Review of: "Mastering Artifact Correction in Neuroimaging Analysis: A Retrospective Approach"

A Retrospective ApproachThe paper presents an approach to artifact correction in MRI imaging using DL, with a two-model framework that is well-suited to addressing the lack of motion-corrupted datasets.However, further improvements in terms of dataset transparency, comparative performance, and analysis could strengthen the impact and applicability of this work.Nonetheless, MOANA holds significant potential for advancing MRI artifact correction and could be highly beneficial in clinical contexts.Several issues are raised, and the paper should be MAJOR revised before a final decision.