APHABAMAS: An analytical phantom-based scheme for assessing the accuracy of high-resolution 3D MRI motion-artifact simulations
TL;DR
A digital phantom whose representations in both the image and Fourier domains can be expressed analytically under arbitrary rigid-body transformations allows the accuracy-based ranking of existing simulation algorithms to be established, thereby enabling informed selection of the most suitable algorithm for synthesizing motion-corrupted data.
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. Developing such a scheme is the aim of this study. Methods: The essential ingredient of the desired scheme is a ground-truth reference simulation that does not suffer from sampling-induced error. To meet this requirement, the proposed scheme, APHABAMAS, leverages a digital phantom whose representations in both the image and Fourier domains can be expressed analytically under arbitrary rigid-body transformations. Results: APHABAMAS is used to quantify the sampling-induced errors of three existing simulation algorithms, establishing their first definitive accuracy-based ranking. Conclusions: APHABAMAS provides a rigorous tool for assessing the accuracy of high-resolution 3D MRI motion-artifact simulations. It allows the accuracy-based ranking of existing simulation algorithms to be established, thereby enabling informed selection of the most suitable algorithm for synthesizing motion-corrupted data.