提出真实脑部MRI运动伪影数据集与评估方法，提升运动校正模型可靠性。
Reliable Evaluation of MRI Motion Correction: Dataset and Insights
- 构建真实运动伪影数据集PMoC3D，支持真实场景评估
- 引入特征空间度量MoMRISim，显著提升评估准确性
- 发现模拟数据会高估性能，无参考评估易偏好平滑结果
MRI运动伪影的校正对准确诊断至关重要，但深度学习与传统方法的评估因缺乏可访问的真实目标数据而困难重重。为此，本文研究了三种评估方法：基于参考扫描的真实世界评估、模拟运动和无参考评估，各有优劣。为支持真实运动伪影的评估，我们发布PMoC3D数据集，包含未处理的配对运动污染3D脑部MRI。为提升评估质量，我们提出MoMRISim，一种在特征空间训练的运动重建评估度量。通过对比各方法，发现结合真实世界评估与MoMRISim最具可靠性。基于模拟运动的评估系统性高估算法性能，而无参考评估则过度青睐过度平滑的深度学习输出。
原文摘要 · Abstract (English)
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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