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Helical CT Reconstruction from Sparse-view Data through Exploiting the 3D Anatomical Structure Sparsity
2021
IEEE Access
Sparse-view scanning has great potential for realizing ultra-low-dose computed tomography (CT) examination. However, noise and artifacts in reconstructed images are big obstacles, which must be handled to maintain the diagnosis accuracy. Existing sparse-view CT reconstruction algorithms were usually designed for circular imaging geometry, whereas the helical imaging geometry is commonly adopted in the clinic. In this paper, we show that the sparse-view helical CT (SHCT) images contain not only
doi:10.1109/access.2021.3049181
fatcat:xu44rurg7jcp7gehhbrd6rijwm