Helical CT Reconstruction from Sparse-view Data through Exploiting the 3D Anatomical Structure Sparsity

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 circular imaging geometry, whereas helical geometry is commonly adopted clinic. In this paper, we show that (SHCT) contain not only but also severe anatomical distortions. These troubles reduce applicability of existing algorithms. To deal with problem, analyzed three-dimensional (3D) structure sparsity SHCT images. Based on analyses, proposed a tensor decomposition anisotropic total variation regularization model (TDATV) reconstruction. Specifically, works nonlocal cube groups exploit redundancy; whole volume structural piecewise-smooth. Finally, an alternating direction method multipliers developed solve TDATV model. our knowledge, paper presents first work investigating CT. The was validated through digital phantom, physical clinical patient studies. results reveal could serve as solution reducing HCT radiation dose ultra-low level by using