Deep learning based solutions are being succesfully implemented for a wide variety of applications. Most notably, clinical use-cases have gained an increased interest and have been the main driver behind some of the cutting-edge data-driven algorithms proposed in the last years. For applications like sparse-view tomographic reconstructions, where the amount of measurement data is small in order to keep acquisition time short and radiation dose low, reduction of the streaking artifacts has prompted the development of data-driven denoising algorithms with the main goal of obtaining diagnostically viable images with only a subset of a full-scan data. We propose WNet, a data-driven dual-domain denoising model which contains a trainable reconstruction layer for sparse-view artifact denoising. Two encoder-decoder networks perform denoising in both sinogram- and reconstruction-domain simultaneously, while a third layer implementing the Filtered Backprojection algorithm is sandwiched between the first two and takes care of the reconstruction operation. We investigate the performance of the network on sparse-view chest CT scans, and we highlight the added benefit of having a trainable reconstruction layer over the more conventional fixed ones. We train and test our network on two clinically relevant datasets and we compare the obtained results with three different types of sparse-view CT denoising and reconstruction algorithms.

翻译：基于深度学习的解决方案正被成功应用于各类场景。其中，临床医疗用例引起广泛关注，并成为近年多项前沿数据驱动算法发展的主要驱动力。在稀疏视角断层重建等应用中，为缩短采集时间、降低辐射剂量，测量数据量通常较小。为消除此类场景下的条状伪影，数据驱动去噪算法应运而生，其核心目标是利用全扫描数据的子集获得具有诊断价值的图像。本文提出WNet——一种含可训练重建层的数据驱动双域去噪模型，专门用于稀疏视角伪影抑制。该模型采用两个编码-解码网络分别对正弦图域与重建域进行同步去噪，并在二者之间嵌入基于滤波反投影算法的第三层网络执行重建操作。我们在稀疏视角胸部CT扫描数据上评估网络性能，并重点论证可训练重建层相对于传统固定重建层的优势。基于两个临床相关数据集完成网络训练与测试，并将结果与三种不同类型稀疏视角CT去噪及重建算法进行对比分析。