CMRsim-A python package for cardiovascular MR simulations incorporating complex motion and flow
Open access
Date
2024-06Type
- Journal Article
ETH Bibliography
yes
Altmetrics
Abstract
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 Tensor Flow 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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000656442Publication status
publishedExternal links
Journal / series
Magnetic Resonance in MedicineVolume
Pages / Article No.
Publisher
WileySubject
cardiovascular; GPU; motion; MRI simulation; open source; PythonFunding
197702 - Learning Physics-Based Optimal Design of Cardiovascular MRI (SNF)
More
Show all metadata
ETH Bibliography
yes
Altmetrics