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Proceedings Paper

Liver fat quantification using fast kVp-switching dual energy CT
Author(s): Andras Kriston; Paulo Mendonça; Alvin Silva; Robert G. Paden; William Pavlicek; Dushyant Sahani; Benedek Janos Kis; Laszlo Rusko; Darin Okerlund; Rahul Bhotika
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Paper Abstract

Nonalcoholic steatohepatitis (NASH) is a liver disease that occurs in patients that lack a history of the well-proven association of alcohol use. A major symptom of NASH is increased fat deposition in the liver. Gemstone Spectral Imaging (GSI) with fast kVp-switching enables projection-based material decomposition, offering the opportunity to accurately characterize tissue types, e.g., fat and healthy liver tissue, based on their energy-sensitive material attenuation and density. We describe our pilot efforts to apply GSI to locate and quantify the amount of fat deposition in the liver. Two approaches are presented, one that computes percentage fat from the difference in HU values at high and low energies and the second based on directly computing fat volume fraction at each voxel using multi-material decomposition. Simulation software was used to create a phantom with a set of concentric rings, each composed of fat and soft tissue in different relative amounts with attenuation values obtained from the National Institute of Standards and Technology. Monte Carlo 80 and 140 kVp X-ray projections were acquired and CT images of the phantom were reconstructed. Results demonstrated the sensitivity of dual energy CT to the presence of fat and its ability to distinguish fat from soft tissue. Additionally, actual patient (liver) datasets were acquired using GSI and monochromatic images at 70 and 140 keV were reconstructed. Preliminary results demonstrate a tissue sensitivity that appears sufficient to quantify fat content with a degree of accuracy as may be needed for non-invasive clinical assessment of NASH.

Paper Details

Date Published: 31 March 2011
PDF: 8 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79623W (31 March 2011); doi: 10.1117/12.878206
Show Author Affiliations
Andras Kriston, GE Healthcare (Hungary)
Paulo Mendonça, GE Global Research (United States)
Alvin Silva, Mayo Clinic Scottsdale (United States)
Robert G. Paden, Mayo Clinic Scottsdale (United States)
William Pavlicek, Mayo Clinic Scottsdale (United States)
Dushyant Sahani, Massachusetts General Hospital (United States)
Benedek Janos Kis, Univ. of Szeged (Hungary)
Laszlo Rusko, GE Healthcare (Hungary)
Darin Okerlund, GE Healthcare (United States)
Rahul Bhotika, GE Global Research (United States)

Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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