Motion artifacts present in magnetic resonance imaging (MRI) can seriously interfere with clinical diagnosis. Removing motion artifacts is a straightforward solution and has been extensively studied. However, paired data are still heavily relied on in recent works and the perturbations in \textit{k}-space (frequency domain) are not well considered, which limits their applications in the clinical field. To address these issues, we propose a novel unsupervised purification method which leverages pixel-frequency information of noisy MRI images to guide a pre-trained diffusion model to recover clean MRI images. Specifically, considering that motion artifacts are mainly concentrated in high-frequency components in \textit{k}-space, we utilize the low-frequency components as the guide to ensure correct tissue textures. Additionally, given that high-frequency and pixel information are helpful for recovering shape and detail textures, we design alternate complementary masks to simultaneously destroy the artifact structure and exploit useful information. Quantitative experiments are performed on datasets from different tissues and show that our method achieves superior performance on several metrics. Qualitative evaluations with radiologists also show that our method provides better clinical feedback. Our code is available at https://github.com/medcx/PFAD.

翻译：磁共振成像（MRI）中存在的运动伪影会严重干扰临床诊断。去除运动伪影是一种直接的解决方案，并已得到广泛研究。然而，现有方法仍严重依赖配对数据，且未能充分考虑 \textit{k} 空间（频域）中的扰动，这限制了其在临床领域的应用。为解决这些问题，我们提出一种新颖的无监督净化方法，该方法利用含噪 MRI 图像的像素-频率信息来引导预训练的扩散模型以恢复干净的 MRI 图像。具体而言，考虑到运动伪影主要集中在 \textit{k} 空间的高频分量中，我们利用低频分量作为引导以确保正确的组织纹理。此外，鉴于高频和像素信息有助于恢复形状和细节纹理，我们设计了交替互补掩码，以同时破坏伪影结构并利用有用信息。我们在不同组织的多个数据集上进行了定量实验，结果表明我们的方法在多项指标上均取得了优越的性能。与放射科医师进行的定性评估也表明，我们的方法提供了更好的临床反馈。我们的代码可在 https://github.com/medcx/PFAD 获取。