PROJECT TITLE :
Constrained statistical modelling of knee flexion From multi-pose magnetic resonance imaging - 2016
Reconstruction of the anterior cruciate ligament (ACL) through arthroscopy is one amongst the most common procedures in orthopaedics. It requires accurate alignment and drilling of the tibial and femoral tunnels through which the ligament graft is connected. Although business laptop-assisted navigation systems exist to guide the location of those tunnels, most of them are limited to a fastened create without due consideration of dynamic factors involved in several knee flexion angles. This paper presents a brand new model for intraoperative guidance of arthroscopic ACL reconstruction with reduced error significantly within the ligament attachment space. The strategy uses 3D preoperative information at completely different flexion angles to build an issue-specific statistical model of knee cause. To circumvent the problem of limited training samples and guarantee physically meaningful create instantiation, homogeneous transformations between completely different poses and local-deformation finite component modelling are used to enlarge the coaching set. Subsequently, an anatomical geodesic flexion analysis is performed to extract the topic-specific flexion characteristics. The benefits of the tactic were additionally tested by detailed comparison to straightforward Principal Component Analysis (PCA), nonlinear PCA while not training set enlargement, and different state-of-the-art articulated joint modelling methods. The method yielded sub-millimetre accuracy, demonstrating its potential clinical price.
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