Madlaine Müller1, Maike F Dohrn1, Sandro Romanzetti1, Michael Gadermayr2, Kathrin Reetz1,3, Nils A Krämer4, Christiane Kuhl4, Jörg B Schulz1,3, Burkhard Gess1. 1. Department of Neurology, University Hospital Aachen, Aachen, Germany. 2. Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany. 3. JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, RWTH Aachen University, Aachen, Germany. 4. Clinic for Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany.
Abstract
BACKGROUND: Muscle MRI is of increasing importance for neuromuscular patients to detect changes in muscle volume, fat-infiltration, and edema. We developed a method for semi-automated segmentation of muscle MRI datasets. METHODS: An active contour-evolution algorithm implemented within the ITK-SNAP software was used to segment T1-weighted MRI, and to quantify muscle volumes of neuromuscular patients (n = 65). RESULTS: Semi-automated compared with manual segmentation was shown to be accurate and time-efficient. Muscle volumes and ratios of thigh/lower leg volume were lower in myopathy patients than in controls (P < .0001; P < .05). We found a decrease of lower leg muscle volume in neuropathy patients compared with controls (P < .01), which correlated with clinical parameters. In myopathy patients, muscle volume showed a positive correlation with muscle strength (rleft = 0.79, pleft < .0001). Muscle volumes were independent of body mass index and age. CONCLUSIONS: Our method allows for exact and time-efficient quantification of muscle volumes with possible use as a biomarker in neuromuscular patients.
BACKGROUND: Muscle MRI is of increasing importance for neuromuscular patients to detect changes in muscle volume, fat-infiltration, and edema. We developed a method for semi-automated segmentation of muscle MRI datasets. METHODS: An active contour-evolution algorithm implemented within the ITK-SNAP software was used to segment T1-weighted MRI, and to quantify muscle volumes of neuromuscular patients (n = 65). RESULTS: Semi-automated compared with manual segmentation was shown to be accurate and time-efficient. Muscle volumes and ratios of thigh/lower leg volume were lower in myopathy patients than in controls (P < .0001; P < .05). We found a decrease of lower leg muscle volume in neuropathy patients compared with controls (P < .01), which correlated with clinical parameters. In myopathy patients, muscle volume showed a positive correlation with muscle strength (rleft = 0.79, pleft < .0001). Muscle volumes were independent of body mass index and age. CONCLUSIONS: Our method allows for exact and time-efficient quantification of muscle volumes with possible use as a biomarker in neuromuscular patients.
Authors: Ellen van der Plas; Laurie Gutmann; Dan Thedens; Richard K Shields; Kathleen Langbehn; Zhihui Guo; Milan Sonka; Peggy Nopoulos Journal: Muscle Nerve Date: 2021-02-05 Impact factor: 3.217
Authors: Matthew Wilcox; Liane Dos Santos Canas; Rikin Hargunani; Tom Tidswell; Hazel Brown; Marc Modat; James B Phillips; Sebastien Ourselin; Tom Quick Journal: Sci Rep Date: 2021-11-17 Impact factor: 4.379