Segmentation and characterization of interscapular brown adipose tissue in rats by multi-parametric magnetic resonance imaging.
MAGMA
; 29(2): 277-86, 2016 Apr.
Article
em En
| MEDLINE
| ID: mdl-26747282
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 ANDMETHODS:
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.Palavras-chave
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Base de dados:
MEDLINE
Assunto principal:
Escápula
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Articulação do Ombro
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Tecido Adiposo Marrom
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Interpretação de Imagem Assistida por Computador
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Temperatura Baixa
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Imagem Multimodal
Tipo de estudo:
Diagnostic_studies
Limite:
Animals
Idioma:
En
Ano de publicação:
2016
Tipo de documento:
Article