CMRsim–A python package for cardiovascular MR simulations incorporating complex motion and flow - Weine, Jonathan; McGrath, Charles; Dirix, Pietro; Buoso, Stefano; Kozerke, Sebastian (Magnetic resonance in medicine, 2024)
Purpose To present an open‐source MR simulation framework that facilitates the incorporation of complex motion and flow for studying cardiovascular MR (CMR) acquisition and reconstruction. Methods CMRsim is a Python package that allows simulation of CMR images using dynamic digital phantoms with complex motion as input. Two simulation paradigms are available, namely, numerical and analytical solutions to the Bloch equations, using a common motion representation. Competitive simulation speeds are achieved using TensorFlow for GPU acceleration. To demonstrate the capability of the package, one introductory and two advanced CMR simulation experiments are presented. The latter showcase phase‐contrast imaging of turbulent flow downstream of a stenotic section and cardiac diffusion tensor imaging on a contracting left ventricle. Additionally, extensive documentation and example resources are provided. Results The Bloch simulation with turbulent flow using approximately 1.5 million particles and a sequence duration of 710 ms for each of the seven different velocity encodings took a total of 29 min on a NVIDIA Titan RTX GPU. The results show characteristic phase contrast and magnitude modulation present in real data. The analytical simulation of cardiac diffusion tensor imaging with bulk‐motion phase sensitivity took approximately 10 s per diffusion‐weighted image, including preparation and loading steps. The results exhibit the expected alteration of diffusion metrics due to strain. Conclusion CMRsim is the first simulation framework that allows one to feasibly incorporate complex motion, including turbulent flow, to systematically study advanced CMR acquisition and reconstruction approaches. The open‐source package features modularity and transparency, facilitating maintainability and extensibility in support of reproducible research.