Multiscale model of liver DCE-MRI towards a better understanding of tumor complexity.
Abstract
The use of quantitative imaging for the characterization of hepatic tumors in magnetic resonance imaging (MRI) can improve the diagnosis and therefore the treatment of these life-threatening tumors. However, image parameters remain difficult to interpret because they result from a mixture of complex processes related to pathophysiology and to acquisition. These processes occur at variable spatial and temporal scales. We propose a multiscale model of liver dynamic contrast-enhanced (DCE) MRI in order to better understand the tumor complexity in images. Our design couples a model of the organ (tissue and vasculature) with a model of the image acquisition. At the macroscopic scale, vascular trees take a prominent place. Regarding the formation of MRI images, we propose a distributed model of parenchymal biodistribution of extracellular contrast agents. Model parameters can be adapted to simulate the tumor development. The sensitivity of the multiscale model of liver DCE-MRI was studied through observations of the influence of two physiological parameters involved in carcinogenesis (arterial flow and capillary permeability) on its outputs (MRI images at arterial and portal phases). Finally, images were simulated for a set of parameters corresponding to the five stages of hepatocarcinogenesis (from regenerative nodules to poorly differentiated HepatoCellular Carcinoma).
Domains
Life Sciences [q-bio] Bioengineering Engineering Sciences [physics] Signal and Image processing Physics [physics] Physics [physics] Biological Physics [physics.bio-ph] Physics [physics] Physics [physics] Medical Physics [physics.med-ph] Computer Science [cs] Modeling and Simulation Computer Science [cs] Medical Imaging Life Sciences [q-bio] Cancer Life Sciences [q-bio] Human health and pathology Hépatology and Gastroenterology Computer Science [cs] Signal and Image Processing
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Origin : Files produced by the author(s)
Origin : Files produced by the author(s)
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