Quantitative CT imaging for adipose tissue analysis in mouse model of obesity

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 ½ 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

A Correlative Method for Imaging Identical Regions of Samples by Micro-CT, Light Microscopy, and Electron Microscopy

“…These algorithms are based on edge detection and mathematical operations that identify the body surface, perform a preliminary separation between visceral adipose tissue and subcutaneous adipose tissue, and then, after automatic correction, achieve an accurate separation between adipose deposits, eliminating the need for manually drawing contour lines (Hildebrandt et al 2002;Judex et al 2010;Marchadiera et al 2011). These methods are reliable in the determination of whole body fat content and distribution (Kvist et al 1988;Seidell et al 1990); however, adipose tissue is recognized only as the darkest region among brighter areas of muscle and bone, which affords little structural detail (Judex et al 2010).…”

In vivo X-Ray Computed Tomographic Imaging of Soft Tissue with Native, Intravenous, or Oral Contrast

“…Mouse models of obesity are highly prevalent and provide a convenient experimental system with which to study progression of the disease [29]. Fortunately, adipose is one of the few soft tissues that is readily distinguishable in non-contrast enhanced CT images due to the significantly lower material density of fat in relation to other soft tissues [30]. Thus, CT is an excellent modality to conduct longitudinal studies examining the genetic and environmental factors contributing to fat gain or loss using mouse models.…”

Robust separation of visceral and subcutaneous adipose tissues in micro-CT of mice

“…The wall is beneath the subcutaneous fat, which is visible in any given trans-axial slice; thus, evolving the wall along the slice stack will result in enclosed VAT area. Edge-based [5], [12], [13] and region-based [6] active contour models fit in well to achieve this goal. However, the contour-based walltracking approaches for VAT/SAT separation tend to suffer from weak boundary as well as edge leakage problem.…”

“…The past ten years have seen the development of a number of automatic VAT/SAT separation algorithms [1], [2], [3], [4], [5], [6], which usually involve two major analysis tasks. The first step is adipose extraction, i.e.…”

“…In order to keep the contour faithfully aligned with the actual muscle wall, extra care needs to be taken to avoid the curve bending into VAT areas. In addition, with the exception of [6], most published works in the literature rely on user intervention to manually choose the starting and ending slices that define the VAT region of interest (ROI).…”