Sparse-view computed tomography (SVCT) aims to reconstruct a cross-sectional image using a reduced number of x-ray projections. While SVCT can efficiently reduce the radiation dose, the reconstruction suffers from severe streak artifacts, and the artifacts are further amplified with the presence of metallic implants, which could adversely impact the medical diagnosis and other downstream applications. Previous methods have extensively explored either SVCT reconstruction without metallic implants, or full-view CT metal artifact reduction (MAR). The issue of simultaneous sparse-view and metal artifact reduction (SVMAR) remains under-explored, and it is infeasible to directly apply previous SVCT and MAR methods to SVMAR which may yield non-ideal reconstruction quality. In this work, we propose a dual-domain data consistent recurrent network, called DuDoDR-Net, for SVMAR. Our DuDoDR-Net aims to reconstruct an artifact-free image by recurrent image domain and sinogram domain restorations. To ensure the metal-free part of acquired projection data is preserved, we also develop the image data consistent layer (iDCL) and sinogram data consistent layer (sDCL) that are interleaved in our recurrent framework. Our experimental results demonstrate that our DuDoDR-Net is able to produce superior artifact-reduced results while preserving the anatomical structures, that outperforming previous SVCT and SVMAR methods, under different sparse-view acquisition settings.

稀疏视图计算机断层扫描 (SVCT) 旨在使用减少数量的 X 射线投影重建横截面图像。虽然SVCT可以有效降低辐射剂量，但重建会受到严重的条纹伪影的影响，并且随着金属植入物的存在，伪影会进一步放大，这可能会对医疗诊断和其他下游应用产生不利影响。以前的方法广泛探索了无金属植入物的 SVCT 重建或全视图 CT 金属伪影减少 (MAR)。同时稀疏视图和金属伪影减少（SVMAR）的问题仍未得到充分研究，并且直接将先前的 SVCT 和 MAR 方法应用于 SVMAR 是不可行的，这可能会产生不理想的重建质量。在这项工作中，我们提出了一种用于 SVMAR 的双域数据一致循环网络，称为 DuDoDR-Net。我们的 DuDoDR-Net 旨在通过循环图像域和正弦图域恢复来重建无伪影图像。为了确保保留所采集投影数据的无金属部分，我们还开发了在循环框架中交错的图像数据一致层（iDCL）和正弦图数据一致层（sDCL）。我们的实验结果表明，我们的 DuDoDR-Net 能够在保留解剖结构的同时产生出色的伪影减少结果，在不同的稀疏视图采集设置下，其性能优于之前的 SVCT 和 SVMAR 方法。