Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance. In reality, CT images may be affected adversely in the presence of metallic objects, which could lead to severe metal artifacts and influence clinical diagnosis or dose calculation in radiation therapy. In this article, we propose a generalizable framework for metal artifact reduction (MAR) by simultaneously leveraging the advantages of image domain and sinogram domain-based MAR techniques. We formulate our framework as a sinogram completion problem and train a neural network (SinoNet) to restore the metal-affected projections. To improve the continuity of the completed projections at the boundary of metal trace and thus alleviate new artifacts in the reconstructed CT images, we train another neural network (PriorNet) to generate a good prior image to guide sinogram learning, and further design a novel residual sinogram learning strategy to effectively utilize the prior image information for better sinogram completion. The two networks are jointly trained in an end-to-end fashion with a differentiable forward projection (FP) operation so that the prior image generation and deep sinogram completion procedures can benefit from each other. Finally, the artifact-reduced CT images are reconstructed using the filtered backward projection (FBP) from the completed sinogram. Extensive experiments on simulated and real artifacts data demonstrate that our method produces superior artifact-reduced results while preserving the anatomical structures and outperforms other MAR methods.

中文翻译：

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使用图像先验进行深度正弦图补全，以减少 CT 图像中的金属伪影


计算机断层扫描 (CT) 已广泛用于医学诊断、评估以及治疗计划和指导。事实上，CT 图像在存在金属物体的情况下可能会受到不利影响，这可能导致严重的金属伪影并影响临床诊断或放射治疗中的剂量计算。在本文中，我们通过同时利用图像域和基于正弦图域的 MAR 技术的优势，提出了一种用于金属伪影减少 (MAR) 的通用框架。我们将我们的框架制定为正弦图完成问题，并训练神经网络（SinoNet）来恢复受金属影响的投影。为了提高金属迹线边界处已完成投影的连续性，从而减轻重建 CT 图像中的新伪影，我们训练另一个神经网络（PriorNet）来生成良好的先验图像来指导正弦图学习，并进一步设计一种新颖的残差正弦图学习策略有效利用先验图像信息以更好地完成正弦图。这两个网络通过可微分前向投影（FP）操作以端到端的方式联合训练，以便先前的图像生成和深度正弦图完成过程可以相互受益。最后，使用来自完整正弦图的滤波后向投影 (FBP) 重建伪影减少的 CT 图像。对模拟和真实伪影数据进行的大量实验表明，我们的方法可以产生出色的伪影减少结果，同时保留解剖结构，并且优于其他 MAR 方法。