Metal implants in the patient's body can generate severe metal artifacts in x‐ray computed tomography (CT) images. These artifacts may cover the tissues around the metal implants in CT images and even corrupt the tissue regions, thus affecting disease diagnosis using these images. Previous deep learning metal trace inpainting methods used both valid pixels of uncorrupted areas and invalid pixels of corrupted areas to patch metal trace (i.e., the holes of removed metal‐corrupted regions). Such methods cannot recover fine details well and often suffer information mismatch due to interference of invalid pixels, thus incurring considerable secondary artifacts. In this paper, we develop a new irregular metal trace inpainting network for reducing metal artifacts.

中文翻译：

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用于减少X射线CT金属伪影的不规则金属痕迹修补网络。

患者体内的金属植入物会在X射线计算机断层扫描（CT）图像中产生严重的金属伪影。这些伪影可能会覆盖CT图像中金属植入物周围的组织，甚至破坏组织区域，从而影响使用这些图像进行的疾病诊断。先前的深度学习金属迹线修复方法使用未损坏区域的有效像素和损坏区域的无效像素来修补金属迹线（即，已移除的金属损坏区域的孔）。这样的方法不能很好地恢复细节，并且由于无效像素的干扰而经常遭受信息失配，从而导致相当大的二次伪像。在本文中，我们开发了一种新的不规则金属痕迹修补网络，以减少金属伪像。