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Limited-angle CT is a challenging problem in real applications. Incomplete projection data will lead to severe artifacts and distortions in reconstruction images. To tackle this problem, we propose a novel reconstruction framework termed Deep Iterative Optimization-based Residual-learning (DIOR) for limited-angle CT. Instead of directly deploying the regularization term on image space, the DIOR combines iterative optimization and deep learning based on the residual domain, significantly improving the convergence property and generalization ability. Specifically, the asymmetric convolutional modules are adopted to strengthen the feature extraction capacity in smooth regions for deep priors. Besides, in our DIOR method, the information contained in low-frequency and high-frequency components is also evaluated by perceptual loss to improve the performance in tissue preservation. Both simulated and clinical datasets are performed to validate the performance of DIOR. Compared with existing competitive algorithms, quantitative and qualitative results show that the proposed method brings a promising improvement in artifact removal, detail restoration and edge preservation. [ABSTRACT FROM AUTHOR]

X-ray computed tomography (CT) is of great clinical significance in medical practice because it can provide anatomical information about the human body without invasion, while its radiation risk has continued to attract public concerns. Reducing the radiation dose may induce noise and artifacts to the reconstructed images, which will interfere with the judgments of radiologists. Previous studies have confirmed that deep learning (DL) is promising for improving low-dose CT imaging. However, almost all the DL-based methods suffer from subtle structure degeneration and blurring effect after aggressive denoising, which has become the general challenging issue. This paper develops the Comprehensive Learning Enabled Adversarial Reconstruction (CLEAR) method to tackle the above problems. CLEAR achieves subtle structure enhanced low-dose CT imaging through a progressive improvement strategy. First, the generator established on the comprehensive domain can extract more features than the one built on degraded CT images and directly map raw projections to high-quality CT images, which is significantly different from the routine GAN practice. Second, a multi-level loss is assigned to the generator to push all the network components to be updated towards high-quality reconstruction, preserving the consistency between generated images and gold-standard images. Finally, following the WGAN-GP modality, CLEAR can migrate the real statistical properties to the generated images to alleviate over-smoothing. Qualitative and quantitative analyses have demonstrated the competitive performance of CLEAR in terms of noise suppression, structural fidelity and visual perception improvement. [ABSTRACT FROM AUTHOR]

This work demonstrates the feasibility of two-orthogonal-projection-based CBCT (2V-CBCT) reconstruction and dose calculation for radiation therapy (RT) using real projection data, which is the first 2V-CBCT feasibility study with real projection data, to the best of our knowledge. RT treatments are often delivered in multiple fractions, for which on-board CBCT is desirable to calculate the delivered dose per fraction for the purpose of RT delivery quality assurance and adaptive RT. However, not all RT treatments/fractions have CBCT acquired, but two orthogonal projections are always available. The question to be addressed in this work is the feasibility of 2V-CBCT for the purpose of RT dose calculation. 2V-CBCT is a severely ill-posed inverse problem for which we propose a coarse-to-fine learning strategy. First, a 3D deep neural network that can extract and exploit the inter-slice and intra-slice information is adopted to predict the initial 3D volumes. Then, a 2D deep neural network is utilized to fine-tune the initial 3D volumes slice-by-slice. During the fine-tuning stage, a perceptual loss based on multi-frequency features is employed to enhance the image reconstruction. Dose calculation results from both photon and proton RT demonstrate that 2V-CBCT provides comparable accuracy with full-view CBCT based on real projection data.