Keywords: In Silico, Simulations
Motivation: Signal dynamics in MRI are influenced by multiple factors at different scales, complicating analyses. Existing analytical solutions and simulation methods are inadequate for complex geometries or susceptibility distributions.
Goal(s): We aim to demonstrate and validate a physics-informed neural network (PINN) capable of handling arbitrary susceptibility-induced Larmor fields and diffusion terms.
Approach: We implemented a flexible PINN-based simulation framework for solving the underlying the Bloch-Torrey equation which is able to predict the signal dynamics over time.
Results: The framework produced accurate results for local and total magnetization across various Larmor field configurations at low to moderate oscillation frequencies, closely matching finite difference method results.
Impact: By accommodating complex susceptibility distributions and geometries, our PINN-based framework enhances the simulation of MRI signal dynamics in tissues with varying properties—such as hemorrhages, amyloid deposits, iron accumulation, and vascular malformations—offering significant clinical relevance.
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