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Osteoarthritis Cartilage. 2015 Oct;23(10):1695-703. doi: 10.1016/j.joca.2015.05.027. Epub 2015 Jun 5.

Three-dimensional MRI-based statistical shape model and application to a cohort of knees with acute ACL injury.

Author information

1
Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA. Electronic address: valentina.pedoia@ucsf.edu.
2
Department of Orthopaedic Surgery, University of California, San Francisco, USA. Electronic address: LansdownDA@orthosurg.ucsf.edu.
3
Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA. Electronic address: musa.zaid@ucsf.edu.
4
Department of Epidemiology & Biostatistics, University of California, San Francisco, USA. Electronic address: CMcCulloch@psf.ucsf.edu.
5
Department of Orthopaedic Surgery, University of California, San Francisco, USA. Electronic address: richard.souza@ucsf.edu.
6
Department of Orthopaedic Surgery, University of California, San Francisco, USA. Electronic address: MaBen@orthosurg.ucsf.edu.
7
Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA. Electronic address: xiaojuan.li@ucsf.edu.

Abstract

OBJECTIVE:

The aim of this study is to develop a novel 3D magnetic resonance imaging (MRI)-based Statistical Shape Modeling (SSM) and apply it in knee MRIs in order to extract and compare relevant shapes of the tibia and femur in patients with and without acute Anterior cruciate ligament (ACL) injuries.

METHODS:

Bilateral MR images were acquired and analyzed for 50 patients with acute ACL injuries and for 19 control subjects. A shape model was extracted for the tibia and femur using an SSM algorithm based on a set of matched landmarks that are computed in a fully automatic manner.

RESULTS:

Shape differences were detected between the knees in the ACL-injury group and control group, suggesting a common shape feature that may predispose these knees to injury. Some of the detected shape features that discriminate between injured and control knees are related to intercondylar width and posterior tibia slope, features that have been suggested in previous studies as ACL morphological risk factors. However, shape modeling has the great potential to quantify these characteristics with a comprehensive description of the surfaces describing complex 3D deformation that cannot be represented with simple geometric indexes.

CONCLUSIONS:

3D MRI-based bone shape quantification has the ability to identify specific anatomic risk factors for ACL injury. A better understanding of the role in bony shape on ligamentous injuries could help in the identification of subjects with an increased risk for an ACL tear and to develop targeted prevention strategies, including education and training.

KEYWORDS:

Anterior cruciate ligament; Magnetic resonance imaging; Osteoarthritis; Statistical shape modeling

PMID:
26050865
PMCID:
PMC5019115
DOI:
10.1016/j.joca.2015.05.027
[Indexed for MEDLINE]
Free PMC Article

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