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14 March 2019 Anatomically- and computationally-informed hepatic contrast perfusion simulations for use in virtual clinical trials
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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.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Thomas J. Sauer, Ehsan Abadi, Paul Segars, and Ehsan Samei "Anatomically- and computationally-informed hepatic contrast perfusion simulations for use in virtual clinical trials", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094806 (14 March 2019); https://doi.org/10.1117/12.2513465
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