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Scientific article
Open access
English

Helical CT Reconstruction From Sparse-View Data Through Exploiting the 3D Anatomical Structure Sparsity

Published inIEEE Access, vol. 9, p. 15200-15211
Publication date2021
Abstract

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 noise and artifacts but also severe anatomical distortions. These troubles reduce the applicability of existing sparse-view CT reconstruction algorithms. To deal with this problem, we analyzed the three-dimensional (3D) anatomical structure sparsity in SHCT images. Based on the analyses, we proposed a tensor decomposition and anisotropic total variation regularization model (TDATV) for SHCT reconstruction. Specifically, the tensor decomposition works on nonlocal cube groups to exploit the anatomical structure redundancy; the anisotropic total variation works on the whole volume to exploit the structural piecewise-smooth. Finally, an alternating direction method of multipliers is developed to solve the TDATV model. To our knowledge, the paper presents the first work investigating the reconstruction of sparse-view helical CT. The TDATV model was validated through digital phantom, physical phantom, and clinical patient studies. The results reveal that SHCT could serve as a potential solution for reducing HCT radiation dose to ultra-low level by using the proposed TDATV model.

Keywords
  • Helical CT
  • Sparse-view
  • Tensor
  • Total variation
  • Iterative reconstruction
Funding
  • Swiss National Science Foundation - SNRF 320030_176052
Citation (ISO format)
WANG, Yongbo et al. Helical CT Reconstruction From Sparse-View Data Through Exploiting the 3D Anatomical Structure Sparsity. In: IEEE Access, 2021, vol. 9, p. 15200–15211. doi: 10.1109/ACCESS.2021.3049181
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Article (Published version)
Identifiers
ISSN of the journal2169-3536
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Technical informations

Creation01/28/2021 4:48:00 PM
First validation01/28/2021 4:48:00 PM
Update time03/16/2023 12:01:28 AM
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