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arXiv · 2506.05975

Reliable Evaluation of MRI Motion Correction: Dataset and Insights

Kun Wang·Tobit Klug·Stefan Ruschke·Jan S. Kirschke·Reinhard Heckel
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Abstract

Correcting motion artifacts in MRI is important, as they can hinder accurate diagnosis. However, evaluating deep learning-based and classical motion correction methods remains fundamentally difficult due to the lack of accessible ground-truth target data. To address this challenge, we study three evaluation approaches: real-world evaluation based on reference scans, simulated motion, and reference-free evaluation, each with its merits and shortcomings. To enable evaluation with real-world motion artifacts, we release PMoC3D, a dataset consisting of unprocessed Paired Motion-Corrupted 3D brain MRI data. To advance evaluation quality, we introduce MoMRISim, a feature-space metric trained for evaluating motion reconstructions. We assess each evaluation approach and find real-world evaluation together with MoMRISim, while not perfect, to be most reliable. Evaluation based on simulated motion systematically exaggerates algorithm performance, and reference-free evaluation overrates oversmoothed deep learning outputs.

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Kun Wang, Tobit Klug, Stefan Ruschke, Jan S. Kirschke, Reinhard Heckel. 2025-06-06. Reliable Evaluation of MRI Motion Correction: Dataset and Insights. https://arxiv.org/abs/2506.05975

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