Assessment centre ⏵ Imaging ⏵ Abdominal MRI ⏵ Abdominal composition
DescriptionThis category contains derived data from the abdominal MRI, which were supplied by AMRA (Advanced MR Analytics AB, AMRA, Sweden).
Image-derived phenotypes relating to liver iron and fat, and abdominal composition have been generated by multiple institutes. The data from these other institutes are held in Category 126 and Category 158. Meaningful differences may exist between data across these categories and therefore, data should not be merged without very careful consideration.
Two highly comparable, but distinct pipelines have been used to generate the derived data in this category. Data for participants with only instance 2 have been generated using one pipeline (pipeline version 1), and those with data at both instance 2 and 3 have been processed using the other pipeline (pipeline version 2). A list of the participants with data at both instance 2 and 3, analysed with pipeline version 2, can currently be found in Field 41000.
The following OpenAccess publications provide details of the methods used to derive data in this category under pipeline version 1:
West J, Dahlqvist Leinhard O, Romu T, Collins R, Garratt S, Bell JD, et al. (2016) Feasibility of MR-Based Body Composition Analysis in Large Scale Population Studies. PLoS ONE 11(9): e0163332. doi:10.1371/journal.pone.0163332
Borga M, West J, Bell JD, et al. (2018) Advanced body composition assessment: from body mass index to body composition profiling. Journal of Investigative Medicine 66 (5). doi: 10.1136/jim-2018-000722
Linge J, Borga M, West J et al. (2018) Body Composition Profiling in the UK Biobank Imaging Study. Obesity 26(11). doi: 10.1002/oby.22210
Linge J, Whitcher B, Borga M et al. (2019) Sub-phenotyping Metabolic Disorders Using Body Composition: An Individualized, Nonparametric Approach Utilizing Large Data Sets. Obesity 27(7). doi: 10.1002/oby.22510
which are available as Resource 163332, Resource 2166, Resource 1005 and Resource 1006 in the Resources tab.
Documentation on Pipeline Version 2 is currently being developed.
When using the data for research purposes, the following publications may also be of interest:
- Dahlqvist Leinhard O, Johansson A, Rydell J, et al., editors. Quantitative abdominal fat estimation using MRI. Proceedings of the 19th International Conference on Pattern Recognition (ICPR); 2008.
- Karlsson A, Rosander J, Romu T, et al. Automatic and quantitative assessment of regional muscle volume by multi-atlas segmentation using whole-body water-fat MRI. Journal of magnetic resonance imaging: JMRI. 2014. doi: 10.1002/jmri.24726 PMID: 25111561.
- Borga M, Thomas LE, Romu T, Rosander J, et al. Validation of a fast method for quantification of intra-abdominal and subcutaneous adipose tissue for large scale human studies. NMR in biomedicine. 2015; 28(12):1747-53. doi: 10.1002/nbm.3432 PMID: 26768490
- West J, Dahlqvist Leinhard O, Romu T, et al. (2016) Feasibility of MR-Based Body Composition Analysis in Large Scale Population Studies. PLoS ONE 11(9): e0163332. doi:10.1371/journal.pone.0163332