TY  - EJOU
AU  - Cheng, Shuaiqi 
AU  - Chen, Yuxi 
AU  - Yang, Bo 
AU  - Zhang, Legend 
AU  - Lyu, Junmin 
AU  - Xu, Guangyu 
AU  - Liu, Chao 

TI  - Sparse-View CT Reconstruction with Deep Learning: A Comprehensive Survey
T2  - Computer Modeling in Engineering \& Sciences

PY  - 
VL  - 
IS  - 
SN  - 1526-1506

AB  - Computed tomography (CT) is an essential medical imaging technique that produces high-resolution cross-sectional images, but delivers substantial radiation dose to patients. Sparse-view CT (SVCT) reduces radiation dose by decreasing projection views, but causes streak artifacts that degrade image quality. Deep learning has emerged as a powerful tool to address this challenge. Although prospective paired acquisitions for low-current/voltage CT may be constrained by radiation-dose management and clinical workflow considerations, SVCT provides a practical way to construct paired training data through retrospective angular downsampling of full-view projections. This survey provides a systematic review of deep learning–based SVCT, analyzing nearly 70 articles published since 2017. We describe CT imaging fundamentals with differentiable back-projection, propose a taxonomy of reconstruction frameworks (image-domain, sinogram-domain, dual-domain, direct mapping, and enhanced iterative), and review neural network architectures, loss functions and datasets. We further provide open-source implementations of fan-beam forward projection and filtered back projection for dual-domain training. Finally, we discuss future directions and key remaining challenges.
KW  - Sparse-view CT; deep learning; CT reconstruction; low dose CT; sinogram

DO  - 10.32604/cmes.2026.085787