Segmentation and characterization of interscapular brown adipose tissue in rats by multi-parametric magnetic resonance imaging.

ABSTRACT

OBJECTIVE: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: 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.