Torch-EPG-X: a GPU-powered differentiable framework for the simulation of magnetization exchanging systems

Motivation: Most existing MR simulators either focus on the implementation of multiple physical phenomena or on massive parallelization, but these two aspects are usually not tackled simultaneously. Goal(s): To provide a feature-rich, massively parallelized and differentiable MR simulator. Approach: We built on the Extended Phase Graphs formalism to efficiently simulate all the main MR physical phenomena. We used PyTorch as a backend to enable massive parallelization and efficient differentiation. Results: Our toolbox, demonstrated on a numerical Fast Spin Echo experiment on an exchanging two-pool system, achieved order of magnitude speed-up compared to existing implementations and efficient differentiation with minimal boilerplate. Impact: Torch-EPG-X will represent a useful tool for synthetic signal generation for deep learning, parameter fitting, model-based reconstruction and sequence optimization.