Bhanu Prakash K N, Verma Sanjay K, Yaligar Jadegoud, Goggi Julian, Gopalan Venkatesh, Lee Swee Shean, Tian Xianfeng, Sugii Shigeki, Leow Melvin Khee Shing, Bhakoo Kishore, Velan Sendhil S
Laboratory of Molecular Imaging, Singapore Bioimaging Consortium, A*STAR, 11 Biopolis Way, #02-02 Helios, Singapore, 138667, Singapore.
Laboratory of Metabolic Medicine, Singapore Bioimaging Consortium, A*STAR, Singapore, Singapore.
MAGMA. 2016 Apr;29(2):277-86. doi: 10.1007/s10334-015-0514-3. Epub 2016 Jan 8.
The aim was to auto-segment and characterize brown adipose, white adipose and muscle tissues in rats by multi-parametric magnetic resonance imaging with validation by histology and UCP1.
Male Wistar rats were randomized into two groups for thermoneutral (n = 8) and cold exposure (n = 8) interventions, and quantitative MRI was performed longitudinally at 7 and 11 weeks. Prior to imaging, rats were maintained at either thermoneutral body temperature (36 ± 0.5 °C), or short term cold exposure (26 ± 0.5 °C). Neural network based automatic segmentation was performed on multi-parametric images including fat fraction, T2 and T2* maps. Isolated tissues were subjected to histology and UCP1 analysis.
Multi-parametric approach showed precise delineation of the interscapular brown adipose tissue (iBAT), white adipose tissue (WAT) and muscle regions. Neural network based segmentation results were compared with manually drawn regions of interest, and showed 96.6 and 97.1% accuracy for WAT and BAT respectively. Longitudinal assessment of the iBAT volumes showed a reduction at 11 weeks of age compared to 7 weeks. The cold exposed group showed increased iBAT volume compared to thermoneutral group at both 7 and 11 weeks. Histology and UCP1 expression analysis supported our imaging results.
Multi-parametric MR based neural network auto-segmentation provides accurate separation of BAT, WAT and muscle tissues in the interscapular region. The cold exposure improves the classification and quantification of heterogeneous BAT.
旨在通过多参数磁共振成像对大鼠的棕色脂肪、白色脂肪和肌肉组织进行自动分割和特征描述，并通过组织学和解偶联蛋白1（UCP1）进行验证。
将雄性Wistar大鼠随机分为两组，分别进行热中性（n = 8）和冷暴露（n = 8）干预，并在第7周和第11周纵向进行定量磁共振成像。在成像前，将大鼠维持在热中性体温（36 ± 0.5°C）或短期冷暴露（26 ± 0.5°C）状态。对包括脂肪分数、T2和T2*图在内的多参数图像进行基于神经网络的自动分割。对分离出的组织进行组织学和UCP1分析。
多参数方法显示出对肩胛间棕色脂肪组织（iBAT）、白色脂肪组织（WAT）和肌肉区域的精确描绘。将基于神经网络的分割结果与手动绘制的感兴趣区域进行比较，WAT和BAT的准确率分别为96.6%和97.1%。iBAT体积的纵向评估显示，与7周龄相比，11周龄时体积减小。在第7周和第11周，冷暴露组的iBAT体积均高于热中性组。组织学和UCP1表达分析支持了我们的成像结果。
基于多参数磁共振的神经网络自动分割能够准确分离肩胛间区域的BAT、WAT和肌肉组织。冷暴露改善了异质性BAT的分类和定量。
