Constrained Statistical Modelling of Knee Flexion From Multi-Pose Magnetic Resonance Imaging

Yu, Weimin; Zheng, Guoyan (2016). Constrained Statistical Modelling of Knee Flexion From Multi-Pose Magnetic Resonance Imaging. IEEE transactions on medical imaging, 35(7), pp. 1686-1695. Institute of Electrical and Electronics Engineers IEEE 10.1109/TMI.2016.2524587

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Abstract—Reconstruction of the anterior cruciate ligament (ACL) through arthroscopy is one of the most common procedures in orthopaedics. It requires accurate alignment and drilling of the tibial and femoral tunnels through which the ligament graft is attached. Although commercial computer-assisted navigation systems exist to guide the placement of these tunnels, most of them are limited to a fixed pose without due consideration of dynamic factors involved in different knee flexion angles. This paper presents a new model for intraoperative guidance of arthroscopic ACL reconstruction with reduced error particularly in the ligament attachment area. The method uses 3D preoperative data at different flexion angles to build a subject-specific statistical model of knee pose. To circumvent the problem of limited training samples and ensure physically meaningful pose instantiation, homogeneous transformations between different poses and local-deformation finite element modelling are used to enlarge the training set. Subsequently, an anatomical geodesic flexion analysis is performed to extract the subject-specific flexion characteristics. The advantages of the method were also tested by detailed comparison to standard Principal Component Analysis (PCA), nonlinear PCA without training set enlargement, and other state-of-the-art articulated joint modelling methods. The method yielded sub-millimetre accuracy, demonstrating its potential clinical value.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB [discontinued]

UniBE Contributor:

Yu, Weimin and Zheng, Guoyan

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health

ISSN:

0278-0062

Publisher:

Institute of Electrical and Electronics Engineers IEEE

Language:

English

Submitter:

Weimin Yu

Date Deposited:

24 May 2017 10:03

Last Modified:

24 May 2017 10:03

Publisher DOI:

10.1109/TMI.2016.2524587

PubMed ID:

26863651

BORIS DOI:

10.7892/boris.96684

URI:

https://boris.unibe.ch/id/eprint/96684

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