Literature DB >> 18719667

Influence of age and position on the CT number of adipose tissues in pigs.

Fintan J McEvoy1, Mads T Madsen, Eiliv L Svalastoga.   

Abstract

The location of adipose tissue depots is important in determining their significance. Research into the physical and chemical differences between these depots is therefore of interest. Using image analysis, this paper examines the influence of location on the linear attenuation coefficient of adipose tissue for X-rays, in computed tomography (as indicated by CT number) at three time points. Nine pigs were CT scanned on three separate occasions approximately 1 month apart. The mean CT number was -78, -100, and -104 for visceral adipose tissue (VAT) from the first to the final scan, respectively. The corresponding CT numbers for subcutaneous adipose tissue (SAT) were -80, -101, and -106. There was a significant difference between the CT numbers at each location at each scan (P values from 0.025 to <0.001) and between the CT numbers for each location at different times (P < 0.05). In a separate analysis of the final scan session, the mean CT number of adipose tissue at increasing distances from a mathematically defined center of the animal was determined. Regression analysis showed that the CT number of adipose tissue decreases with increasing distance from the animal's center (y = -102.7 - 0.04 x, P < 0.001, where y is the predicted CT number for adipose tissue, from the animal center (x = 0) to the skin (x = 100)). It can thus be expected that the overall mean CT number for adipose tissue can be used as an indicator of the relative quantities of adipose tissue at each location if the mean for each is known.

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Year:  2008        PMID: 18719667     DOI: 10.1038/oby.2008.360

Source DB:  PubMed          Journal:  Obesity (Silver Spring)        ISSN: 1930-7381            Impact factor:   5.002


  2 in total

1.  Calibration and validation of EchoMRI™ whole body composition analysis based on chemical analysis of piglets, in comparison with the same for DXA.

Authors:  Israel Kovner; Gersh Z Taicher; Alva D Mitchell
Journal:  Int J Body Compos Res       Date:  2010-03-01

2.  Shading artifact correction in breast CT using an interleaved deep learning segmentation and maximum-likelihood polynomial fitting approach.

Authors:  Peymon Ghazi; Andrew M Hernandez; Craig Abbey; Kai Yang; John M Boone
Journal:  Med Phys       Date:  2019-06-23       Impact factor: 4.071

  2 in total

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