  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.
