FEelMRI: a simple and extensible Python library for the generation of synthetic MR images from finite elements simulations

Motivation: Traditional Bloch equation methods for MR image simulation are computationally expensive, especially with large datasets to capture complex tissue behaviors like flow and motion. Using Finite Element (FE) models to evaluate the signal equation provides an efficient solution, advancing imaging research and accurately depicting tissue dynamics. Goal(s): To develop an efficient and versatile library for generating synthetic MR images directly from FE simulations. Approach: FEelMRI evaluates the MRI signal equation at specific k-space locations, without explicitly solving Bloch equations, supporting customizable magnetization models and k-space trajectories. Results: FEelMRI produced images efficiently on both CPU and GPU, reflecting realistic imaging artifacts and characteristics. Impact: FEelMRI enables rapid and flexible generation of synthetic MR images from FE models, significantly reducing computational costs and expanding possibilities in MRI research. This novel tool facilitates the study of MRI under complex physiological conditions, enhancing research capabilities and applications.