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Proceedings Paper

Fat quantification and analysis of lung transplant patients on unenhanced chest CT images based on standardized anatomic space
Author(s): Yubing Tong; Jayaram K. Udupa; Drew A. Torigian; Caiyun Wu; Jason Christie; David J. Lederer
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Paper Abstract

Chest fat estimation is important for identifying high-risk lung transplant candidates. In this paper, an approach to chest fat quantification based on a recently formulated concept of standardized anatomic space (SAS) is presented. The goal of this paper is to seek answers to the following questions related to chest fat quantification on single slice versus whole volume CT, which have not been addressed in the literature. What level of correlation exists between total chest fat volume and fat areas measured on single abdominal and thigh slices? What is the anatomic location in the chest where maximal correlation of fat area with fat volume can be expected? Do the components of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) have the same area-to-volume correlative behavior or do they differ? The SAS approach includes two steps: calibration followed by transformation which will map the patient slice locations non-linearly to SAS. The optimal slice locations found for SAT and VAT based on SAS are different and at the mid-level of the T8 vertebral body for SAT and mid-level of the T7 vertebral body for VAT. Fat volume and area on optimal slices for SAT and VAT are correlated with Pearson correlation coefficients of 0.97 and 0.86, respectively. The correlation of chest fat volume with abdominal and thigh fat areas is weak to modest.

Paper Details

Date Published: 29 March 2016
PDF: 7 pages
Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 978817 (29 March 2016); doi: 10.1117/12.2217866
Show Author Affiliations
Yubing Tong, Medical Imaging Processing Group, Univ. of Pennsylvania (United States)
Jayaram K. Udupa, Medical Imaging Processing Group, Univ. of Pennsylvania (United States)
Drew A. Torigian, Medical Imaging Processing Group, Univ. of Pennsylvania (United States)
Caiyun Wu, Medical Imaging Processing Group, Univ. of Pennsylvania (United States)
Jason Christie, Hospital of the Univ. of Pennsylvania School of Medicine (United States)
David J. Lederer, Columbia Univ. Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 9788:
Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

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