Three-Dimensional Morphology and Kinematics of the Distal Part of the Femur Viewed in Virtual Reality

Eckhoff, Donald G.; Bach, Joel M.; Spitzer, Victor M.; Reinig, Karl D.; Bagur, Michelle M.; Baldini, Todd; Rubinstein, David; Humphries, Stephen

doi:10.2106/00004623-200300004-00012

Cited by 101 publications

(106 citation statements)

References 9 publications

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“…The first applied a cylinder of best fit to the medial femoral condyle and a second, concentric cylinder to the lateral femoral condyle in a similar approach as a previously described method [10]. The second algorithm modified this approach by applying a single cylinder of best fit to both condyles.…”

Section: Methodsmentioning

confidence: 99%

Is Femoral Component Rotation in a TKA Reliably Guided by the Functional Flexion Axis?

Oussedik

Scholes

Ferguson

et al. 2012

Clinical Orthopaedics &Amp; Related Research

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Background The position of the femoral component in a TKA in the axial plane influences patellar tracking and flexion gap symmetry. Errors in femoral component rotation have been implicated in the need for early revision surgery. Methods of guiding femoral component rotation at the time of implantation typically are derived from the mean position of the flexion-extension axis across experimental subjects. The functional flexion axis (FFA) of the knee is kinematically derived and therefore a patient-specific reference axis that can be determined intraoperatively by a computer navigation system as an alternative method of guiding femoral component rotation. However, it is unclear whether the FFA is reliable and how it compares with traditional methods. Question/purposes We asked if the FFA could be measured reproducibly at different stages of the operative procedure; (2) where it lies in relation to a CT-derived gold standard; and (3) how it compares with more traditional methods of judging femoral component rotation. Methods Thirty-seven patients undergoing elective TKAs were recruited to the study. Preoperative CT scans were obtained and the transepicondylar axis (TEA) was identified. The TKA then was performed using computer navigation. The FFA was derived before incision and again after the surgical approach and osseous registration. The navigation system was used to register the surgical TEA. The FFA and surgical TEA then were compared with the CT-derived TEA. Results The mean preincision FFA was similar to the intraoperative FFA and therefore deemed reproducible. We observed no differences in variability between surgical TEA and preincision FFA. The FFA was different from the CT-TEA and judged similar in accuracy to the surgical TEA. Conclusion The reliability and accuracy of the FFA were similar to those of other intraoperative methods. Further evaluation is required to ascertain whether the FFA improves on currently available methods for determining the ideal rotation of the femoral component during TKA.

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Section: Methodsmentioning

confidence: 99%

Is Femoral Component Rotation in a TKA Reliably Guided by the Functional Flexion Axis?

Oussedik

Scholes

Ferguson

et al. 2012

Clinical Orthopaedics &Amp; Related Research

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“…Recent studies have suggested that the natural alignment of the knee in relation to the mechanical axis can be extremely variable [2][3][4]. Therefore, functional improvements in total knee replacement might focus on restoring the natural kinematics of the joint rather than aligning to a standardised mechanical axis.…”

mentioning

confidence: 99%

Initial experience with custom-fit total knee replacement: intra-operative events and long-leg coronal alignment

Spencer

Mont

McGrath

et al. 2008

International Orthopaedics (SICOT)

126

117

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New technology using magnetic resonance imaging (MRI) allows the surgeon to place total knee replacement components into each patient's pre-arthritic natural alignment. This study evaluated the initial intraoperative experience using this technique. Twenty-one patients had a sagittal MRI of their arthritic knee to determine component placement for a total knee replacement. Cutting guides were machined to control all intraoperative cuts. Intra-operative events were recorded and these knees were compared to a matching cohort of the senior surgeon's previous 30 conventional total knee replacements. Post-operative scanograms were obtained from each patient and coronal alignment was compared to previous studies using conventional and computer-assisted techniques. There were no intra-operative or acute postoperative complications. There were no differences in blood loss and there was a mean decrease in operative time of 14% compared to a cohort of patients with conventional knee replacements. The average deviation from the mechanical axis was 1.2°of varus, which was comparable to previously reported conventional and computer-assisted techniques. Custom-fit total knee replacement appeared to be a safe procedure for uncomplicated cases of osteoarthritis.

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“…Accuracy measurements include median distance between surfaces, average distance between surfaces, volume difference and volume overlap difference. In Table 1, we compare our proposed understanding the kinematic function of the knee, but also has clinical significance in areas of total knee arthroplasty and anterior cruciate ligament reconstruction [14]. The automatic segmentation of condyle surface is useful to knee disease diagnosis and therapy planning.…”

Section: Resultsmentioning

confidence: 99%

Cross Modality Deformable Segmentation Using Hierarchical Clustering and Learning

Zhan

Dewan

Zhou

2009

Lecture Notes in Computer Science

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Abstract. Segmentation of anatomical objects is always a fundamental task for various clinical applications. Although many automatic segmentation methods have been designed to segment specific anatomical objects in a given imaging modality, a more generic solution that is directly applicable to different imaging modalities and different deformable surfaces is desired, if attainable. In this paper, we propose such a framework, which learns from examples the spatially adaptive appearance and shape of a 3D surface (either open or closed). The application to a new object/surface in a new modality requires only the annotation of training examples. Key contributions of our method include: (1) an automatic clustering and learning algorithm to capture the spatial distribution of appearance similarities/variations on the 3D surface. More specifically, the model vertices are hierarchically clustered into a set of anatomical primitives (sub-surfaces) using both geometric and appearance features. The appearance characteristics of each learned anatomical primitive are then captured through a cascaded boosting learning method. (2) To effectively incorporate non-Gaussian shape priors, we cluster the training shapes in order to build multiple statistical shape models. (3) To our best knowledge, this is the first time the same segmentation algorithm has been directly employed in two very diverse applications: a. Liver segmentation (closed surface) in PET-CT, in which CT has very low-resolution and low-contrast; b. Distal femur (condyle) surface (open surface) segmentation in MRI.

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Three-Dimensional Morphology and Kinematics of the Distal Part of the Femur Viewed in Virtual Reality

Cited by 101 publications

References 9 publications

Is Femoral Component Rotation in a TKA Reliably Guided by the Functional Flexion Axis?

Is Femoral Component Rotation in a TKA Reliably Guided by the Functional Flexion Axis?

Initial experience with custom-fit total knee replacement: intra-operative events and long-leg coronal alignment

Cross Modality Deformable Segmentation Using Hierarchical Clustering and Learning

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