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Recently, linear computed tomography (LCT) systems have actively attractedattention. To weaken projection truncation and image the region of interest(ROI) for LCT, the backprojection filtration (BPF) algorithm is an effectivesolution. However, in BPF for LCT, it is difficult to achieve stable interiorreconstruction, and for differentiated backprojection (DBP) images of LCT,multiple rotation-finite inversion of Hilbert transform (Hilbertfiltering)-inverse rotation operations will blur the image. To satisfy multiplereconstruction scenarios for LCT, including interior ROI, complete object, andexterior region beyond field-of-view (FOV), and avoid the rotation operationsof Hilbert filtering, we propose two types of reconstruction architectures. Thefirst overlays multiple DBP images to obtain a complete DBP image, then uses anetwork to learn the overlying Hilbert filtering function, referred to as theOverlay-Single Network (OSNet). The second uses multiple networks to traindifferent directional Hilbert filtering models for DBP images of multiplelinear scannings, respectively, and then overlays the reconstructed results,i.e., Multiple Networks Overlaying (MNetO). In two architectures, we introducea Swin Transformer (ST) block to the generator of pix2pixGAN to extract bothlocal and global features from DBP images at the same time. We investigate twoarchitectures from different networks, FOV sizes, pixel sizes, number ofprojections, geometric magnification, and processing time. Experimental resultsshow that two architectures can both recover images. OSNet outperforms BPF invarious scenarios. For the different networks, ST-pix2pixGAN is superior topix2pixGAN and CycleGAN. MNetO exhibits a few artifacts due to the differencesamong the multiple models, but any one of its models is suitable for imagingthe exterior edge in a certain direction.

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