The aim of this study was to describe a quantitative 3D CT method to measure rotator cuff muscle volume, atrophy, and balance in healthy controls and in three pathological shoulder cohorts. In all, 102 CT scans were included in the analysis: 46 healthy, 21 cuff tear arthropathy (CTA), 18 irreparable rotator cuff tear (IRCT), and 17 primary osteoarthritis (OA). The four rotator cuff muscles were manually segmented and their volume, including intramuscular fat, was calculated. The normalized volume (NV) of each muscle was calculated by dividing muscle volume to the patient’s scapular bone volume. Muscle volume and percentage of muscle atrophy were compared between muscles and between cohorts.Aims
Methods
Active Shape Models (ASM) have been widely used in the literature for the extraction of the tibial and the femoral bones from MRI. These methods use Statistical Shape Models (SSM) to drive the deformation and make the segmentation more robust. One crucial step for building such SSM is the shape correspondence (SC). Several methods have been described in the literature. The goal of this paper is to compare two SC methods, the 28 MRI of the knee have been used. The validation has been performed by using the leave-one-out cross-validation technique. An ASMMDL and an ASMIMCP-GMMM has been built with the SSMs computed respectively with the MDL and IMCP-GMM methods. The computation time for building both SSMs has been also measured. For 90% of data, the error is inferior to 1.78 mm and 1.85 mm for respectively the ASMIMCP-GMM and the ASMMDL methods. The computation time for building the SSMs is five hours and two days for respectively the IMCP-GMM and the MDL methods. Both methods seem to give, at least, similar results for the femur segmentation in MRI. But (1) IMCP-GMM can be used for all types of shape, this is not the case for the MDL method which only works for closed shape, and (2) IMCP-GMM is much faster than MDL.
The anterior cruciate ligament (ACL) is one of the most common ligament injuries. Several ACL reconstructions exist and are consequently performed. An accurate and comprehensive description of knee motion is essential for an adequate assessment of these surgeries, in terms of restoring knee motion. We propose to compare these reconstructions thanks to an index of articular coherence. This index measures the instantaneous state surface configurations during a motion. More specifically, this refers to the position between two articular surfaces facing each other. First of all, the index has to refer to a position known to be physiological. This initial position of the bones, named reference, directly results from the segmentation of CT scans. First we compute all distances between the two surfaces and then we compute the Cumulative Distribution Function (CDF). We process this way for each iteration of the motion. Then we obtain a batch of CDF curves which provide us qualitative information relative to the motion such as potential collisions or dislocations. The graph of all CDF curves is called Figure of Articular Coherence (FoAC). A good articular coherence is characterised by CDF which are close to the reference. This qualitative method is coupled to a quantitative one named Index of Articular Coherence (IoAC) which computes the Haussdorff distance between the temporal distributions and the reference. This distance has to be as low as possible. The tools were tested on cadaveric experiments of ACL reconstruction provided by Hagemeister et al, (1999). They recorded the knee flexion/extension motion in following situations: the intact knee, after ACL resection, after three methods of ACL reconstruction on the same knee (‘over-the-top’ method (OTT), two different two tunnel reconstructions (2 tunnel). Our method was used, for the time being, for one specimen. We compare different post-surgery kinematics thanks to the FoAC and IoAC.Introduction
Methods
