Open framework for management and processing of multi-modality and multidimensional imaging data for analysis and modelling muscular function

David García Juan; Bénédicte M. A. Delattre; Sara Trombella; Sean Lynch; Matthias Becker; Hon Fai Choi; Osman Ratib

doi:10.1117/12.2042853

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12 March 2014 Open framework for management and processing of multi-modality and multidimensional imaging data for analysis and modelling muscular function

David García Juan, Bénédicte M. A. Delattre, Sara Trombella, Sean Lynch, Matthias Becker, Hon Fai Choi, Osman Ratib

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Proceedings Volume 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling; 90361W (2014) https://doi.org/10.1117/12.2042853
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Musculoskeletal disorders (MSD) are becoming a big healthcare economical burden in developed countries with aging population. Classical methods like biopsy or EMG used in clinical practice for muscle assessment are invasive and not accurately sufficient for measurement of impairments of muscular performance. Non-invasive imaging techniques can nowadays provide effective alternatives for static and dynamic assessment of muscle function. In this paper we present work aimed toward the development of a generic data structure for handling n-dimensional metabolic and anatomical data acquired from hybrid PET/MR scanners. Special static and dynamic protocols were developed for assessment of physical and functional images of individual muscles of the lower limb. In an initial stage of the project a manual segmentation of selected muscles was performed on high-resolution 3D static images and subsequently interpolated to full dynamic set of contours from selected 2D dynamic images across different levels of the leg. This results in a full set of 4D data of lower limb muscles at rest and during exercise. These data can further be extended to a 5D data by adding metabolic data obtained from PET images. Our data structure and corresponding image processing extension allows for better evaluation of large volumes of multidimensional imaging data that are acquired and processed to generate dynamic models of the moving lower limb and its muscular function.

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David García Juan, Bénédicte M. A. Delattre, Sara Trombella, Sean Lynch, Matthias Becker, Hon Fai Choi, and Osman Ratib "Open framework for management and processing of multi-modality and multidimensional imaging data for analysis and modelling muscular function", Proc. SPIE 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, 90361W (12 March 2014); https://doi.org/10.1117/12.2042853

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