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Body Composition Analysis In Large Scale Population Studies using Dixon Water-Fat Separated Imaging
Linköpings universitet, Institutionen för medicin och hälsa. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Region Östergötland, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US.
Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
Department of Life Sciences Faculty of Science and Technology, University of Westminster, London, UK.
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2016 (engelsk)Konferansepaper, Poster (with or without abstract) (Annet vitenskapelig)
Abstract [en]

Water-fat separated MRI, based on Dixon imaging techniques enables high soft-tissue contrast and the separation of fat and muscle compartments. This study investigate the feasibility and success-rate of one recently described method for MR data-acquisition and body composition analysis, in a large-scale population study. The first 1,000 subjects in the UK Biobank imaging cohort were scanned, quality assured and included for body composition analysis. Volumes of visceral adipose tissue, abdominal subcutaneous tissue, and thigh muscles were calculated. This study showed that the rapid MR-examination was sufficiently robust to achieve very high success-rate for body composition analysis. 

sted, utgiver, år, opplag, sider
2016.
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-128987OAI: oai:DiVA.org:liu-128987DiVA: diva2:933993
Konferanse
International Society for Magnetic Resonance in Medicine Annual Meeting & Exhibition, Singapore, May 7-13, 2016
Tilgjengelig fra: 2016-06-07 Laget: 2016-06-07 Sist oppdatert: 2016-06-22bibliografisk kontrollert

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