Retrospective artefact correction (RAC) improves image quality post acquisition and enhances image usability. Recent machine-learning-driven techniques for RAC are predominantly based on supervised learning, so practical utility can be limited as data with paired artefact-free and artefact-corrupted images are typically insufficient or even non-existent. Here we show that unwanted image artefacts can be disentangled and removed from an image via an RAC neural network learned with unpaired data. This implies that our method does not require matching artefact-corrupted data to be either collected via acquisition or generated via simulation. Experimental results demonstrate that our method is remarkably effective in removing artefacts and retaining anatomical details in images with different contrasts.

回顾性伪影校正 (RAC) 可提高采集后的图像质量并增强图像可用性。最近用于 RAC 的机器学习驱动技术主要基于监督学习，因此实用性可能会受到限制，因为具有成对的无伪影和伪影损坏图像的数据通常不足甚至不存在。在这里，我们展示了不需要的图像伪影可以通过使用未配对数据学习的 RAC 神经网络从图像中解开和移除。这意味着我们的方法不需要通过采集或通过模拟生成匹配的人工损坏数据。实验结果表明，我们的方法在去除伪影和保留不同对比度图像中的解剖细节方面非常有效。