Recently, linear computed tomography (LCT) systems have actively attracted attention. To weaken projection truncation and image the region of interest (ROI) for LCT, the backprojection filtration (BPF) algorithm is an effective solution. However, in BPF for LCT, it is difficult to achieve stable interior reconstruction, and for differentiated backprojection (DBP) images of LCT, multiple rotation-finite inversion of Hilbert transform (Hilbert filtering)-inverse rotation operations will blur the image. To satisfy multiple reconstruction scenarios for LCT, including interior ROI, complete object, and exterior region beyond field-of-view (FOV), and avoid the rotation operations of Hilbert filtering, we propose two types of reconstruction architectures. The first overlays multiple DBP images to obtain a complete DBP image, then uses a network to learn the overlying Hilbert filtering function, referred to as the Overlay-Single Network (OSNet). The second uses multiple networks to train different directional Hilbert filtering models for DBP images of multiple linear scannings, respectively, and then overlays the reconstructed results, i.e., Multiple Networks Overlaying (MNetO). In two architectures, we introduce a Swin Transformer (ST) block to the generator of pix2pixGAN to extract both local and global features from DBP images at the same time. We investigate two architectures from different networks, FOV sizes, pixel sizes, number of projections, geometric magnification, and processing time. Experimental results show that two architectures can both recover images. OSNet outperforms BPF in various scenarios. For the different networks, ST-pix2pixGAN is superior to pix2pixGAN and CycleGAN. MNetO exhibits a few artifacts due to the differences among the multiple models, but any one of its models is suitable for imaging the exterior edge in a certain direction.

翻译：近年来，线性计算机断层扫描（LCT）系统引起了广泛关注。为削弱LCT的投影截断效应并实现感兴趣区域（ROI）成像，反投影滤波（BPF）算法是一种有效方案。然而，LCT的BPF算法难以实现稳定的内部重建，且其微分反投影（DBP）图像需经多次旋转-有限希尔伯特变换（希尔伯特滤波）-反旋转操作，这将导致图像模糊。为满足LCT的多场景重建需求（包括内部ROI、完整物体及视场外外部区域），并避免希尔伯特滤波的旋转操作，我们提出两种重建架构。第一种架构通过叠加多张DBP图像获取完整DBP图像，再利用网络学习叠加过程的希尔伯特滤波函数，称为叠加单网络（OSNet）。第二种架构采用多个网络分别训练多种线性扫描方向对应的DBP图像的希尔伯特滤波模型，再将重建结果进行叠加，即多网络叠加（MNetO）。两种架构中，我们在pix2pixGAN的生成器中引入Swin Transformer（ST）模块，以同时提取DBP图像的局部与全局特征。我们从不同网络结构、视场尺寸、像素尺寸、投影次数、几何放大倍数及处理时间等多个维度对两种架构进行了研究。实验结果表明，两种架构均能有效恢复图像。OSNet在各场景下的表现均优于BPF算法。在不同网络结构中，ST-pix2pixGAN性能优于pix2pixGAN和CycleGAN。MNetO因多模型差异会产生少量伪影，但其任一模型均适用于特定方向的外部边缘成像。