Abstract. The dose of X-ray radiation and the scanning time are crucial factors in computed tomography (CT) for clinical applications. In this work, we introduce a multisource static CT (MSCT) imaging system designed to rapidly acquire sparse view and limited angle data in CT imaging, addressing these critical factors. This linear imaging inverse problem is solved by a conditional generation process within the denoising diffusion image reconstruction framework. The noisy volume data sample generated by the reverse time diffusion process is projected onto the affine set to ensure its consistency with the measured data. To enhance the quality of the reconstruction, the 3D phantom’s orthogonal space projector is parameterized implicitly by a neural network. Then, a self-supervised learning algorithm is adopted to optimize the implicit neural representation. Through this multistage conditional generation process, we obtain a new approximate posterior sampling strategy for MSCT volume reconstruction. Numerical experiments are implemented with various imaging settings to verify the effectiveness of our methods for incomplete data MSCT volume reconstruction.

Computed tomography (CT) is a widely-used imaging technology that assists clinical decision-making with high-quality human body representations. To reduce the radiation dose posed by CT, sparse-view (SV) CT is developed with preserved image quality. However, these methods are still stuck with a fixed uniform SV (USV) sampling strategy, which inhibits the possibility of acquiring a better image with an even reduced dose. In this paper, we explore this possibility via learning an active SV (ASV) sampling policy that optimizes the sampling positions for regions of interest (RoI)-specific, high-quality reconstruction. To this end, we design an sampling agent for the recommendation of ASV sampling positions based on on-the-fly reconstruction with obtained sinograms in a progressive fashion. With such a design, we achieve better performances on the NIH-AAPM dataset over popular USV sampling, especially when the number of views is small. Finally, such a design enables the RoI-aware reconstruction with improved local quality within the RoI that are clinically important. Experiments on the VerSe dataset demonstrate the ability of the proposed sampling policy, which is difficult to achieve with USV sampling.

X-ray Computed Tomography (CT) is widely used in clinical applications such as diagnosis and image-guided interventions. In this paper, we propose a new deep learning based model for CT image reconstruction with the backbone network architecture built by unrolling an iterative algorithm. However, unlike the existing strategy to include as many data-adaptive components in the unrolled dynamics model as possible, we find that it is enough to only learn the parts where traditional designs mostly rely on intuitions and experience. More specifically, we propose to learn an initializer for the conjugate gradient (CG) algorithm that involved in one of the subproblems of the backbone model. Other components, such as image priors and hyperparameters, are kept as the original design. Since a hypernetwork is introduced to inference on the initialization of the CG module, it makes the proposed model a certain meta-learning model. Therefore, we shall call the proposed model the meta-inversion network (MetaInv-Net). The proposed MetaInv-Net can be designed with much less trainable parameters while still preserves its superior image reconstruction performance than some state-of-the-art deep models in CT imaging. In simulated and real data experiments, MetaInv-Net performs very well and can be generalized beyond the training setting, i.e., to other scanning settings, noise levels, and data sets.

CT image reconstruction from incomplete data, such as sparse views and limited angle reconstruction, is an important and challenging problem in medical imaging. This work proposes a new deep convolutional neural network (CNN), called JSR-Net, that jointly reconstructs CT images and their associated Radon domain projections. JSR-Net combines the traditional model based approach with deep architecture design of deep learning. A hybrid loss function is adopted to improve the performance of the JSR-Net making it more effective in protecting important image structures. Numerical experiments demonstrate that JSR-Net outperforms some latest model based reconstruction methods, as well as a recently proposed deep model.

High-density implants such as metals often lead to serious artifacts in reconstructed computerized tomographic (CT) images, which hampers the accuracy of image-based diagnosis and treatment planning. In this paper, we propose a novel wavelet frame--based CT image reconstruction model to reduce metal artifacts. This model is built on a joint spatial and Radon (projection) domain (JSR) image reconstruction framework with a built-in weighting and reweighting mechanism in the Radon domain to repair degraded projection data. The new weighting strategy used in the proposed model makes the regularization in the Radon domain by wavelet frame transform more effective. The proposed model, which will be referred to as the reweighted JSR model, combines the ideas of the recently proposed wavelet frame--based JSR model [B. Dong, J. Li, and Z. Shen, J. Sci. Comput., 54 (2013), pp. 333--349] and the normalized metal artifact reduction model [E. Meyer, R. Raupach, M. Lell, B. Schmidt, and M. Kachelriess, Med. Phys., 37 (2010), pp. 5482--5493.] and manages to achieve noticeably better CT reconstruction quality than both methods. To solve the proposed reweighted JSR model, an efficient alternative iteration algorithm is proposed with guaranteed convergence. Numerical experiments on both simulated and real CT image data demonstrate the effectiveness of the reweighted JSR model and its advantage over some state-of-the-art methods.