用扩散模型修复脑部MRI运动伪影，效果好坏取决于数据差异和成像平面。
Assessing the use of Diffusion models for motion artifact correction in brain MRI
- 对比扩散模型与监督式U-Net在运动伪影修复中的表现。
- 在部分数据上生成高质量图像，在另一些情况下产生有害幻觉。
- 适合关注医学影像修复中模型可靠性的研究人员参考。
磁共振成像通常需要较长的扫描时间，且对患者运动敏感，导致图像出现伪影，影响诊断价值。尽管缩短采集时间、优化采集序列的研究不断推进，运动伪影仍是顽固问题，亟需自动修正技术。近期，扩散模型被提出用于解决该问题。虽然扩散模型能生成高质量重建结果，但其易产生幻觉，可能危及诊断应用。本研究针对2D脑部MRI扫描中的运动伪影修正任务，使用主流基准数据集，将基于扩散模型的方法与基于监督训练的U-Net方法进行对比。结果表明：扩散模型的表现呈现两面性——在数据异质性较低或特定成像平面输入时可实现准确重建；但在其他情况下可能引入有害幻觉。
原文摘要 · Abstract (English)
Magnetic Resonance Imaging generally requires long exposure times, while being sensitive to patient motion, resulting in artifacts in the acquired images, which may hinder their diagnostic relevance. Despite research efforts to decrease the acquisition time, and designing efficient acquisition sequences, motion artifacts are still a persistent problem, pushing toward the need for the development of automatic motion artifact correction techniques. Recently, diffusion models have been proposed as a solution for the task at hand. While diffusion models can produce high-quality reconstructions, they are also susceptible to hallucination, which poses risks in diagnostic applications. In this study, we critically evaluate the use of diffusion models for correcting motion artifacts in 2D brain MRI scans. Using a popular benchmark dataset, we compare a diffusion model-based approach with state-of-the-art methods consisting of Unets trained in a supervised fashion on motion-affected images to reconstruct ground truth motion-free images. Our findings reveal mixed results: diffusion models can produce accurate predictions or generate harmful hallucinations in this context, depending on data heterogeneity and the acquisition planes considered as input.
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