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Validation of a Fast Method for Quantification of Intra-abdominal and Subcutaneous Adipose Tissue for Large Scale Human Studies
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9267-2191
Department of Life Sciences Faculty of Science and Technology University of Westminster, London, United Kingdom.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Advanced MR Analytics AB, Linköping, Sweden.
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2015 (English)In: NMR in Biomedicine, ISSN 1099-1492, Vol. 28, no 12, 1747-1753 p.Article in journal (Refereed) Published
Abstract [en]

Central obesity is the hallmark of a number of non-inheritable disorders. The advent of imaging techniques such as magnetic resonance imaging (MRI) has allowed for a fast and accurate assessment of body fat content and distribution. However, image analysis continues to be one of the major obstacles for the use of MRI in large scale studies. In this study we assess the validity of the recently proposed fat-muscle-quantitation-system (AMRATM Profiler) for the quantification of intra-abdominal adipose tissue (IAAT) and abdominal subcutaneous adipose tissue (ASAT) from abdominal MR images.  Abdominal MR images were acquired from 23 volunteers with a broad range of BMIs and analysed using SliceOmatic, the current gold-standard, and the AMRATM Profiler based on a non-rigid image registration of a library of segmented atlases. The results show that there was a highly significant correlation between the fat volumes generated by both analysis methods, (Pearson correlation r = 0.97 p<0.001), with the AMRATM Profiler analysis being significantly faster (~3 mins) than the conventional SliceOmatic approach (~40 mins). There was also excellent agreement between the methods for the quantification of IAAT (AMRA 4.73 ± 1.99 vs SliceOmatic 4.73 ± 1.75 litres, p=0.97). For the AMRATM Profiler analysis, the intra-observer coefficient of variation was 1.6 % for IAAT and 1.1 % for ASAT, the inter-observer coefficient of variation was 1.4 % for IAAT and 1.2 % for ASAT, the intra-observer correlation was 0.998 for IAAT and 0.999 for ASAT, and the inter-observer correlation was 0.999 for both IAAT and ASAT. These results indicate that precise and accurate measures of body fat content and distribution can be obtained in a fast and reliable form by the AMRATM Profiler, opening up the possibility of large-scale human phenotypic studies.

Place, publisher, year, edition, pages
John Wiley & Sons, 2015. Vol. 28, no 12, 1747-1753 p.
Keyword [en]
adipose tissue, fat quantitation, obesity, MRI, Dixon, abdominal fat
National Category
Radiology, Nuclear Medicine and Medical Imaging Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-121393DOI: 10.1002/nbm.3432ISI: 000367315100015OAI: oai:DiVA.org:liu-121393DiVA: diva2:854389
Available from: 2015-09-16 Created: 2015-09-16 Last updated: 2016-05-04

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Borga, MagnusRomu, ThobiasDahlqvist Leinhard, Olof
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Center for Medical Image Science and Visualization (CMIV)Medical InformaticsThe Institute of TechnologyFaculty of Science & EngineeringDivision of Radiological SciencesFaculty of Medicine and Health SciencesDepartment of Radiation Physics
Radiology, Nuclear Medicine and Medical ImagingMedical Image Processing

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