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Anatomically- and computationally-informed hepatic contrast perfusion simulations for use in virtual clinical trials
Author(s): Thomas J. Sauer; Ehsan Abadi; Paul Segars; Ehsan Samei
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Paper Abstract

This study modeled a framework for virtual human liver phantoms, focusing primarily on the intricate vascular networks that comprise the liver. Large vasculature was segmented from clinical liver perfusion images to ascertain a general starting point for the vascular networks of the liver that would be common among a healthy population. Clinical imaging methods cannot currently resolve the vast majority of the vasculature of the liver, and at the limiting resolution, modeling techniques continued the structure of the existing vasculature according to empirically known properties of blood vessel formation. Such advances in virtual phantom modeling enable simulation work in CT liver imaging, as clinical CT liver imaging is not ideally performed without contrast and multi-phasic acquisitions taking place over the course of the contrast's perfusion. The total amount of contrast in each organ in the body as a function of time is known from prior work, and the complete vascular network of the liver allows this information to be translated into an organ-specific contrast-concentration as a function of time. The ability to simulate this physiology is necessary for liver perfusion imaging, as pathologies typically impede or otherwise alter healthy perfusion patterns. The perfusion simulated here was in good agreement with known patterns of perfusion. Thus, virtual clinical trials can be performed with a dynamic model of the liver containing a fully integrated and realistic vascular network.

Paper Details

Date Published: 14 March 2019
PDF: 9 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094806 (14 March 2019); doi: 10.1117/12.2513465
Show Author Affiliations
Thomas J. Sauer, Carl E. Ravin Advanced Imaging Labs. (United States)
Duke Univ. Medical Physics Graduate Program (United States)
Ehsan Abadi, Carl E. Ravin Advanced Imaging Labs. (United States)
Paul Segars, Carl E. Ravin Advanced Imaging Labs. (United States)
Duke Univ. Medical Physics Graduate Program (United States)
Duke Univ. Medical Ctr. (United States)
Ehsan Samei, Carl E. Ravin Advanced Imaging Labs. (United States)
Duke Univ. Medical Physics Graduate Program (United States)
Duke Univ. Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 10948:
Medical Imaging 2019: Physics of Medical Imaging
Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans, Editor(s)

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