A GPU-accelerated MRI Sequence Simulation for Differentiable Optimization and Learning

The Extended Phase Graph (EPG) Algorithm is a powerful tool for MRI sequence simulation and quantitative fitting, but such simulators are mostly written to run on CPU only and (with some exception) are poorly parallelized. A parallelized simulator compatible with other learning-based frameworks would be a useful tool to optimize scan parameters. Thus, we created an open source, GPU-accelerated EPG simulator in PyTorch. Since the simulator is fully differentiable by means of automatic differentiation, it canbe used to take derivatives with respect to sequence parameters, e.g. flip angles, as well as tissue parameters, e.g. T1 and T2.