13 March 2014 Abdominal adipose tissue quantification on water-suppressed and non-water-suppressed MRI at 3T using semi-automated FCM clustering algorithm
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Abstract
Accurate measurements of human body fat distribution are desirable because excessive body fat is associated with impaired insulin sensitivity, type 2 diabetes mellitus (T2DM) and cardiovascular disease. In this study, we hypothesized that the performance of water suppressed (WS) MRI is superior to non-water suppressed (NWS) MRI for volumetric assessment of abdominal subcutaneous (SAT), intramuscular (IMAT), visceral (VAT), and total (TAT) adipose tissues. We acquired T1-weighted images on a 3T MRI system (TIM Trio, Siemens), which was analyzed using semi–automated segmentation software that employs a fuzzy c-means (FCM) clustering algorithm. Sixteen contiguous axial slices, centered at the L4–L5 level of the abdomen, were acquired in eight T2DM subjects with water suppression (WS) and without (NWS). Histograms from WS images show improved separation of non-fatty tissue pixels from fatty tissue pixels, compared to NWS images. Paired t-tests of WS versus NWS showed a statistically significant lower volume of lipid in the WS images for VAT (145.3 cc less, p=0.006) and IMAT (305 cc less, p<0.001), but not SAT (14.1 cc more, NS). WS measurements of TAT also resulted in lower fat volumes (436.1 cc less, p=0.002). There is strong correlation between WS and NWS quantification methods for SAT measurements (r=0.999), but poorer correlation for VAT studies (r=0.845). These results suggest that NWS pulse sequences may overestimate adipose tissue volumes and that WS pulse sequences are more desirable due to the higher contrast generated between fatty and non-fatty tissues.
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Sunil K. Valaparla, Sunil K. Valaparla, Qi Peng, Qi Peng, Feng Gao, Feng Gao, Geoffrey D. Clarke, Geoffrey D. Clarke, } "Abdominal adipose tissue quantification on water-suppressed and non-water-suppressed MRI at 3T using semi-automated FCM clustering algorithm", Proc. SPIE 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging, 903802 (13 March 2014); doi: 10.1117/12.2043172; https://doi.org/10.1117/12.2043172
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