Helical Artifact Reduction Method Using Image Segmentation With CNN Denoising Technique
Helical computed tomography (CT) scans are often performed to obtain three-dimensional images of an object that is longer than the detector. However, the existing quasi-exact and exact reconstruction methods, such as re-binning and Katsevich algorithm, generate interpolation errors or require high c...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10124941/ |
| _version_ | 1797820026447724544 |
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| author | Seungwon Choi Byeongjoon Kim Chulkyu Park Jueon Park Yousuk Kim Sungil Choi Jongduk Baek |
| author_facet | Seungwon Choi Byeongjoon Kim Chulkyu Park Jueon Park Yousuk Kim Sungil Choi Jongduk Baek |
| author_sort | Seungwon Choi |
| collection | DOAJ |
| description | Helical computed tomography (CT) scans are often performed to obtain three-dimensional images of an object that is longer than the detector. However, the existing quasi-exact and exact reconstruction methods, such as re-binning and Katsevich algorithm, generate interpolation errors or require high computational power. In this work, we propose a method to reconstruct helical CT projections by iteratively reducing helical artifacts. In each iteration, a convolutional neural network (CNN)-based denoising technique is used to accurately segment the prior image (bone and soft tissue image). The results indicate that the proposed algorithm reduces helical artifacts to a significantly greater extent than the existing single slice re-binning (SSR) and weighted filtered backprojection (W-FBP) methods. |
| first_indexed | 2024-03-13T09:31:19Z |
| format | Article |
| id | doaj.art-4bd94d6e522e4dc098c694ee879bc3bc |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T09:31:19Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4bd94d6e522e4dc098c694ee879bc3bc2023-05-25T23:00:41ZengIEEEIEEE Access2169-35362023-01-0111492614927210.1109/ACCESS.2023.327686410124941Helical Artifact Reduction Method Using Image Segmentation With CNN Denoising TechniqueSeungwon Choi0https://orcid.org/0009-0009-3003-5451Byeongjoon Kim1https://orcid.org/0000-0002-1970-576XChulkyu Park2Jueon Park3Yousuk Kim4https://orcid.org/0009-0005-1758-668XSungil Choi5Jongduk Baek6https://orcid.org/0000-0002-2532-5413School of Integrated Technology, Yonsei Institute of Convergence Technology, Yonsei University, Incheon, South KoreaDepartment of Artificial Intelligence, College of Computing, Yonsei University, Seoul, South KoreaResearch and Development Center, VATECH, Hwaseong-si, Gyeonggi-do, Republic of KoreaResearch and Development Center, VATECH, Hwaseong-si, Gyeonggi-do, Republic of KoreaResearch and Development Center, VATECH, Hwaseong-si, Gyeonggi-do, Republic of KoreaResearch and Development Center, VATECH, Hwaseong-si, Gyeonggi-do, Republic of KoreaDepartment of Artificial Intelligence, College of Computing, Yonsei University, Seoul, South KoreaHelical computed tomography (CT) scans are often performed to obtain three-dimensional images of an object that is longer than the detector. However, the existing quasi-exact and exact reconstruction methods, such as re-binning and Katsevich algorithm, generate interpolation errors or require high computational power. In this work, we propose a method to reconstruct helical CT projections by iteratively reducing helical artifacts. In each iteration, a convolutional neural network (CNN)-based denoising technique is used to accurately segment the prior image (bone and soft tissue image). The results indicate that the proposed algorithm reduces helical artifacts to a significantly greater extent than the existing single slice re-binning (SSR) and weighted filtered backprojection (W-FBP) methods.https://ieeexplore.ieee.org/document/10124941/Helical CThelical artifactfiltered backprojectionreconstruction |
| spellingShingle | Seungwon Choi Byeongjoon Kim Chulkyu Park Jueon Park Yousuk Kim Sungil Choi Jongduk Baek Helical Artifact Reduction Method Using Image Segmentation With CNN Denoising Technique IEEE Access Helical CT helical artifact filtered backprojection reconstruction |
| title | Helical Artifact Reduction Method Using Image Segmentation With CNN Denoising Technique |
| title_full | Helical Artifact Reduction Method Using Image Segmentation With CNN Denoising Technique |
| title_fullStr | Helical Artifact Reduction Method Using Image Segmentation With CNN Denoising Technique |
| title_full_unstemmed | Helical Artifact Reduction Method Using Image Segmentation With CNN Denoising Technique |
| title_short | Helical Artifact Reduction Method Using Image Segmentation With CNN Denoising Technique |
| title_sort | helical artifact reduction method using image segmentation with cnn denoising technique |
| topic | Helical CT helical artifact filtered backprojection reconstruction |
| url | https://ieeexplore.ieee.org/document/10124941/ |
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