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Abstract Purpose The effectiveness of prospective motion correction (PMC) is often evaluated by comparing artifacts in images acquired with and without PMC (NoPMC). However, such an approach is not applicable in clinical setting due to unavailability of NoPMC images. We aim to develop a simulation approach for demonstrating the ability of fat‐navigator‐based PMC in improving perivascular space (PVS) visibility in T 2 ‐weighted MRI. Methods MRI datasets from two earlier studies were used for motion artifact simulation and evaluating PMC, including T 2 ‐weighted NoPMC and PMC images. To simulate motion artifacts, k‐space data at motion‐perturbed positions were calculated from artifact‐free images using nonuniform Fourier transform and misplaced onto the Cartesian grid before inverse Fourier transform. The simulation's ability to reproduce motion‐induced blurring, ringing, and ghosting artifacts was evaluated using sharpness at lateral ventricle/white matter boundary, ringing artifact magnitude in the Fourier spectrum, and background noise, respectively. PVS volume fraction in white matter was employed to reflect its visibility. Results In simulation, sharpness, PVS volume fraction, and background noise exhibited significant negative correlations with motion score. Significant correlations were found in sharpness, ringing artifact magnitude, and PVS volume fraction between simulated and real NoPMC images ( p ≤ 0.006). In contrast, such correlations were reduced and nonsignificant between simulated and real PMC images ( p ≥ 0.48), suggesting reduction of motion effects with PMC. Conclusions The proposed simulation approach is an effective tool to study the effects of motion and PMC on PVS visibility. PMC may reduce the systematic bias of PVS volume fraction caused by motion artifacts.
Zhao et al. (Tue,) studied this question.