Junning Cui

Zhisheng Wang, Zihan Deng, Fenglin Liu et al.

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.

Zhisheng Wang, Yanxu Sun, Shangyu Li et al.

For extending CT field-of-view to perform non-destructive testing, the Symmetric Multi-Linear trajectory Computed Tomography (SMLCT) has been developed as a successful example of non-standard CT scanning modes. However, inevitable geometric errors can cause severe artifacts in the reconstructed images. The existing calibration method for SMLCT is both crude and inefficient. It involves reconstructing hundreds of images by exhaustively substituting each potential error, and then manually identifying the images with the fewest geometric artifacts to estimate the final geometric errors for calibration. In this paper, we comprehensively and efficiently address the challenging geometric artifacts in SMLCT, , and the corresponding works mainly involve theory, method, and generalization. In particular, after identifying sensitive parameters and conducting some theory analysis of geometric artifacts, we summarize several key properties between sensitive geometric parameters and artifact characteristics. Then, we further construct mathematical relationships that relate sensitive geometric errors to the pixel offsets of reconstruction images with artifact characteristics. To accurately extract pixel bias, we innovatively adapt the Generalized Cross-Correlation with Phase Transform (GCC-PHAT) algorithm, commonly used in sound processing, for our image registration task for each paired symmetric LCT. This adaptation leads to the design of a highly efficient rigid translation registration method. Simulation and physical experiments have validated the excellent performance of this work. Additionally, our results demonstrate significant generalization to common rotated CT and a variant of SMLCT.

Zhisheng Wang, Yue Liu, Shunli Wang et al.

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 (their differences are different derivate directions along the detector and source, respectively), make some errors and artifacts in the reconstructed images due to a 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 combine the full-scan mSTCT (F-mSTCT) geometry with the previous BPF algorithms to study the performance and derive a suitable redundancy-weighted function for F-mSTCT. The experimental results indicate FS-BPF can get high-quality, stable images under the extremely extended FOV of imaging a large object, though it requires more projections than FD-BPF. Finally, for different practical requirements in extending FOV imaging, we give suggestions on algorithm selection.

Zhisheng Wang, Haijun Yu, Yixing Huang et al.

Micro-computed tomography (micro-CT) is a widely used state-of-the-art instrument employed to study the morphological structures of objects in various fields. However, its small field-of-view (FOV) cannot meet the pressing demand for imaging relatively large objects at high spatial resolutions. Recently, we devised a novel scanning mode called multiple source translation CT (mSTCT) that effectively enlarges the FOV of the micro-CT and correspondingly developed a virtual projection-based filtered backprojection (V-FBP) algorithm for reconstruction. Although V-FBP skillfully solves the truncation problem in mSTCT, it requires densely sampled projections to arrive at high-resolution reconstruction, which reduces imaging efficiency. In this paper, we developed two backprojection-filtration (BPF)-based algorithms for mSTCT, i.e., S-BPF (derivatives along source) and D-BPF (derivatives along detector). D-BPF can achieve high-resolution reconstruction with fewer projections than V-FBP and S-BPF. Through simulated and real experiments conducted in this paper, we demonstrate that D-BPF can reduce source sampling by 75% compared with V-FBP at the same spatial resolution, which makes mSTCT more feasible in practice. Meanwhile, S-BPF can yield more stable results than D-BPF, which is similar to V-FBP.