APHABAMAS: An analytical phantom-based scheme for assessing the accuracy of high-resolution 3D MRI motion-artifact simulations

Abstract

Purpose: Motion compromises the utility of high-resolution 3D MRI, an established tool in quantitative neuroimaging research. Deep learning-based methods have shown promise for mitigating motion-induced artifacts, but their development typically requires simulated motion-corrupted data. Several open-source tools exist for this task, each implementing different algorithms. However, no scheme currently exists for evaluating the accuracy of these simulations, making it difficult for users to choose the most suitable tool. This study aims to develop such a scheme.Methods: The essential ingredient of the desired scheme is a ground-truth reference that does not suffer from sampling-induced error. To meet this requirement, the proposed scheme, APHABAMAS, leverages a digital phantom whose image- and Fourier-domain representations can be expressed analytically under arbitrary rigid-body transformations. APHABAMAS is used to quantify sampling-induced errors of three existing algorithms under synthetic and tracking-data-derived motion trajectories at different motion severity levels. Results: All three algorithms produce distinct but visually plausible artifacts, demonstrating the need for a ground-truth reference. APHABAMAS shows that the algorithm utilizing the uniform-to-non-uniform (Type-2) NUFFT provides the most consistent agreement with the ground-truth reference across motion types and severity levels. The non-uniform-to-uniform (Type-1) NUFFT-based algorithm, which does not mimic the MR acquisition process, deteriorates substantially with increasing motion severity. Conclusions: APHABAMAS provides a rigorous tool for assessing the accuracy of high-resolution 3D MRI motion-artifact simulations. It enables accuracy-based assessment of three existing algorithms, thereby facilitating informed selection of the most suitable one for synthesizing motion-corrupted data.