自动评估核磁共振运动伪影等级，还能解释判断依据。
Automated Motion Artifact Check for MRI (AutoMAC-MRI): An Interpretable Framework for Motion Artifact Detection and Severity Assessment
- 用对比学习提取运动伪影特征，构建可解释的分级模型。
- 在5000+张脑部MRI图像上验证，评分与专家判断高度一致。
- 适合临床用于实时质量控制，减少重复扫描。
运动伪影会降低核磁共振成像质量并增加患者复查率。现有自动化质量评估方法多为二分类，缺乏可解释性。本文提出AutoMAC-MRI，一种可解释的运动伪影分级框架，适用于多种磁共振对比度和方位。该方法采用监督对比学习，学习运动严重程度的判别性表征；在特征空间中计算各等级的亲和度得分，量化图像与各等级的接近程度，使分级结果透明可解释。我们在超过5000张由专家标注的脑部MRI切片上评估该方法，涵盖多种对比度和视角。实验显示，亲和度得分与专家标注高度吻合，支持其作为运动严重程度的可解释度量。通过结合精准分级与每级亲和度评分，AutoMAC-MRI可实现核磁共振成像的在线质量控制，有望减少不必要的重扫，提升流程效率。
原文摘要 · Abstract (English)
Motion artifacts degrade MRI image quality and increase patient recalls. Existing automated quality assessment methods are largely limited to binary decisions and provide little interpretability. We introduce AutoMAC-MRI, an explainable framework for grading motion artifacts across heterogeneous MR contrasts and orientations. The approach uses supervised contrastive learning to learn a discriminative representation of motion severity. Within this feature space, we compute grade-specific affinity scores that quantify an image's proximity to each motion grade, thereby making grade assignments transparent and interpretable. We evaluate AutoMAC-MRI on more than 5000 expert-annotated brain MRI slices spanning multiple contrasts and views. Experiments assessing affinity scores against expert labels show that the scores align well with expert judgment, supporting their use as an interpretable measure of motion severity. By coupling accurate grade detection with per-grade affinity scoring, AutoMAC-MRI enables inline MRI quality control, with the potential to reduce unnecessary rescans and improve workflow efficiency.
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