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08.01.2016 | Research Article | Ausgabe 2/2016

Magnetic Resonance Materials in Physics, Biology and Medicine 2/2016

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

Zeitschrift:
Magnetic Resonance Materials in Physics, Biology and Medicine > Ausgabe 2/2016
Autoren:
K. N. Bhanu Prakash, Sanjay K. Verma, Jadegoud Yaligar, Julian Goggi, Venkatesh Gopalan, Swee Shean Lee, Xianfeng Tian, Shigeki Sugii, Melvin Khee Shing Leow, Kishore Bhakoo, Sendhil S. Velan
Wichtige Hinweise
K. N. Bhanu Prakash, Sanjay K. Verma and Jadegoud Yaligar have contributed equally to this work.

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, T 2 and T 2* 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.

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