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3D-patient-specific geometry of the muscles involved in knee motion from selected MRI images

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Abstract

Patient-specific muscle geometry is not only an interesting clinical tool to evaluate different pathologies and treatments, but also provides an essential input data to more realistic musculoskeletal models. The protocol set up in our study provided the 3D-patient-specific geometry of the 13 main muscles involved in the knee joint motion from a few selected magnetic resonance images (MRIs). The contours of the muscles were identified on five to seven MRI axial slices. A parametric-specific object was then constructed for each muscle and deformed to fit those contours. The 13 muscles were obtained within 1 h, with less than 5% volume error and 5 mm point-surface error (2RMS). From this geometry, muscle volumes and volumic fractions of asymptomatic and anterior cruciate ligament deficient subjects could easily be computed and compared to previous studies. This protocol provides an interesting precision/time trade-off to obtain patient-specific muscular geometry.

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Notes

  1. Ecole de Technologie Supérieure de Montréal, Centre Hospitalier de l’Université de Montréal, Hôpital du Sacré Cœur de Montréal, Centre de Recherche interdisciplinaire en réadaptation du Montréal métropolitain, Hôpital Maisonneuve-Rosemont de Montréal.

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Acknowledgments

We thank D. Blain, N. Langlois (Radiological Department, University of Montreal Hospital Centre), and M. Charbonneau (LIO, Montreal) for their help in MRI acquisitions. We also thank Pr J.D. Laredo for helpful comments. This work was funded by the Canada Research Chair in 3D Imaging and Biomedical Engineering, Chaire de recherche en orthopédie Marie-Lou et Yves Cotrel du CHUM, the MENTOR program (ETS-IRSC), and the EGIDE program (Ministère des affaires étrangères français, Ministère des relations internationales québécois).

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Correspondence to I. Südhoff or W. Skalli.

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Südhoff, I., de Guise, J.A., Nordez, A. et al. 3D-patient-specific geometry of the muscles involved in knee motion from selected MRI images. Med Biol Eng Comput 47, 579–587 (2009). https://doi.org/10.1007/s11517-009-0466-8

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  • DOI: https://doi.org/10.1007/s11517-009-0466-8

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