@Article{cmes.2026.085787,
AUTHOR = {Shuaiqi Cheng, Yuxi Chen, Bo Yang, Legend Zhang, Junmin Lyu, Guangyu Xu, Chao Liu},
TITLE = {Sparse-View CT Reconstruction with Deep Learning: A Comprehensive Survey},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/CMES/online/detail/27871},
ISSN = {1526-1506},
ABSTRACT = {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.},
DOI = {10.32604/cmes.2026.085787}
}