Feasibility of MR-Based Body Composition Analysis in Large Scale Population Studies.
BACKGROUND
Quantitative and accurate measurements of fat and muscle in the body are important for prevention and diagnosis of diseases related to obesity and muscle degeneration. Manually segmenting muscle and fat compartments in MR body-images is laborious and time-consuming, hindering implementation in large cohorts. In the present study, the feasibility and success-rate of a Dixon-based MR scan followed by an intensity-normalised, non-rigid, multi-atlas based segmentation was investigated in a cohort of 3,000 subjects.


METHODS
3,000 participants in the in-depth phenotyping arm of the UK Biobank imaging study underwent a comprehensive MR examination. All subjects were scanned using a 1.5 T MR-scanner with the dual-echo Dixon Vibe protocol, covering neck to knees. Subjects were scanned with six slabs in supine position, without localizer. Automated body composition analysis was performed using the AMRA Profiler™ system, to segment and quantify visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT) and thigh muscles. Technical quality assurance was performed and a standard set of acceptance/rejection criteria was established. Descriptive statistics were calculated for all volume measurements and quality assurance metrics.


RESULTS
Of the 3,000 subjects, 2,995 (99.83%) were analysable for body fat, 2,828 (94.27%) were analysable when body fat and one thigh was included, and 2,775 (92.50%) were fully analysable for body fat and both thigh muscles. Reasons for not being able to analyse datasets were mainly due to missing slabs in the acquisition, or patient positioned so that large parts of the volume was outside of the field-of-view.


CONCLUSIONS
In conclusion, this study showed that the rapid UK Biobank MR-protocol was well tolerated by most subjects and sufficiently robust to achieve very high success-rate for body composition analysis. This research has been conducted using the UK Biobank Resource.
Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.|Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.|Advanced MR Analytics AB, Linköping, Sweden.
Janne
J
West
Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.|Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.|Advanced MR Analytics AB, Linköping, Sweden.
Olof
O
Dahlqvist Leinhard
Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.|Advanced MR Analytics AB, Linköping, Sweden.|Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
Thobias
T
Romu
Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
Rory
R
Collins
UK Biobank, Stockport, United Kingdom.
Steve
S
Garratt
Research Centre for Optimal Health, Department of Life Sciences, Faculty of Science and Technology, University of Westminster, London, United Kingdom.
Jimmy D
JD
Bell
Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.|Advanced MR Analytics AB, Linköping, Sweden.|Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
Magnus
M
Borga
Research Centre for Optimal Health, Department of Life Sciences, Faculty of Science and Technology, University of Westminster, London, United Kingdom.
Louise
L
Thomas
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