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MRI-Based Body Composition Analysis
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Medical AB, Linköping, Sweden.ORCID iD: 0000-0002-9267-2191
AMRA Medical AB, Linköping, Sweden.
AMRA Medical AB, Linköping, Sweden; Edinburgh Imaging Academy, University of Edinburgh, Edinburgh, UK.
2022 (English)In: Basic Protocols in Foods and Nutrition / [ed] Cinthia Bau Betim Cazarin, New York, NY, United States: Springer Nature, 2022, 1, p. 307-334Chapter in book (Refereed)
Abstract [en]

Magnetic resonance imaging (MRI) is considered being state-of-the-art technology for body composition analysis. Compared to other indirect techniques such as scales, calipers, bioimpedance, and dual-energy X-ray absorptiometry (DXA), MRI offers direct and precise measurements of the volumes of different tissue compartments and also enables quantification of diffuse fat infiltration in organs. Here, we describe a protocol for acquiring of fat–water-separated MRI data and the image postprocessing required for the quantification of several body composition biomarkers relevant for metabolic research. This protocol has successfully been used in several clinical studies and also in the large UK Biobank population study.

Place, publisher, year, edition, pages
New York, NY, United States: Springer Nature, 2022, 1. p. 307-334
Series
Methods and Protocols in Food Science, ISSN 2662-950X, E-ISSN 2662-9518
Keywords [en]
Body composition analysis, Magnetic resonance imaging, Metabolic imaging biomarkers, Visceral adipose tissue, Subcutaneous adipose tissue, Muscle fat infiltration, Liver fat
National Category
Radiology, Nuclear Medicine and Medical Imaging Medical Imaging Nutrition and Dietetics
Identifiers
URN: urn:nbn:se:liu:diva-186942DOI: 10.1007/978-1-0716-2345-9_19Libris ID: n4qf9n1vl2bn60hmISBN: 9781071623442 (print)OAI: oai:DiVA.org:liu-186942DiVA, id: diva2:1681721
Funder
Swedish Research Council, 2019-04751Available from: 2022-07-07 Created: 2022-07-07 Last updated: 2025-02-11Bibliographically approved

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Borga, Magnus

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