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Proceedings Paper

Quantitative CT imaging for adipose tissue analysis in mouse model of obesity
Author(s): A. Marchadier; C. Vidal; J.-P. Tafani; S. Ordureau; R. Lédée; C. Léger
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Paper Abstract

In obese humans CT imaging is a validated method for follow up studies of adipose tissue distribution and quantification of visceral and subcutaneous fat. Equivalent methods in murine models of obesity are still lacking. Current small animal micro-CT involves long-term X-ray exposure precluding longitudinal studies. We have overcome this limitation by using a human medical CT which allows very fast 3D imaging (2 sec) and minimal radiation exposure. This work presents novel methods fitted to in vivo investigations of mice model of obesity, allowing (i) automated detection of adipose tissue in abdominal regions of interest, (ii) quantification of visceral and subcutaneous fat. For each mouse, 1000 slices (100μm thickness, 160 μm resolution) were acquired in 2 sec using a Toshiba medical CT (135 kV, 400mAs). A Gaussian mixture model of the Hounsfield curve of 2D slices was computed with the Expectation Maximization algorithm. Identification of each Gaussian part allowed the automatic classification of adipose tissue voxels. The abdominal region of interest (umbilical) was automatically detected as the slice showing the highest ratio of the Gaussian proportion between adipose and lean tissues. Segmentation of visceral and subcutaneous fat compartments was achieved with 2D 1/2 level set methods. Our results show that the application of human clinical CT to mice is a promising approach for the study of obesity, allowing valuable comparison between species using the same imaging materials and software analysis.

Paper Details

Date Published: 9 March 2011
PDF: 8 pages
Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79632O (9 March 2011); doi: 10.1117/12.878144
Show Author Affiliations
A. Marchadier, Institut PRISME, Univ. d'Orléans (France)
UsefulProgress (France)
C. Vidal, Institut Pasteur (France)
INSERM (France)
J.-P. Tafani, APCIS S.A. (France)
S. Ordureau, UsefulProgress (France)
R. Lédée, Institut PRISME, Univ. d'Orléans (France)
C. Léger, Institut PRISME, Univ. d'Orléans (France)


Published in SPIE Proceedings Vol. 7963:
Medical Imaging 2011: Computer-Aided Diagnosis
Ronald M. Summers M.D.; Bram van Ginneken, Editor(s)

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