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Book Sections Year : 2023

Microvascular modeling for medical imaging and toxicity assessment

Abstract

Dynamic-contrast-enhanced medical imaging is a non-invasive method for detecting tissue abnormalities like tumors, evaluating organ function, or assessing drug efficacy. Tracers injected into the body reflect tissue perfusion and function. Characterizing this 'reflection' in-vivo is challenging: in-silico methods are being developed for better image interpretation. Moreover in organs such as the liver, cells are nourished by networks of small vessels. This vasculature is prone to developing diseases due to the liver detoxification function. Understanding drug effects on the liver involves invasive in-vivo toxicity experiments, reducing subject lifespan. In-vitro experiments are simpler; yet their relevance needs to be assessed. In-silico models are increasingly invoked to link the two experiment types, reducing the need for in-vivo toxicity tests. This chapter contains three in-silico models that, based on a common mathematical framework, simulate blood flow and drug transport in and out of a diseased micro-vascular network, and their effect on cells. To that end, the main pillars of multi-phase multi-species flow and transport models are first derived from primary principles and then transcribed for a vascularized tissue. The chapter provides overall a methodology to link changes in vascular network architectures, microcirculation consequences and some macroscale biomarkers relevant for dynamic medical imaging or toxicity experiments.
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Dates and versions

hal-04465901 , version 1 (19-02-2024)

Identifiers

  • HAL Id : hal-04465901 , version 1

Cite

Jérôme Kowalski, Dirk Drasdo, Peter Kottman, Jules Dichamp, Irene Vignon-Clementel. Microvascular modeling for medical imaging and toxicity assessment. Quantitative approaches to microcirculation: mathematical models, computational methods, measurements and data analysis, Springer, 2023, SEMA SIMAI Springer Series. ⟨hal-04465901⟩
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