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...
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9314041/ |
| _version_ | 1818727623804583936 |
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| author | Yongbo Wang Gaofeng Chen Tao Xi Zhaoying Bian Dong Zeng Habib Zaidi Ji He Jianhua Ma |
| author_facet | Yongbo Wang Gaofeng Chen Tao Xi Zhaoying Bian Dong Zeng Habib Zaidi Ji He Jianhua Ma |
| author_sort | Yongbo Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T22:17:03Z |
| format | Article |
| id | doaj.art-a185cf47645c41a4b23a801f95ea2297 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T22:17:03Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a185cf47645c41a4b23a801f95ea22972022-12-21T21:30:34ZengIEEEIEEE Access2169-35362021-01-019152001521110.1109/ACCESS.2021.30491819314041Helical CT Reconstruction From Sparse-View Data Through Exploiting the 3D Anatomical Structure SparsityYongbo Wang0Gaofeng Chen1Tao Xi2https://orcid.org/0000-0002-0944-9504Zhaoying Bian3Dong Zeng4https://orcid.org/0000-0001-6015-5010Habib Zaidi5https://orcid.org/0000-0001-7559-5297Ji He6https://orcid.org/0000-0001-9811-6500Jianhua Ma7https://orcid.org/0000-0003-2958-1710School of Biomedical Engineering, Southern Medical University, Guangzhou, ChinaSchool of Biomedical Engineering, Southern Medical University, Guangzhou, ChinaSchool of Biomedical Engineering, Southern Medical University, Guangzhou, ChinaSchool of Biomedical Engineering, Southern Medical University, Guangzhou, ChinaGuangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong, ChinaDivision of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, SwitzerlandSchool of Biomedical Engineering, Southern Medical University, Guangzhou, ChinaSchool of Biomedical Engineering, Southern Medical University, Guangzhou, ChinaSparse-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.https://ieeexplore.ieee.org/document/9314041/Helical CTsparse-viewtensortotal variationiterative reconstruction |
| spellingShingle | Yongbo Wang Gaofeng Chen Tao Xi Zhaoying Bian Dong Zeng Habib Zaidi Ji He Jianhua Ma Helical CT Reconstruction From Sparse-View Data Through Exploiting the 3D Anatomical Structure Sparsity IEEE Access Helical CT sparse-view tensor total variation iterative reconstruction |
| title | Helical CT Reconstruction From Sparse-View Data Through Exploiting the 3D Anatomical Structure Sparsity |
| title_full | Helical CT Reconstruction From Sparse-View Data Through Exploiting the 3D Anatomical Structure Sparsity |
| title_fullStr | Helical CT Reconstruction From Sparse-View Data Through Exploiting the 3D Anatomical Structure Sparsity |
| title_full_unstemmed | Helical CT Reconstruction From Sparse-View Data Through Exploiting the 3D Anatomical Structure Sparsity |
| title_short | Helical CT Reconstruction From Sparse-View Data Through Exploiting the 3D Anatomical Structure Sparsity |
| title_sort | helical ct reconstruction from sparse view data through exploiting the 3d anatomical structure sparsity |
| topic | Helical CT sparse-view tensor total variation iterative reconstruction |
| url | https://ieeexplore.ieee.org/document/9314041/ |
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