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.

提出两种用于线性计算机断层扫描（LCT）的重建体系结构，其中一种通过神经网络学习 Hilbert 滤波函数从而得到完整的内部重建，另一种通过多个方向的 Hilbert 滤波模型叠加重建结果，虽然存在一些伪影，但适用于特定方向的外部轮廓成像。

OSNet 和 MNetO：多场景线性计算机断层扫描的两种通用重建架构

OSNet & MNetO: Two Types of General Reconstruction Architectures for  Linear Computed Tomography in Multi-Scenarios

This paper is to investigate the high-quality analytical reconstructions of
multiple source-translation computed tomography (mSTCT) under an extended field
of view (FOV). Under the larger FOVs, the previously proposed backprojection
filtration (BPF) algorithms for mSTCT, including D-BPF and S-BPF, make some
intolerable errors in the image edges due to an unstable backprojection
weighting factor and the half-scan mode, which deviates from the intention of
mSTCT imaging. In this paper, to achieve reconstruction with as little error as
possible under the extremely extended FOV, we propose two strategies, including
deriving a no-weighting D-BPF (NWD-BPF) for mSTCT and introducing BPFs into a
special full-scan mSTCT (F-mSTCT) to balance errors, i.e., abbreviated as
FD-BPF and FS-BPF. For the first strategy, we eliminate this unstable
backprojection weighting factor by introducing a special variable relationship
in D-BPF. For the second strategy, we combine the F-mSTCT geometry with BPFs to
study the performance and derive a suitable redundant weighting function for
F-mSTCT. The experiments demonstrate our proposed methods for these strategies.
Among them, NWD-BPF can weaken the instability at the image edges but blur the
details, and FS-BPF can get high-quality stable images under the extremely
extended FOV imaging a large object but requires more projections than FD-BPF.
For different practical requirements in extending FOV imaging, we give
suggestions on algorithm selection.

研究在扩展的视野下，采用两种策略实现多源平移计算机体层摄影的高质量分析重建，分别为无权 D-BPF 和全扫描 mSTCT 引入 BPF 以平衡误差。