Enhanced motion artifact simulator for structural MRI with MP-RAGE sequence

Motivation: Motion compromises the utility of structural MRI with MP-RAGE sequence, a workhorse of quantitative neuroimaging research. Recent interest in deep learning-based mitigating solutions, and the scarcity of motion-corrupted data, motivates the need for realistic data simulation. Unfortunately, existing open-source simulators fail to consider important features in real-world acquisitions, including variations in phase-encoding direction, multi-coil acquisition and GRAPPA parallel imaging, resulting in less realistic simulations. Goal(s): We aim to develop a more realistic motion artifact simulator for MP-RAGE structural MRI. Approach: We extend TorchIO, an existing simulation framework, to support aforementioned features. Results: The comparison between simulations demonstrated the importance of including these features. Impact: The proposed simulation framework can be used to generate more realistic motion-corrupted MRI data from clean images. These data can be served as training sets for deep learning algorithms in motion artifact related applications.