K N Bhanu Prakash1, Hussein Srour2, Sendhil S Velan1, Kai-Hsiang Chuang1. 1. Laboratory of Molecular Imaging, Singapore Bioimaging Consortium, Agency for Science, Technology and Research, #02-02 Helios, 11 Biopolis Way, Singapore, 138667, Singapore. 2. Laboratory of Molecular Imaging, Singapore Bioimaging Consortium, Agency for Science, Technology and Research, #02-02 Helios, 11 Biopolis Way, Singapore, 138667, Singapore. h.srour@uq.edu.au.
Abstract
OBJECTIVE: Brown adipose tissue (BAT) plays a key role for thermogenesis in mammals and infants. Recent confirmation of BAT presence in adult humans has aroused great interest for its potential to initiate weight-loss and normalize metabolic disorders in diabetes and obesity. Reliable detection and differentiation of BAT from the surrounding white adipose tissue (WAT) and muscle is critical for assessment/quantification of BAT volume. This study evaluates magnetic resonance (MR) acquisition for BAT and the efficacy of different automated methods for MR features-based BAT segmentation to identify the best suitable method. MATERIALS AND METHODS: Multi-point Dixon and multi-echo T2 spin-echo images were acquired from 12 mice using an Agilent 9.4T scanner. Four segmentation methods: multidimensional thresholding (MTh); region-growing (RG); fuzzy c-means (FCM) and neural-network (NNet) were evaluated for the interscapular region and validated against manually defined BAT, WAT and muscle. RESULTS: Statistical analysis of BAT segmentation yielded a median Dice-Statistical-Index, and sensitivity of 89.92% for NNet, 82.86% for FCM, 72.74% for RG, and 72.70%, for MTh, respectively. CONCLUSION: This study demonstrates that NNet improves the specificity to BAT from surrounding tissue based on 3-point Dixon and T2 MRI. This method facilitates quantification and longitudinal measurement of BAT in preclinical-models and human subjects.
OBJECTIVE: Brown adipose tissue (BAT) plays a key role for thermogenesis in mammals and infants. Recent confirmation of BAT presence in adult humans has aroused great interest for its potential to initiate weight-loss and normalize metabolic disorders in diabetes and obesity. Reliable detection and differentiation of BAT from the surrounding white adipose tissue (WAT) and muscle is critical for assessment/quantification of BAT volume. This study evaluates magnetic resonance (MR) acquisition for BAT and the efficacy of different automated methods for MR features-based BAT segmentation to identify the best suitable method. MATERIALS AND METHODS: Multi-point Dixon and multi-echo T2 spin-echo images were acquired from 12 mice using an Agilent 9.4T scanner. Four segmentation methods: multidimensional thresholding (MTh); region-growing (RG); fuzzy c-means (FCM) and neural-network (NNet) were evaluated for the interscapular region and validated against manually defined BAT, WAT and muscle. RESULTS: Statistical analysis of BAT segmentation yielded a median Dice-Statistical-Index, and sensitivity of 89.92% for NNet, 82.86% for FCM, 72.74% for RG, and 72.70%, for MTh, respectively. CONCLUSION: This study demonstrates that NNet improves the specificity to BAT from surrounding tissue based on 3-point Dixon and T2 MRI. This method facilitates quantification and longitudinal measurement of BAT in preclinical-models and human subjects.
Entities:
Keywords:
Automated segmentation; Brown adipose tissue; Fat–water imaging; Magnetic resonance imaging; Mouse; White adipose tissue
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