1, Gp2). For purposes of the adjustment to the shape matching, the adjustment value tr may be calculated as being half of the value for Gp3, or in other words, tr=Gp3/2. As can be understood from
In one embodiment, the joint line gap assessment may be at least a part of a primary assessment of the geometry relationship between the distal femur and proximal tibia. In such an embodiment, the joint gap assessment step may occur between [block 173] and [block 174] of
4. Adjust Femoral Implant to Account for Joint Gap or Cartilage Thickness.
Once the adjustment value tr is determined based off of cartilage thickness or joint line gap Gp3, the femoral implant model 34′ can be modified or adjusted to account for cartilage thickness to restore the joint line (see
As can be understood from
where P=wr, q=ws, and 0<w<1, wherein when p=q the result is a circle curve and when p≠q the result is an ellipse curve. Via the adjustment value tr, a restored condylar shape may be obtained by using the ellipse model and the mathematical information described above. The outer ellipse 480″ may be attached with the adjustment value tr, which may be representative of cartilage thickness or half of the restored joint gap Gp3, and the inner ellipse 480′ may be the bone contour without cartilage extending about the bone contour. The inner and outer ellipse 480′, 480″ may differ in a ratio of w factor, where 0<w<1. Based on the w factor, the p radius is smaller than radius r in a ratio of w. A similar analogy applies for radius q, where q is smaller than s in a ratio of w.
As best illustrated in
A distal portion 696 of the distal-anterior non-articular surface 695 may be a plane generally perpendicular to a natural alignment vertically extending axis when the actual physical implant is mounted on the distal femur end as part of an arthroplasty procedure. An anterior chamfered portion 697 of the distal-anterior non-articular surface 695 may be a plane having a generally chamfered relationship to the distal portion 696.
The distal portion 696 of the distal-anterior non-articular surface 695 may abut against the first distal planar resection formed in the distal femur end during the arthroplasty procedure. The first distal planar resection may act as a guide from which other resections (e.g., the posterior and anterior chamfer resections) are referenced. The anterior chamfered portion 697 of the distal-anterior non-articular surface 695 may abut against the anterior planar resection formed in the distal femur end during the arthroplasty procedure. Thus, the interior anterior-distal non-articular surface 685 is adapted to receive the anterior flange of a resected distal femur.
The lower or distal-posterior part 675 of femoral implant model 34′ includes an external posterior-distal articular surface 700 and a multi-faced interior posterior-distal non-articular surface 705. The external posterior-distal articular surface 700 includes the medial distal-posterior condylar articulating surface 540 and the lateral distal-posterior condylar articulating surface 535. The lower or distal-posterior part 675 of femoral implant model 34′ is modified to account for the adjustment value tr, which may be based on the cartilage thickness or half of the restored joint gap Gp3. In one embodiment, the adjustment value tr is applied in both a posterior-anterior direction and a distal-proximal direction to the lower or distal-posterior portion 675 of the implant model 34′.
As can be understood from
5. Shape Matching of Condyle Surfaces of Restored Femoral Bone Model to Condyle Surfaces of Femoral Implant Model.
In one embodiment, the surface models 720, 725 may displace medial-lateral relative to each other, but are constrained to move with each other in all other directions. For example the surface models 720, 725 may displace closer or further apart to each other along the x-axis, but are matched to displace along the y-axis and z-axis as a set and fixed relative to each other.
The function ē(x,y,z) represents a true vector assuming that template noise is independent of the implant surface profile noise. The problem is estimating the parameters of a 3D transformation that satisfies the least squares fit surface matching of the implant condyle articular surface profile s (x, y, z) to the femoral condyle articular surface profile h (x, y, z). This can be achieved by minimizing a goal function, which measures the sum of squares of the Euclidean distances between these two surface profiles, represented by ē(x,y,z)=h(x,y,z)=s(x,y,z). For greater detail regarding this operation, see the following publications, which are incorporated by reference in their entireties into this Detailed Description: D. Akca, Matching of 3D Surfaces and Their Intensities, ISPRS Journal of Photogrammetry & Remote Sensing, 62 (2007), 112-121; and Gruen A. et al., Least Squares 3D surface and Curve Matching, ISPRS Journal of Photogrammetry & Remote Sensing 59 (2005), 151-174.
As an option to the process discussed with respect to
The ellipsoid equation in model in 730 can be illustrated as
The surface models of ellipsoid condylar portions 735, 735′ can be obtained from these two ellipsoid equations. These two portions 735, 735′ correspond to the distal-posterior portions of each condyle 430, 445 of the distal femur surface model 28′. In the femur model 28′, the function f (x, y, z) represents a portion of ellipsoid surface of model portion 735, approximately describing the distal-posterior bone surface of the medial condyle 445. Similarly, the function f′ (x, y, z) represents a portion of ellipsoid surface of model portion 735′, approximately describing the distal-posterior bone surface of the lateral condyle 430.
The function s (x, y, z) represents the surface model 725 of the medial distal-posterior exterior articular surface 700 of the lower or distal-posterior part 675 of femoral implant model 34′ in
In one embodiment, the surface models 720, 725 may displace medial-lateral relative to each other, but are constrained to move with each other in all other directions. For example the surface models 720, 725 may displace closer or further apart to each other along the x-axis, but are matched to displace along the y-axis and z-axis as a set and fixed relative to each other.
The function e(x, y, z) represents a true vector assuming that template noise is independent of the implant surface profile noise. The parameters of a 3D transformation satisfy the least squares matching of the interior surface profile s (x, y, z) of the implant to the ellipsoid potions surface profile f (x, y, z) of the femoral condyle. Similarly, the e(x, y, z) represents the least squares matching of the interior surface profile s′ (x, y, z) of the implant to the ellipsoid potions surface profile f′(x, y, z) of the femoral condyle. This can be achieved by minimizing a goal function, which measures the sum of squares of the Euclidean distances between the two surface profiles, represented by e(x,y,z)=f(x,y,z)−s(x,y,z), and e′(x,y,z)=f′(x,y,z)−s′(x,y,z), where J=MIN (e(x,y,z)), and J′=MIN (e′(x,y,z)). See D. Akca (supra). The valgus/varus and IR/ER of the original joint line has now been restored.
6. Aligning with Respect to Rotation and Translation the Modified Femoral Implant Model to the Femur Model
As previously discussed with respect to
As can be understood from a comparison of
By employing the three-point tangent contact spot method (i.e., points M, N, and any points between M and N), the minimum degree of error A° is achieved. The degree of error A° is based on the limitation of available sizes of commercial implants, where 0<A°<20°. For example, some implant manufacturers only make available eight sizes of femoral implants. If the patient's femur bAP extent is greater than the iAP extent of the selected implant size, while the bML is approximately equal to iML, then applying the model 34′ of the selected implant to align with the patient's femur restored bone model 28′ will cause an error of degree A° that is larger than a 20° rotation alignment range. In this case it is suggested to choose the next bigger size of implant to minimize the degree of error.
As can be understood from
The orientation and location of the implant's mounting post P (see
Once the shape match process (see
H. Determining Areas of Interest A, B for Tibia Plateau Corresponding to Areas of Interest A, B for the Femoral Condyles
I. Determining Reference Points for Tibia Plateau
As with the identification of the distal reference points at the most distal points of the femoral condyle articular surfaces, as discussed above, corresponding reference points are identified on the tibia plateaus (see
In each of the MRI slices 16, the landmarks as well as the origin O of the medial and lateral tibia plateaus 765, 760 for IR/ER rotation and alignment of the tibia implant model 34″ can be obtained. A medial-lateral extending line connecting both spots S, V can be made which is parallel to the joint line or parallel to a reference Z-axis of the X-Y axis indicated in
In
As can be understood from
J. Determining Joint Line and Adjustment to Surface Matching That Allows Surface Matching of Implant Model Condylar Surfaces to Restored Bone Model Condylar Surfaces to Restore Joint to Natural Configuration.
In order to allow an actual physical arthroplasty implant to restore the patient's knee to the knee's pre-degenerated or natural configuration with its natural alignment and natural tensioning in the ligaments, the condylar surfaces of the actual physical implant generally replicate the condylar surfaces of the pre-degenerated joint bone. In one embodiment of the systems and methods disclosed herein, condylar surfaces of the restored bone model 28″ are surface matched to the condylar surfaces of the implant model 34″. However, because the restored bone model 28″ may be bone only and not reflect the presence of the cartilage that actually extends over the pre-degenerated condylar surfaces, the surface matching of the modeled condylar surfaces may be adjusted to account for cartilage or proper spacing between the condylar surfaces of the cooperating actual physical implants (e.g., an actual physical femoral implant and an actual physical tibia implant) used to restore the joint such that the actual physical condylar surfaces of the actual physical cooperating implants will generally contact and interact in a manner substantially similar to the way the cartilage covered condylar surfaces of the pre-degenerated femur and tibia contacted and interacted.
Thus, in one embodiment, the implant model is modified or positionally adjusted to achieve the proper spacing between the femur and tibia implants. To achieve the correct adjustment, an adjustment value tr may be determined (see
i. Determining Cartilage Thickness
The wm in
ii. Determining Joint Gap
In one embodiment, the joint gap is analyzed as discussed above with respect to
K. Determine Slope Vectors for Tibia Plateau
The slope vectors for the plateau of the tibia restored bone model 28″ are determined (see
As indicated in
As indicated in
L. Determine Slope Vectors for Tibia Implant
The of slope vectors for the plateau of the tibia implant model 34″ are determined (see
As shown in
M. Addressing Possible IR/ER Misalignment for the Tibial Implant
The possible IR/ER misalignment issue for the design of tibial implant model 34″ can be assessed (see
The above described landmark references and the IR/ER alignment of the tibial restored bone model 28″ provides the proximity information of the landmarks and IR/ER alignment to the tibial implant model 34″.
N. Modifying the Tibial Implant Model to Account for the Adjustment Value tr
The adjustment value tr may be determined via any of the above-described embodiments. Having determined the adjustment value tr, the compensation of the tibial implant model 34″ for the adjustment value tr can be achieved by lowering the mid-portions of each tibial plateau 770, 775 a tr distance. For example, the mid-portions of the medial side 775 will be lowered to achieve the adjustment value tr. Similarly, the mid-portions of the lateral side 770 of the articular bearing member 785 will be lowered to achieve the adjustment value tr.
O. Surface Matching for Tibia Implant
For example, based on the surface profile q (x, y, z) of the medial plateau 765 of the restored bone model 28″, the surface profile 805 (i.e., p (x, y, z)) of medial portion 775 of the implant model 34″ can be obtained. The function e(x, y, z) represents a true vector assuming that template noise is independent of the implant medial surface profile noise. The problem is estimating the parameters of a 3D transformation which satisfies the least squares fit 3D surface matching of the tibial medial surface profile 805 (i.e., p (x, y, z)) of the tibial implant model 34″ to the medial plateau surface profile q (x, y, z) of restored bone model 28″. This can be achieved by minimizing a goal function, which measures the sum of squares of the Euclidean distances between these two surface profiles, represented by e(x,y,z)=q(x,y,z)−p(x,y,z).
The same rationale applies to the surface profile modeling of the lateral compartment 800 of implant model 34″. Based on the surface profile q′ (x, y, z) of lateral plateau 760 in restored bone model 28″, the surface profile 800 (i.e., p′ (x, y, z) of the lateral compartment 770 of the implant model 34″ can be obtained. The e′(x, y, z) represents a true vector assuming that template noise is independent of the implant medial surface profile noise. Again, the problem is estimating the parameters of a 3D transformation which satisfies the least squares fit 3D surface matching the lateral surface profile 800 (i.e., p′ (x, y, z)) of the tibial implant model 34″ to the lateral plateau surface profile q (x, y, z) of the tibial restored bone model 28″. This can be achieved by minimizing a goal function, which measures the sum of squares of the Euclidean distances between these two surface profiles, represented by e′(x,y,z)=q′(x,y,z)−p′(x,y,z). See D. Akca (supra).
Surface modeling, as described in the following discussion, can be utilized as an option to the surface matching process discussed with respect to
The surface profile 805 (i.e., p(x,y,z)), representing the medial compartment 775 of the articular bearing member 785, can be obtained either through the surface profile of the ellipsoid model 815 or the medial concavity 830 of the tibial restored bone model 28″, as shown in
The rectangle block model 825 is reconstructed from the lateral plateau MRI slices. The surface profile of this rectangle block model 825 can be represented as k′(x,y,z). The lateral concavity 835 matches the rectangle block model 825. In one embodiment, the surface profile 800 (i.e., p′(x,y,z)), representing the lateral compartment 760 of the articular bearing member 785, can be obtained either through the surface profile of the rectangle block model 825 or the lateral concavity 835 of the tibial restored bone model 28″ as shown in
P. Determining Surgical Cut Plane for Tibia
As can be understood from
In a manner similar to that depicted in
The orientation and location of the implant's mounting post 780 may also be determined once the implant model 34″ and restored bone model 28″ are superposed. Also, the locations and orientations of the drill holes 124 (see
Once the shape match process (see
R. Verification of Implant Planning Models and Generation of Surgical Jigs Based of Planning Model Information
The IR/ER rotation between the implants 34′, 34″ and the femur and tibia restored bone models 28′, 28″ is examined in both the medial view and the lateral view. For example,
S. Mechanical Axis Alignment
While much of the preceding disclosure is provided in the context of achieving natural alignment for the patient's knee post implantation of the actual physical femur and tibia implants, it should be noted that the systems and methods disclosed herein can be readily modified to produce an arthroplasty jig 2 that would achieve a zero degree mechanical axis alignment for the patient's knee post implantation.
For example, in one embodiment, the surgeon utilizes a natural alignment femoral arthroplasty jig 2A as depicted in
In one embodiment, as indicated in
In one embodiment of the POP systems and methods disclosed herein, instead of superposing and shape matching the restored bone models 28′, 28″ to the implant models 34′, 34″ in a manner that results in the saw cut and drill hole data 44 that leads to the production of natural alignment arthroplasty jigs 2A, 2B, the superposing and shape matching of the bone and implant models 28, 34 may be conducted such that the resulting saw cut and drill hole data 44 leads to the production of zero degree mechanical axis alignment arthroplasty jigs or some other type of arthroplasty jig deviating in a desired manner from zero degree mechanical axis.
Thus, depending on the type of arthroplasty jig desired, the systems and methods disclosed herein may be applied to both the production of natural alignment arthroplasty jigs, zero degree mechanical axis alignment jigs, or arthroplasty jigs configured to provide a result that is somewhere between natural alignment and zero degree mechanical axis alignment.
Although the present invention has been described with reference to preferred embodiments, persons skilled in the art will recognize that changes may be made in form and detail without departing from the spirit and scope of the invention.
The present application claims priority to U.S. Patent Application 61/102,692 (“the '692 application”) which was filed Oct. 3, 2008 and entitled Arthroplasty System and Related Methods. The present application is also continuation-in-part (“CIP”) application of U.S. patent application Ser. No. 11/959,344, which was filed Dec. 18, 2007 and entitled System and Method for Manufacturing Arthroplasty Jigs. The present application is also a CIP application of U.S. patent application Ser. No. 12/111,924 (“the '924 application”), which was filed Apr. 29, 2008 and entitled Generation of a Computerized Bone Model Representative of a Pre-Degenerated State and Useable in the Design and Manufacture of Arthroplasty Devices. The present application is also a CIP application of U.S. patent application Ser. No. 12/505,056 (“the '056 application”), which was filed Jul. 17, 2009 and entitled System and Method for Manufacturing Arthroplasty Jigs Having Improved Mating Accuracy. The '056 application claims priority to U.S. Patent Application 61/083,053 filed Jul. 23, 2008 and entitled System and Method for Manufacturing Arthroplasty Jigs Having Improved Mating Accuracy. The present application claims priority to all of the above-mentioned applications and hereby incorporates by reference all of the above-mentioned applications in their entireties into the present application.
Number | Name | Date | Kind |
---|---|---|---|
3195411 | MacDonald et al. | Jul 1965 | A |
3825151 | Arnaud | Jul 1974 | A |
D245920 | Shen | Sep 1977 | S |
4298992 | Burstein | Nov 1981 | A |
4436684 | White | Mar 1984 | A |
D274093 | Kenna | May 1984 | S |
D274161 | Kenna | Jun 1984 | S |
4575330 | Hull | Mar 1986 | A |
4821213 | Cline et al. | Apr 1989 | A |
4822365 | Walker et al. | Apr 1989 | A |
4931056 | Ghajar et al. | Jun 1990 | A |
4936862 | Walker et al. | Jun 1990 | A |
5027281 | Rekow et al. | Jun 1991 | A |
5075866 | Goto et al. | Dec 1991 | A |
5078719 | Schreiber | Jan 1992 | A |
5098383 | Hemmy et al. | Mar 1992 | A |
5122144 | Bert et al. | Jun 1992 | A |
5123927 | Duncan et al. | Jun 1992 | A |
5140646 | Ueda | Aug 1992 | A |
5156777 | Kaye | Oct 1992 | A |
5171276 | Caspari et al. | Dec 1992 | A |
D336518 | Taylor | Jun 1993 | S |
5218427 | Koch | Jun 1993 | A |
5234433 | Bert et al. | Aug 1993 | A |
5274565 | Reuben | Dec 1993 | A |
5298115 | Leonard | Mar 1994 | A |
D346979 | Stalcup et al. | May 1994 | S |
5320529 | Pompa | Jun 1994 | A |
5360446 | Kennedy | Nov 1994 | A |
5364402 | Mumme et al. | Nov 1994 | A |
5365996 | Crook | Nov 1994 | A |
D355254 | Krafft et al. | Feb 1995 | S |
5408409 | Glassman et al. | Apr 1995 | A |
5448489 | Reuben | Sep 1995 | A |
5452407 | Crook | Sep 1995 | A |
5462550 | Dietz et al. | Oct 1995 | A |
5484446 | Burke et al. | Jan 1996 | A |
D372309 | Heldreth | Jul 1996 | S |
D374078 | Johnson et al. | Sep 1996 | S |
5556278 | Meitner | Sep 1996 | A |
5569261 | Marik et al. | Oct 1996 | A |
5601563 | Burke et al. | Feb 1997 | A |
5662656 | White | Sep 1997 | A |
5682886 | Delp et al. | Nov 1997 | A |
5683398 | Carls et al. | Nov 1997 | A |
5716361 | Masini | Feb 1998 | A |
5725376 | Poirier | Mar 1998 | A |
5749876 | Duvillier et al. | May 1998 | A |
5768134 | Swaelens et al. | Jun 1998 | A |
5769859 | Dorsey | Jun 1998 | A |
D398058 | Collier | Sep 1998 | S |
5810830 | Noble et al. | Sep 1998 | A |
5824100 | Kester et al. | Oct 1998 | A |
5824111 | Schall et al. | Oct 1998 | A |
5860980 | Axelson, Jr. et al. | Jan 1999 | A |
5860981 | Bertin et al. | Jan 1999 | A |
5871018 | Delp et al. | Feb 1999 | A |
5880976 | DiGioia, III et al. | Mar 1999 | A |
5908424 | Bertin et al. | Jun 1999 | A |
5964808 | Blaha et al. | Oct 1999 | A |
5967777 | Klein et al. | Oct 1999 | A |
5993448 | Remmler | Nov 1999 | A |
6002859 | DiGioia, III et al. | Dec 1999 | A |
6068658 | Insall et al. | May 2000 | A |
6090114 | Matsuno et al. | Jul 2000 | A |
6096043 | Techiera et al. | Aug 2000 | A |
6106529 | Techiera | Aug 2000 | A |
6112109 | D'Urso | Aug 2000 | A |
6132447 | Dorsey | Oct 2000 | A |
6171340 | McDowell | Jan 2001 | B1 |
6205411 | DiGioia, III et al. | Mar 2001 | B1 |
6228121 | Khalili | May 2001 | B1 |
6254639 | Peckitt | Jul 2001 | B1 |
6343987 | Hayama et al. | Feb 2002 | B2 |
6382975 | Poirier | May 2002 | B1 |
6383228 | Schmotzer | May 2002 | B1 |
6415171 | Gueziec et al. | Jul 2002 | B1 |
6458135 | Harwin et al. | Oct 2002 | B1 |
6463351 | Clynch | Oct 2002 | B1 |
6503254 | Masini | Jan 2003 | B2 |
6510334 | Schuster et al. | Jan 2003 | B1 |
6520964 | Tallarida et al. | Feb 2003 | B2 |
6575980 | Robie et al. | Jun 2003 | B1 |
6602259 | Masini | Aug 2003 | B1 |
6672870 | Knapp | Jan 2004 | B2 |
6692448 | Tanaka et al. | Feb 2004 | B2 |
6701174 | Krause et al. | Mar 2004 | B1 |
6711432 | Weiss et al. | Mar 2004 | B1 |
6712856 | Carignan et al. | Mar 2004 | B1 |
6716249 | Hyde | Apr 2004 | B2 |
6738657 | Franklin et al. | May 2004 | B1 |
6747646 | Gueziec et al. | Jun 2004 | B2 |
6770099 | Andriacchi et al. | Aug 2004 | B2 |
6772026 | Bradbury et al. | Aug 2004 | B2 |
6814575 | Poirier | Nov 2004 | B2 |
6905510 | Saab | Jun 2005 | B2 |
6905514 | Carignan et al. | Jun 2005 | B2 |
6923817 | Carson et al. | Aug 2005 | B2 |
6932842 | Litschko et al. | Aug 2005 | B1 |
6944518 | Roose | Sep 2005 | B2 |
6969393 | Pinczewski et al. | Nov 2005 | B2 |
6975894 | Wehrli et al. | Dec 2005 | B2 |
6978188 | Christensen | Dec 2005 | B1 |
7029479 | Tallarida et al. | Apr 2006 | B2 |
7039225 | Tanaka et al. | May 2006 | B2 |
7060074 | Rosa et al. | Jun 2006 | B2 |
7074241 | McKinnon | Jul 2006 | B2 |
7090677 | Fallin et al. | Aug 2006 | B2 |
RE39301 | Bertin | Sep 2006 | E |
7104997 | Lionberger et al. | Sep 2006 | B2 |
7128745 | Masini | Oct 2006 | B2 |
D532515 | Buttler et al. | Nov 2006 | S |
7141053 | Rosa et al. | Nov 2006 | B2 |
7153309 | Huebner et al. | Dec 2006 | B2 |
7184814 | Lang et al. | Feb 2007 | B2 |
7235080 | Hodorek | Jun 2007 | B2 |
7238190 | Schon et al. | Jul 2007 | B2 |
7239908 | Alexander et al. | Jul 2007 | B1 |
7309339 | Cusick et al. | Dec 2007 | B2 |
7340316 | Spaeth et al. | Mar 2008 | B2 |
7359746 | Arata | Apr 2008 | B2 |
7392076 | De La Barrera | Jun 2008 | B2 |
7393012 | Funakura et al. | Jul 2008 | B2 |
7394946 | Dewaele | Jul 2008 | B2 |
7468075 | Lang et al. | Dec 2008 | B2 |
7517365 | Carignan et al. | Apr 2009 | B2 |
7534263 | Burdulis, Jr. et al. | May 2009 | B2 |
7542791 | Mire et al. | Jun 2009 | B2 |
7547307 | Carson et al. | Jun 2009 | B2 |
7611519 | Lefevre et al. | Nov 2009 | B2 |
7616800 | Paik et al. | Nov 2009 | B2 |
7618421 | Axelson, Jr. et al. | Nov 2009 | B2 |
7618451 | Berez et al. | Nov 2009 | B2 |
7621744 | Massoud | Nov 2009 | B2 |
7621920 | Claypool et al. | Nov 2009 | B2 |
7634119 | Tsougarakis et al. | Dec 2009 | B2 |
7634306 | Sarin et al. | Dec 2009 | B2 |
7641660 | Lakin et al. | Jan 2010 | B2 |
7641663 | Hodorek | Jan 2010 | B2 |
7643862 | Schoenefeld | Jan 2010 | B2 |
7658741 | Claypool et al. | Feb 2010 | B2 |
7682398 | Croxton et al. | Mar 2010 | B2 |
7693321 | Lehtonen-Krause | Apr 2010 | B2 |
7699847 | Sheldon et al. | Apr 2010 | B2 |
D618796 | Cantu et al. | Jun 2010 | S |
D619718 | Gannoe et al. | Jul 2010 | S |
D622854 | Otto et al. | Aug 2010 | S |
7769429 | Hu | Aug 2010 | B2 |
7780681 | Sarin et al. | Aug 2010 | B2 |
7796791 | Tsougarakis et al. | Sep 2010 | B2 |
7799077 | Lang et al. | Sep 2010 | B2 |
D626234 | Otto et al. | Oct 2010 | S |
7806838 | Tsai et al. | Oct 2010 | B2 |
7815645 | Haines | Oct 2010 | B2 |
7881768 | Lang et al. | Feb 2011 | B2 |
D642263 | Park | Jul 2011 | S |
8036729 | Lang et al. | Oct 2011 | B2 |
8160345 | Pavlovskaia et al. | Apr 2012 | B2 |
8221430 | Park et al. | Jul 2012 | B2 |
8311306 | Pavlovskaia et al. | Nov 2012 | B2 |
20020087274 | Alexander et al. | Jul 2002 | A1 |
20020160337 | Klein et al. | Oct 2002 | A1 |
20030009167 | Wozencroft | Jan 2003 | A1 |
20030055502 | Lang et al. | Mar 2003 | A1 |
20040102792 | Sarin et al. | May 2004 | A1 |
20040102866 | Harris et al. | May 2004 | A1 |
20040133276 | Lang et al. | Jul 2004 | A1 |
20040146369 | Kato | Jul 2004 | A1 |
20040147927 | Tsougarakis et al. | Jul 2004 | A1 |
20040152970 | Hunter et al. | Aug 2004 | A1 |
20040153066 | Coon et al. | Aug 2004 | A1 |
20040204760 | Fitz et al. | Oct 2004 | A1 |
20040220583 | Pieczynski, II et al. | Nov 2004 | A1 |
20040243148 | Wasielewski | Dec 2004 | A1 |
20040243481 | Bradbury et al. | Dec 2004 | A1 |
20050059978 | Sherry et al. | Mar 2005 | A1 |
20050065617 | De la Barrera et al. | Mar 2005 | A1 |
20050148843 | Roose | Jul 2005 | A1 |
20050148860 | Liew et al. | Jul 2005 | A1 |
20050192588 | Garcia | Sep 2005 | A1 |
20050201509 | Mostafavi et al. | Sep 2005 | A1 |
20050245934 | Tuke et al. | Nov 2005 | A1 |
20050245936 | Tuke et al. | Nov 2005 | A1 |
20050256389 | Koga et al. | Nov 2005 | A1 |
20050267584 | Burdulis, Jr. et al. | Dec 2005 | A1 |
20060015018 | Jutras et al. | Jan 2006 | A1 |
20060015109 | Haines | Jan 2006 | A1 |
20060015188 | Grimes | Jan 2006 | A1 |
20060036257 | Steffensmeier | Feb 2006 | A1 |
20060110017 | Tsai et al. | May 2006 | A1 |
20060122491 | Murray et al. | Jun 2006 | A1 |
20060155293 | McGinley et al. | Jul 2006 | A1 |
20060155294 | Steffensmeier et al. | Jul 2006 | A1 |
20060195113 | Masini | Aug 2006 | A1 |
20060271058 | Ashton et al. | Nov 2006 | A1 |
20070021838 | Dugas et al. | Jan 2007 | A1 |
20070038059 | Sheffer et al. | Feb 2007 | A1 |
20070055268 | Utz et al. | Mar 2007 | A1 |
20070073305 | Lionberger et al. | Mar 2007 | A1 |
20070100462 | Lang et al. | May 2007 | A1 |
20070118055 | McCombs | May 2007 | A1 |
20070118243 | Schroeder et al. | May 2007 | A1 |
20070123912 | Carson | May 2007 | A1 |
20070162039 | Wozencroft | Jul 2007 | A1 |
20070167833 | Redel et al. | Jul 2007 | A1 |
20070173858 | Engh et al. | Jul 2007 | A1 |
20070191741 | Tsai et al. | Aug 2007 | A1 |
20070198022 | Lang et al. | Aug 2007 | A1 |
20070211928 | Weng et al. | Sep 2007 | A1 |
20070213738 | Martin et al. | Sep 2007 | A1 |
20070226986 | Park et al. | Oct 2007 | A1 |
20070232959 | Couture et al. | Oct 2007 | A1 |
20070233136 | Wozencroft | Oct 2007 | A1 |
20070233140 | Metzger et al. | Oct 2007 | A1 |
20070233141 | Park et al. | Oct 2007 | A1 |
20070233269 | Steines et al. | Oct 2007 | A1 |
20070239167 | Pinczewski et al. | Oct 2007 | A1 |
20070249967 | Buly et al. | Oct 2007 | A1 |
20070276224 | Lang et al. | Nov 2007 | A1 |
20070276400 | Moore et al. | Nov 2007 | A1 |
20070282451 | Metzger et al. | Dec 2007 | A1 |
20070288030 | Metzger et al. | Dec 2007 | A1 |
20080004701 | Axelson et al. | Jan 2008 | A1 |
20080015433 | Alexander et al. | Jan 2008 | A1 |
20080015599 | D'Alessio et al. | Jan 2008 | A1 |
20080015600 | D'Alessio et al. | Jan 2008 | A1 |
20080015602 | Axelson | Jan 2008 | A1 |
20080015606 | D'Alessio et al. | Jan 2008 | A1 |
20080015607 | D'Alessio et al. | Jan 2008 | A1 |
20080031412 | Lang et al. | Feb 2008 | A1 |
20080033442 | Amiot et al. | Feb 2008 | A1 |
20080088761 | Lin et al. | Apr 2008 | A1 |
20080114370 | Schoenefeld | May 2008 | A1 |
20080137926 | Skinner et al. | Jun 2008 | A1 |
20080147072 | Park et al. | Jun 2008 | A1 |
20080153067 | Berckmans et al. | Jun 2008 | A1 |
20080161815 | Schoenefeld et al. | Jul 2008 | A1 |
20080195108 | Bhatnagar et al. | Aug 2008 | A1 |
20080215059 | Carignan et al. | Sep 2008 | A1 |
20080234685 | Gjerde | Sep 2008 | A1 |
20080243127 | Lang et al. | Oct 2008 | A1 |
20080257363 | Schoenefeld et al. | Oct 2008 | A1 |
20080262624 | White et al. | Oct 2008 | A1 |
20080275452 | Lang et al. | Nov 2008 | A1 |
20080281328 | Lang et al. | Nov 2008 | A1 |
20080281329 | Fitz et al. | Nov 2008 | A1 |
20080281426 | Fitz et al. | Nov 2008 | A1 |
20080286722 | Berckmans, III et al. | Nov 2008 | A1 |
20080287953 | Sers | Nov 2008 | A1 |
20080287954 | Kunz et al. | Nov 2008 | A1 |
20080312659 | Metzger et al. | Dec 2008 | A1 |
20080319491 | Schoenefeld | Dec 2008 | A1 |
20090024131 | Metzger et al. | Jan 2009 | A1 |
20090088763 | Aram et al. | Apr 2009 | A1 |
20090093816 | Roose et al. | Apr 2009 | A1 |
20090110498 | Park | Apr 2009 | A1 |
20090112213 | Heavener et al. | Apr 2009 | A1 |
20090131941 | Park et al. | May 2009 | A1 |
20090138020 | Park et al. | May 2009 | A1 |
20090151736 | Belcher et al. | Jun 2009 | A1 |
20090157083 | Park et al. | Jun 2009 | A1 |
20090222014 | Bojarski et al. | Sep 2009 | A1 |
20090222015 | Park et al. | Sep 2009 | A1 |
20090222016 | Park et al. | Sep 2009 | A1 |
20090234217 | Mire et al. | Sep 2009 | A1 |
20090248044 | Amiot et al. | Oct 2009 | A1 |
20090254093 | White et al. | Oct 2009 | A1 |
20090254367 | Belcher et al. | Oct 2009 | A1 |
20090270868 | Park et al. | Oct 2009 | A1 |
20090274350 | Pavlovskaia et al. | Nov 2009 | A1 |
20090306676 | Lang et al. | Dec 2009 | A1 |
20090307893 | Burdulis, Jr. et al. | Dec 2009 | A1 |
20090312805 | Lang et al. | Dec 2009 | A1 |
20100023015 | Park | Jan 2010 | A1 |
20100042105 | Park et al. | Feb 2010 | A1 |
20100152741 | Park et al. | Jun 2010 | A1 |
20100256479 | Park et al. | Oct 2010 | A1 |
20100298894 | Bojarski et al. | Nov 2010 | A1 |
20110282473 | Pavlovskaia et al. | Nov 2011 | A1 |
20120192401 | Pavlovskaia et al. | Aug 2012 | A1 |
20120310400 | Park | Dec 2012 | A1 |
Number | Date | Country |
---|---|---|
3305237 | Aug 1983 | DE |
4341367 | Jun 1995 | DE |
102005023028 | Nov 2006 | DE |
0097001 | Dec 1983 | EP |
0574098 | Dec 1993 | EP |
0622052 | Nov 1994 | EP |
0908836 | Apr 1999 | EP |
0908836 | Dec 1999 | EP |
1486900 | Dec 2004 | EP |
1532939 | May 2005 | EP |
2215610 | Sep 1989 | GB |
2420717 | Jun 2006 | GB |
WO 9325157 | Dec 1993 | WO |
WO 9507509 | Mar 1995 | WO |
WO 9723172 | Jul 1997 | WO |
WO 9812995 | Apr 1998 | WO |
WO 0100096 | Jan 2001 | WO |
WO 0170142 | Sep 2001 | WO |
WO 0296268 | Dec 2002 | WO |
WO 2004032806 | Apr 2004 | WO |
WO 2004049981 | Jun 2004 | WO |
WO 2005051240 | Jun 2005 | WO |
WO 2005087125 | Sep 2005 | WO |
WO 2006058057 | Jun 2006 | WO |
WO 2006060795 | Jun 2006 | WO |
WO 2006092600 | Sep 2006 | WO |
WO 2006134345 | Dec 2006 | WO |
WO 2007014164 | Feb 2007 | WO |
WO 2007058632 | May 2007 | WO |
WO 2007092841 | Aug 2007 | WO |
Entry |
---|
Advisory Action, U.S. Appl. No. 11/642,385, dated Oct. 29, 2010, 3 pages. |
Amendment and Response to Ex Parte Quayle Action, U.S. Appl. No. 29/296,687 dated Mar. 24, 2011, 17 pages. |
Amendment and Response to Final Office Action, U.S. Appl. No. 11/642,385, filed Oct. 4, 2010, 16 pages. |
Amendment and Response to Non-Final Office Action, U.S. Appl. No. 11/641,382, dated Apr. 20, 2010, 23 pages. |
Amendment and Response to Office Action and Petition to Revive, U.S. Appl. No. 10/146,862, filed Jan. 18, 2006, 29 pages. |
Amendment and Response to Office Action, U.S. Appl. No. 11/656,323, filed Jun. 25, 2010, 7 pages. |
Amendment and Response to Office Action, U.S. Appl. No. 11/641,569, dated Feb. 5, 2010, 20 pages. |
Amendment and Response to Restriction Requirement, U.S. Appl. No. 11/641,569, dated May 27, 2009, 12 pages. |
Amendment and Response to Restriction Requirement, U.S. Appl. No. 11/641,382, dated Oct. 5, 2009, 10 pages. |
Amendment and Response to Restriction Requirement, U.S. Appl. No. 11/642,385, filed Nov. 24, 2009, 10 pages. |
Amendment and Response to Restriction/Election Requirement, U.S. Appl. No. 11/656,323, filed Dec. 8, 2009, 6 pages. |
Amendment and Response, U.S. Appl. No. 11/642,385, filed May 28, 2010, 11 pages. |
Amendment and Response to Non-Final Office Action, U.S. Appl. No. 11/959,344, dated Jul. 15, 2011, 13 pages. |
European Search Report, 10192631.9-2310, dated Mar. 17, 2011, 5 pages. |
Ex Parte Quayle Action, U.S. Appl. No. 29/296,687, mailed Jan. 24, 2011, 11 pages. |
Final Office Action and PTO-892, U.S. Appl. No. 11/641,382, mailed Aug. 5, 2010, 13 pages. |
Final Office Action and PTO-892, U.S. Appl. No. 11/656,323, mailed Sep. 3, 2010, 11 pages. |
Final Office Action, U.S. Appl. No. 11/641,569, mailed May 10, 2010, 9 pages. |
International Search Report and Written Opinion, PCT/US2009/034983, dated May 22, 2009, 15 pages. |
International Search Report and Written Opinion, PCT/US2009/034967, dated Jun. 16, 2009, 15 pages. |
International Search Report and Written Opinion, PCT/US2009/041519, dated Jun. 17, 2009, 10 pages. |
International Search Report and Written Opinion, PCT/US2009/040629, dated Aug. 6, 2009, 10 pages. |
International Search Report and Written Opinion, PCT/US2009/051109, dated Nov. 6, 2009, 13 pages. |
International Search Report and Written Opinion, PCT/US2009/058946, dated Jan. 28, 2010, 14 pages. |
International Search Report and Written Opinion, PCT/US2009/068055, dated Mar. 11, 2010, 10 pages. |
International Search Report and Written Opinion, PCT/US2007/001624, dated Dec. 12, 2007, 14 pages. |
International Search Report and Written Opinion, PCT/US2007/001622, dated Jun. 11, 2007, 14 pages. |
International Search Report and Written Opinion, PCT/US2008/083125, dated Mar. 9, 2009, 13 pages. |
International Search Report and Written Opinion, PCT/US2011/032342, dated Jul. 1, 2011, 8 pages. |
Invitation to Pay Additional Fees mailed on Jul. 31, 2007, for PCT Application No. PCT/US2007/001624 filed on Jan. 19, 2007, 5 pages. |
Non-Final Office Action and PTO-892, U.S. Appl. No. 11/641,382, mailed Jan. 20, 2010, 12 pages. |
NonFinal Office Action and PTO-892, U.S. Appl. No. 11/642,385, mailed Mar. 2, 2010, 11 pages. |
Non-Final Office Action and PTO-892, U.S. Appl. No. 11/656,323, mailed Mar. 30, 2010, 10 pages. |
NonFinal Office Action, U.S. Appl. No. 11/641,569, mailed Nov. 12, 2009, 9 pages. |
Nonfinal Office Action, U.S. Appl. No. 11/959,344, dated Feb. 15, 2011, 29 pages. |
Non-Final Office Action, U.S. Appl. No. 11/641,569, dated Aug. 3, 2011, 14 pages. |
Notice of Allowance, U.S. Appl. No. 29/296,687, mailed Mar. 31, 2011, 18 pages. |
Notice of Non-Compliant Amendment, U.S. Appl. No. 11/641,569, mailed Aug. 7, 2009, 3 pages. |
Office Action, U.S. Appl. No. 10/146,862, mailed Jan. 13, 2005, 10 pages. |
Preliminary Amendment, U.S. Appl. No. 11/641,569, dated Aug. 14, 2008, 13 pages. |
Preliminary Amendment, U.S. Appl. No. 11/642,385, filed Aug. 22, 2008, 42 pages. |
RCE/Amendment, U.S. Appl. No. 11/641,569, filed Aug. 9, 2010, 18 pages. |
RCE/Amendment, U.S. Appl. No. 11/642,382, filed Oct. 26, 2010, 14 pages. |
RCE/Amendment, U.S. Appl. No. 11/642,385, filed Dec. 6, 2010, 13 pages. |
RCE/Amendment, U.S. Appl. No. 11/656,323, filed Nov. 19, 2010, 12 pages. |
Response to Notice of Non-Complaint Amendment, U.S. Appl. No. 11/641,569, dated Aug. 19, 2009, 11 pages. |
Response to Restriction Requirement U.S. Appl. No. 29/296,687, filed Oct. 7, 2010, 3 pages. |
Response to Restriction Requirement, U.S. Appl. No. 11/959,344, filed Nov. 24, 2010, 13 pages. |
Response to Restriction Requirement, U.S. Appl. No. 12/390,667, dated Jul. 27, 2011, 8 pages. |
Restriction Requirement, U.S. Appl. No. 11/641,382, mailed Sep. 3, 2009, 6 pages. |
Restriction Requirement, U.S. Appl. No. 11/641,569, mailed Apr. 27, 2009, 7 pages. |
Restriction Requirement, U.S. Appl. No. 11/642,385, mailed Oct. 27, 2009, 7 pages. |
Restriction Requirement, U.S. Appl. No. 11/656,323, mailed Nov. 13, 2009, 10 pages. |
Restriction Requirement, U.S. Appl. No. 11/959,344, dated Oct. 29, 2010, 6 pages. |
Restriction Requirement, U.S. Appl. No. 29/296,687, mailed Sep. 21, 2010, 7 pages. |
Restriction Requirement, U.S. Appl. No. 12/390,667, dated Jul. 14, 2011, 9 pages. |
AKCA, “Matching of 3D Surfaces and Their Intensities,” ISPRS Journal of Photogrammetry & Remote Sensing, 62(2007), 112-121. |
Akenine-Möller et al., Real-Time Rendering, Second Edition, AK Peters, Natick, MA, 6 pages (Table of Contents), 2002. |
Arima et al., “Femoral Rotational Alignment, Based on the Anteroposterior Axis, in Total Knee Arthroplasty in a Valgus Knee. A Technical Note,” Journal Bone Joint Surg Am. 1995;77(9):1331-4. |
Author Unknown, “MRI Protocol Reference,” ConforMIS, Inc., copyright 2007, http://www.conformis.com/Imaging-Professionals/MRI-Protocol-Guides, last visited on Mar. 28, 2008, 18 pages. |
Author Unknown, “MRI Protocol Reference Guide for GE Systems,” ConforMIS, Inc., copyright 2007, http://www.conformis.com/Imaging-Professionals/MRI-Protocol-Guides, last visited on Mar. 28, 2008, 18 pages. |
Author Unknown, “MRI Protocol Reference Guide for Phillips Systems,” ConforMIS, Inc., copyright 2007, http://www.conformis.com/Imaging-Professionals/MRI-Protocol-Guides, last visited on Mar. 28, 2008, 19 pages. |
Author Unknown, “MRI Protocol Reference Guide for Siemens Systems,” ConforMIS, Inc., copyright 2007, http://www.conformis.com/Imaging-Professionals/MRI-Protocol-Guides, last visited on Mar. 28, 2008, 18 pages. |
Barequet et al., “Filling Gaps in the Boundary of a Polyhedron,” Computer Aided Geometric Design, vol. 12, pp. 207-229, 1995. |
Barequet et al., “Repairing CAD Models,” Proceedings of the 8th IEEE Visualization '97 Conference, pp. 363-370, Oct. 1997. |
Bargar et al., “Robotic Systems in Surgery,” Orthopedic and Spine Surgery, Surgical Technology International II, 1993, 419-423. |
Berry et al., “Personalised image-based templates for intra-operative guidance,” Proc. Inst. Mech. Eng. Part H: J. Engineering in Medicine, vol. 219, pp. 111-118, Oct. 7, 2004. |
Besl et al., “A Method for Registration of 3-D Shapes,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 14(2):239-256, Feb. 1992. |
Bi{hacek over (sc)}ević et al., “Variations of Femoral Condyle Shape,” Coll. Antropol., vol. 29 No. 2, pp. 409-414, 2005. |
Blaha et al., “Using the Transepicondylar Axis to Define the Sagittal Morphology of the Distal Part of the Femur,” J Bone Joint Surg Am. 2002;84-A Suppl 2:48-55. |
Blinn, Jim Blinn's Corner—A Trip Down the Graphics Pipeline, Morgan Kaufmann Publishers, Inc., San Francisco, CA, 5 pages (Table of Contents), 1996. |
Bøhn et al., “A Topology-Based Approach for Shell-Closure,” Geometric Modeling for Product Realization (P.R. Wilson et al. editors), pp. 297-319, Elsevier Science Publishers B.V., North-Holland, 1993. |
Bullough et al., “The Geometry of Diarthrodial Joints, Its Physiologic Maintenance and the Possible significance of Age-Related Changes in Geometry-to-Load distribution and the Development of Osteoarthritis,” Clin Orthop Rel Res 1981, 156:61-6. |
Burgkart et al., “Magnetic Resonance Imaging-Based Assessment of Cartilage Loss in Severe Osteoarthritis: Accuracy, Precision, and Diagnostic Value,” Arthritis Rheum 2001, 44:2072-7. |
Canny, “A computational Approach to Edge Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI 8(6), pp. 679-698 (1986). |
Chauhan et al., “Computer-assisted knee arthroplasty versus a conventional jig-based technique—a randomised, prospective trial,” The Journal of Bone and Joint Surgery, vol. 86-B, No. 3, pp. 372-377, Apr. 2004. |
Churchill et al., “The Transepicondylar Axis Approximates the Optimal Flexion Axis of the Knee,” Clin Orthop Relat Res. 1998(356):111-8. |
Cicuttini et al., “Gender Differences in Knee Cartilage Volume as Measured by Magnetic Resonance Imaging,” Osteoarthritis Cartilage 1999, 7:265-71. |
Cicuttini et al., “Longitudinal Study of the Relationship Between Knee angle and Tibiofemoral cartilage Volume in Subjects with Knee Osteoarthritis,” Rheumatology (Oxford) 2004, 43:321-4. |
Cohen et al., Radiosity and Realistic Image Synthesis, Academic Press Professional, Cambridge, MA, 8 pages (Table of Contents), 1993. |
Couglin et al., “Tibial Axis and Patellar Position Relative to the Femoral Epicondylar Axis During Squatting,” The Journal of Arthroplasty, vol. 18, No. 8, Elsevier, 2003. |
Delp et al., “Computer Assisted Knee Replacement,” Clinical Orthopaedics and Related Research, No. 354, pp. 49-56, Sep. 1998. |
Dutré et al., Advanced Global Illumination, AK Peters, Natick, MA, 5 pages (Table of Contents), 2003. |
Eckhoff et al., “Difference Between the Epicondylar and Cylindrical Axis of the Knee,” Clin Orthop Relat Res. 2007;461:238-44. |
Eckhoff et al., “Three-Dimensional Mechanics, Kinematics, and Morphology of the Knee Viewed in Virtual Reality,” The Journal of Bone and Joint Surgery, vol. 87-A, Supplement 2, pp. 71-80, 2005. |
Eisenhart-Rothe et al., “Femorotibial and Patellar Cartilage Loss in Patients Prior to Total Knee arthroplasty, Heterogeneity, and Correlation with alignment of the Knee,” Ann Rheum Dis., Jun. 2005 (BMJ Publishing Group Ltd & European League Against Rheumatism). |
Eisenhart-Rothe et al., “The Role of Knee alignment in Disease Progression and Functional Decline in Knee Osteoarthritis,” JAMA 2001, 286:188-95. |
Elias et al., “A Correlative Study of the Geometry and anatomy of the Distal Femur,” Clin orthop Relat Res. 1990(260):98-103. |
Erikson, “Error Correction of a Large Architectural Model: The Henderson County Courthouse,” Technical Report TR95-013, Dept. of Computer Science, University of North Carolina at Chapel Hill, pp. 1-11, 1995. |
Ervin et al., Landscape Modeling, McGraw-Hill, New York, NY, 8 pages (Table of Contents), 2001. |
Farin, NURB Curves and Surfaces: From Projective Geometry to Practical Use, AK Peters, Wellesley, MA, 7 pages (Table of Contents), 1995. |
Favorito et al., “Total Knee Arthroplasty in the Valgus Knee,” Journal Am Acad Orthop surg. 2002;10(1):16-24. |
Fleischer et al., “Accurate Polygon Scan Conversion Using Half-Open Intervals,” Graphics Gems III, pp. 362-365, code: pp. 599-605, 1992. |
Foley et al., Computer Graphics: Principles and Practice, Addison-Wesley Publishing Company, Reading, MA, 9 pages (Table of Contents), 1990. |
Freeman et al., “The Movement of the Knee Studied by Magnetic Resonance Imaging,” Clinical orthop Relat Res. 2003(410):35-43. |
Freeman et al., “The Movement of the Normal Tibio-Femoral Joint,” Journal Biomech. 2005;38(2):197-208. |
Glassner (editor), An Introduction to Ray Tracing, Academic Press Limited, San Diego, CA, 4 pages (Table of Contents), 1989. |
Glassner, Principles of Digital Image Synthesis, Volumes One and Two, Morgan Kaufmann Publishers, Inc., San Francisco, CA, 32 pages (Table of Contents), 1995. |
Gooch et al., Non-Photorealistic Rendering, AK Peters, Natick, MA, 4 pages (Table of Contents), 2001. |
Graichen et al., “Quantitative Assessment of Cartilage Status in Osteoarthritis by Quantitative Magnetic Resonance Imaging: Technical Validation for Use in analysis of Cartilage Volume and Further Morphologic Parameters,” Arthritis Rheum 2004, 50:811-16. |
Gruen et al., “Least Squares 3D Surface and Curve Matching,” ISPRS Journal of Photogrammetry & Remote Sensing, 59(2005), 151-174. |
Grüne et al., “On numerical algorithm and interactive visualization for optimal control problems,” Journal of Computation and Visualization in Science, vol. 1, No. 4, pp. 221-229, Jul. 1999. |
Guéziec et al., “Converting Sets of Polygons to Manifold Surfaces by Cutting and Stitching,” Proc. IEEE Visualization 1998, pp. 383-390, Oct. 1998. |
Hafez et al., “Patient Specific Instrumentation for TKA: Testing the Reliability Using a Navigational System,” MIS Meets CAOS Symposium & Instructional Academy, Less and Minimally Invasive Surgery for Joint Arthroplasty: FACT and FICTION Syllabus, San Diego, CA, 8 pages, Oct. 20-22, 2005. |
Hafez et al., “Computer Assisted Total Knee Replacement: Could a Two-Piece Custom Template Replace the Complex Conventional Instrumentations?”, Computer Aided Surgery, vol. 9, No. 3, pp. 93-94, 2004. |
Hafez et al., “Computer-Assisted Total Knee Arthroplasty Using Patient-Specific Templating,” Clinical Orthopaedics and Related Research, No. 0, pp. 1-9, 2006. |
Hollister et al., “The Axes of Rotation of the Knee,” Clin Orthop Relat Res. 1993(290):259-68. |
Howell et al., “Longitudinal Shapes of the Tibia and Femur are Unrelated and Variable,” Clinical Orthopaedics and Related Research (2010) 468: 1142-1148. |
Howell et al., “Results of an Initial Experience with Custom-Fit Positioning Total Knee Arthroplasty in a Series of 48 Patients,” Orthopedics, 2008;31(9):857-63. |
Howell et al., “In Vivo Adduction and Reverse Axial Rotation (External) of the Tibial Component can be Minimized During Standing and Kneeling,” Orthopedics, In Press. |
Iwaki et al., “Tibiofemoral Movement 1: The Shapes and Relative Movements of the Femur and Tibia in the Unloaded Cadaver Knee,” Journal Bone Joint Surg Br. 2000;82(8):1189-95. |
Jensen, Realistic Image Synthesis Using Photon Mapping, AK Peters, Natick, MA, 7 pages (Table of Contents), 2001. |
Jacobs et al., “Hip Resurfacing Through an Anterolateral Approach,” J. Bone Joint Surg Am. 2008:90 Suppl 3:38-44. |
Johnson, “Joint Remodeling as the Basis for Osteoarthritis,” Journal Am Vet Med Assoc. 1962, 141:1233-41. |
Jones et al., “A new approach to the construction of surfaces from contour data,” Computer Graphics Forum, vol. 13, No. 3, pp. 75-84, 1994 [ISSN 0167-7055]. |
Kass et al., “Active Contour Models,” International Journal of Computer Vision, pp. 321-331 (1988). |
Kellgren et al., “Radiological Assessment of Osteoarthrosis,” Ann Rheum Dis 1957, 10:494-501. |
Kessler et al, “Sagittal Curvature of Total Knee Replacements Predicts in vivo Kinematics,” Clin Biomech (Bristol, Avon) 2007; 22(1):52-8. |
Khorramabadi, “A Walk Through the Planned CS Building,” Technical Report UCB/CSD 91/652, Computer Science Department, University of California at Berkeley, 74 pages, 1991. |
Kidder et al., “3-D Model Acquisition, Design, Planning and Manufacturing of Orthopaedic Devices: A Framework,” Advanced Sensor and Control-System Interface (B.O. Nnaji editor), Proceedings SPIE—The International Society for Optical Engineering, Bellingham, WA, vol. 2911, pp. 9-22, Nov. 21-22, 1996. |
Kienzel III et al., “Total Knee Replacement,” IEEE May/Jun. 1995. |
Kienzel III et al., “An Integrated CAD-Robotics System for Total Knee Replacement Surgery”, IEEE International Conference, pp. 889-894, vol. 1, May 1993. |
Krackow et al., “Flexion-Extension Joint Gap Changes After Lateral Structure Release for Valgus Deformity Correction in Total Knee Arthroplasty: A Cadaveric Study,” Journal Arthroplasty, 1999;14(8):994-1004. |
Krackow et al., “Primary Total Knee Arthroplasty in Patients with Fixed Valgus Deformity,” Clin Orthop Relat Res. 1991(273):9-18. |
Krackow, “Approaches to Planning lower Extremity alignment for Total Knee arthroplasty and Osteotomy About the Knee,” adv Orthop surg 7:69, 1983. |
Kumar, Robust Incremental Polygon Triangulation for Surface Rendering, Center for Geometric Computing, Department of Computer Science, Johns Hopkins University, Baltimore, MD, WSCG, The International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, pp. 381-388, 2000. |
Kunz et al., “Computer Assisted Hip Resurfacing Using Individualized Drill Templates,” The Journal of Arthroplasty, vol. 00, No. 0, pp. 1-7, 2009. |
Kusumoto et al., “Application of Virtual Reality Force Feedback Haptic Device for Oral Implant Surgery”, Graduate School of Dentistry Course for Integrated Oral Science and Stomatology, Jun. 16, 2005. |
Lea et al., “Registration and immobilization in robot-assisted surgery”, Journal of Image Guided Surgery, pp. 1-10, 1995. |
Lorensen et al., “Marching Cubes: A High Resolution 3d Surface Construction Algorithm,” Computer Graphics, vol. 21, No. 4, pp. 163-169, 1987. |
Manner et al., “Knee Deformity in Congenital Longitudinal Deficiencies of the Lower Extremity,” Clin Orthop Relat Res. 2006;448:185-92. |
Matsuda et al., “Anatomical Analysis of the Femoral Condyle in Normal and Osteoarthritic Knees,” Journal Orthopaedic Res. 2004;22(1):104-9. |
Matsuda et al., “Femoral Condyle Geometry in the Normal and Varus Knee,” Clinical Orthop Relat Res. 1998(349):183-8. |
Messmer et al., “Volumetric Determination of the Tibia Based on 2d Radiographs Using a 2d/3d Database”, Dept. of Surgery, Trauma Unit, University Hospital, Bassel, Switzerland, Computer Aided Surgery 6:183-194 (2001). |
Mihalko et al., The Variability of Intramedullary Alignment of the Femoral Component During Total Knee Arthroplasty, Journal Arthroplasty. 2005;20(1):25-8. |
Mole et al., “A New Three-Dimensional Treatment Algorithm for Complex Surfaces: Applications in Surgery”, Feb. 1995. |
Morvan et al., IVECS, Interactively Correcting .STL Files in a Virtual Environment, Clemson University, Clemson, SC, Proc. Conf. Virtual Design, Aug. 1996. |
Nooruddin et al., Simplification and Repair of Polygonal Models Using Volumetric Techniques, IEEE Transactions on Visualization and Computer Graphics, vol. 9, No. 2, pp. 191-205, Apr.-Jun. 2003. |
Panjabi et al., “Errors in Kinematic Parameters of a Planar Joint: Guidelines for Optimal Experimental Design,” Journal Biomech. 1982;15(7):537-44. |
Perillo-Marcone et al., “Effect of Varus/Valgus Malalignment on Bone Strains in the Proximal Tibia After TKR: An Explicit Finite element Study,” Journal Biomechanical Engineering 2007, vol. 129, 1:1-11. |
Peterfy et al., “Quantification of articular Cartilage in the Knee with Pulsed Saturation Transfer Subtraction and Fact-Suppressed MR Imaging: Optimization and Validation,” Radiology 1994, 192:485-91. |
Pinskerova et al., “The Shapes and Relative Movements of the Femur and Tibia at the Knee,” Orthopaedics 2000;29 Suppl 1:S3-5. |
Platt et al., “Mould Arthroplasty of the Knee, A Ten-Year Follow-up Study,” The Journal of Bone and Joint Surgery (British Volume), vol. 51-B, No. 1, pp. 76-87, Feb. 1969. |
Potter, “Arthroplasty of the Knee with Tibial Metallic Implants of the McKeever and MacIntosh Design,” The Surgical Clinics of North America, vol. 49, No. 4, pp. 903-915, Aug. 1969. |
Radermacher et al., “Computer Assisted Orthopaedic Surgery with Image Based Individual Templates,” Clinical Orthopaedics and Related Research, vol. 354, pp. 28-38, Sep. 1998. |
Rohlfing et al., “Quo Vadis, Atlas-Based Segmentation?”, The Handbook of Medical Image Analysis: Segmentation and Registration Models (Kluwer), pp. 1-55, (http://www.stanford.edu/˜rohlfing/publications/2005-rohlfing-chapter-quo—vadis—atlas—based—segmentation.pdf). |
Rosset et al., “General Consumer Communication Tools for Improved Image Management and Communication in Medicine,” Journal Digital Imaging, 2005;18(4):270-9. |
Shakespeare D., “Conventional Instruments in Total Knee Replacement: What Should We Do With Them?” Knee. 2006;13(1):1-6. |
Shepstone et al., “The shape of the Distal Femur: A Palaeopathological Comparison of Eburnated and Non-Eburnated Femora,” Ann. Rheum Dis. 1999, 58:72-8. |
Shirley et al., Realistic Ray Tracing, Second Edition, AK Peters, Natick, MA, 7 pages (Table of Contents), 2003. |
Siston et al., “The Variability of Femoral Rotational Alignment in Total Knee Arthroplasty,” Journal Bone Joint Surg Am. 2005;87(10):2276-80. |
Siston et al., “Averaging Different Alignment Axes Improves Femoral Rotational Alignment in Computer-Navigated Total Knee Arthroplasty,” Journal Bone Joint Surg Am. 2008;90(10):2098-104. |
Soudan et al., “Methods, Difficulties and Inaccuracies in the Study of Human Joint Kinematics and Pathokinematics by the Instant axis Concept. Example: The Knee Joint,” Journal Biomech.1979;12(1):27-33. |
Spencer et al., “Initial Experience with Custom-Fit Total Knee Replacement: Intra-operative Events and Long-Leg Coronal alignment,” International Orthopaedics (SICOT), 2009:In Press. |
Strothotte et al., Non-Photorealistic Computer Graphics—Modeling, Rendering, and Animation, Morgan Kaufmann Publishers, San Francisco, CA, 9 pages (Table of Contents), 2002. |
Stulberg et al., “Computer- and Robot-Assisted Orthopaedic Surgery”, Computer-Integrated Surgery Technology and Clinical Applications, edited by Taylor et al., Massachusetts Institute of Technology, Chapter 27, pp. 373-378, 1996. |
Teeny et al., “Primary Total Knee Arthroplasty in Patients with Severe Varus Deformity. A Comparative Study,” Clin Orthop Relat Res. 1991(273):19-31. |
Vande Berg et al., “Assessment of Knee Cartilage in Cadavers with Dual-Detector Spiral CT Arthrography and MR Imaging,” Radiology, vol. 222, No. 2, pp. 430-436, Feb. 2002. |
Wright Medical Technology, Inc., “Prophecy Pre-Operative Naviation Guides Surgical Technique,” 2009. |
Wikipedia, the Free Encyclopedia, “CNC,” (date unknown) located at http://en.wikipedia.org/wiki/CNC>, 6 pages, last visited on Apr. 12, 2007. |
U.S. Appl. No. 13/488,505, filed Jun. 5, 2012, Ilwhan Park et al. |
U.S. Appl. No. 29/296,687, filed Oct. 25, 2007, Park. |
U.S. Appl. No. 10/146,862 (abandoned), filed May 15, 2002, Park et al. |
U.S. Appl. No. 13/086,275, filed Apr. 13, 2011, Park et al. |
U.S. Appl. No. 13/066,568, filed Apr. 18, 2011, Pavlovskaia et al. |
U.S. Appl. No. 29/394,882, filed Jun. 22, 2011, Ilwhan Park. |
Final Office Action, U.S. Appl. No. 11/959,344, mailed Oct. 27, 2011, 12 pages. |
Final Office Action, U.S. Appl. No. 12/390,667, mailed Jan. 13, 2012, 27 pages. |
Non-Final Office Action, U.S. Appl. No. 11/924,425, mailed Jan. 25, 2012, 35 pages. |
Non-Final Office Action, U.S. Appl. No. 12/390,667, dated Aug. 24, 2011, 49 pages. |
Non-Final Office Action, U.S. Appl. No. 11/946,002, dated Nov. 25, 2011, 44 pages. |
Non-Final Office Action, U.S. Appl. No. 12/386,105, dated Feb. 9, 2012, 30 pages. |
Non-Final Office Action, U.S. Appl. No. 12/391,008, mailed Oct. 31, 2011, 44 pages. |
Notice of Allowance, U.S. Appl. No. 13/066,568, mailed Oct. 26, 2011, 28 pages. |
Response to Final Office Action, U.S. Appl. No. 11/959,344, filed Dec. 27, 2011, 16 pages. |
Response to Non-Final Office Action, U.S. Appl. No. 12/390,667, filed Nov. 18, 2011, 16 pages. |
Response to Non-Final Office Action, U.S. Appl. No. 11/641,569, filed Dec. 2, 2011, 7 pages. |
Response to Restriction Requirement, U.S. Appl. No. 12/391,008, filed Aug. 29, 2011, 9 pages. |
Response to Restriction Requirement, U.S. Appl. No. 12/386,105, filed Dec. 21, 2011, 9 pages. |
Response to Restriction, U.S. Appl. No. 11/924,425, filed Nov. 8, 2011, 5 pages. |
Response to Restriction, U.S. Appl. No. 11/946,002, filed Sep. 23, 2011, 7 pages. |
Restriction Requirement, U.S. Appl. No. 11/924,425, dated Oct. 13, 2011, 6 pages. |
Restriction Requirement, U.S. Appl. No. 11/946,002, dated Sep. 1, 2011, 8 pages. |
Restriction Requirement, U.S. Appl. No. 12/386,105, dated Oct. 24, 2011, 7 pages. |
Restriction Requirement, U.S. Appl. No. 12/391,008, dated Aug. 18, 2011, 6 pages. |
Advisory Action and Interview Summary, U.S. Appl. No. 12/390,667, mailed Apr. 27, 2012, 23 pages. |
Appeal Brief, U.S. Appl. No. 12/390,667, filed Jul. 12, 2012, 32 pages. |
Final Office Action, U.S. Appl. No. 11/641,382, mailed Jul. 25, 2012, 12 pages. |
Final Office Action, U.S. Appl. No. 11/641,569, mailed Mar. 1, 2012, 12 pages. |
Final Office Action, U.S. Appl. No. 11/924,425, mailed Jul. 6, 2012, 14 pages. |
Final Office Action, U.S. Appl. No. 11/946,002, mailed May 9, 2012, 24 pages. |
Final Office Action, U.S. Appl. No. 12/391,008, mailed May 17, 2012, 28 pages. |
Non-Final Office Action, U.S. Appl. No. 11/641,382, mailed Mar. 29, 2012, 24 pages. |
Non-Final Office Action, U.S. Appl. No. 12/111,924, mailed Jun. 29, 2012, 35 pages. |
Non-Final Office Action, U.S. Appl. No. 12/546,545, mailed Jul. 19, 2012, 28 pages. |
Non-Final Office Action, U.S. Appl. No. 12/636,939, mailed Jul. 20, 2012, 25 pages. |
Non-Final Office Action, U.S. Appl. No. 13/374,960, mailed Aug. 1, 2012, 6 pages. |
Notice of Allowance, U.S. Appl. No. 11/959,344, mailed Mar. 5, 2012, 13 pages. |
Notice of Allowance, U.S. Appl. No. 12/386,105, mailed Jul. 5, 2012, 11 pages. |
Response to Final Office Action, U.S. Appl. No. 11/641,569, filed Jun. 28, 2012, 10 pages. |
Response to Final Office Action, U.S. Appl. No. 12/390,667, filed Mar. 12, 2012, 19 pages. |
Response to Non-Final Office Action, U.S. Appl. No. 12/391,008, filed Feb. 24, 2012, 18 pages. |
Response to Non-Final Office Action, U.S. Appl. No. 11/946,002, filed Mar. 8, 2012, 16 pages. |
Response to Non-Final Office Action, U.S. Appl. No. 11/924,425, filed Apr. 25, 2012, 8 pages. |
Response to Non-Final Office Action, U.S. Appl. No. 12/386,105, filed Jun. 8, 2012, 13 pages. |
Response to Non-Final Office Action, U.S. Appl. No. 11/641,382, filed Jun. 27, 2012, 12 pages. |
Response to Restriction Requirement, U.S. Appl. No. 12/111,924, filed Apr. 16, 2012, 8 pages. |
Response to Restriction Requirement, U.S. Appl. No. 12/636,939, filed Apr. 19, 2012, 6 pages. |
Response to Restriction, U.S. Appl. No. 12/505,056, filed Apr. 11, 2012, 9 pages. |
Response to Restriction, U.S. Appl. No. 12/546,545, filed Jun. 4, 2012, 7 pages. |
Restriction Requirement, U.S. Appl. No. 12/111,924, mailed Mar. 19, 2012, 8 pages. |
Restriction Requirement, U.S. Appl. No. 12/505,056, mailed Mar. 14, 2012, 8 pages. |
Restriction Requirement, U.S. Appl. No. 12/546,545, mailed May 3, 2012, 8 pages. |
Restriction Requirement, U.S. Appl. No. 12/636,939, mailed Apr. 13, 2012, 6 pages. |
U.S. Appl. No. 13/573,662, filed Oct. 2, 2012, Pavlovskaia et al. |
Howell et al., “In Vivo Adduction and Reverse Axial Rotation (External) of the Tibial Component can be Minimized During Standing and Kneeling,” Orthopedics|ORTHOSupersite.com vol. 32 No. 5, 319-326 (May 2009). |
Amendment Under 37 C.F.R. 1.312, U.S. Appl. No. 12/386,105, filed Oct. 1, 2012, 6 pages. |
Appeal Brief, U.S. Appl. No. 12/391,008, filed Oct. 16, 2012, 24 pages. |
Examiner's Answer in appeal, U.S. Appl. No. 12/391,008, mailed Dec. 13, 2012, 27 pages. |
Non-Final Office Action, U.S. Appl. No. 12/390,667, mailed Sep. 26, 2012, 21 pages. |
Notice of Allowance, U.S. Appl. No. 11/641,382, mailed Oct. 9, 2012, 9 pages. |
Notice of Allowance, U.S. Appl. No. 11/924,425, mailed Sep. 25, 2012, 18 pages. |
Notice of Allowance, U.S. Appl. No. 13/374,960, mailed Nov. 2, 2012, 24 pages. |
RCE/Amendment, U.S. Appl. No. 11/946,002, filed Sep. 6, 2012, 38 pages. |
Response to Final Office Action, U.S. Appl. No. 11/641,382, filed Sep. 24, 2012, 11 pages. |
Response to Final Office Action, U.S. Appl. No. 11/924,425, filed Sep. 5, 2012, 9 pages. |
Response to Non-Final Office Action, U.S. Appl. No. 12/111,924, filed Sep. 28, 2012, 10 pages. |
Response to Non-Final Office Action, U.S. Appl. No. 12/636,939, filed Oct. 10, 2012, 8 pages. |
Response to Non-Final Office Action, U.S. Appl. No. 12/546,545, filed Oct. 19, 2012, 15 pages. |
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