Optimization of Analytical Reconstruction Algorithms for Arbitrary CBCT Trajectory Using Deep Learning
This study addresses the challenge of applying analytical methods for Cone Beam Computed Tomography (CBCT) reconstructions along arbitrary trajectories instead of iterative methods. Traditional analytical methods like Filtered Back Projection (FBP) often fail to adequately process CBCT images due t...
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| Format: | Article |
| Language: | deu |
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NDT.net
2025-02-01
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| Series: | e-Journal of Nondestructive Testing |
| Online Access: | https://www.ndt.net/search/docs.php3?id=30749 |
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| author | Yuzhong Zhou Linda-Sophie Schneider Yipeng Sun Andreas K. Maier |
| author_facet | Yuzhong Zhou Linda-Sophie Schneider Yipeng Sun Andreas K. Maier |
| author_sort | Yuzhong Zhou |
| collection | DOAJ |
| description |
This study addresses the challenge of applying analytical methods for Cone Beam Computed Tomography (CBCT) reconstructions along arbitrary trajectories instead of iterative methods. Traditional analytical methods like Filtered Back Projection (FBP) often fail to adequately process CBCT images due to the intricate and varied paths involved. Although iterative methods are a common solution, they require substantial computational time and resources. To address these challenges, this paper proposes two approaches, the first approach enhances the traditional FBP algorithm by using deep learning to train an optimized filter before reconstruction. The second approach improves upon the Backprojection then Filtering (BPF) algorithm by first reconstructing and then applying a deep learning-trained filter. Both methods significantly optimize the initial reconstruction results and enhance efficiency, offering promising improvements over existing iterative reconstruction techniques.
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| format | Article |
| id | doaj-art-f95161adb5734dfc8dded8da298ea26d |
| institution | Kabale University |
| issn | 1435-4934 |
| language | deu |
| publishDate | 2025-02-01 |
| publisher | NDT.net |
| record_format | Article |
| series | e-Journal of Nondestructive Testing |
| spelling | doaj-art-f95161adb5734dfc8dded8da298ea26d2025-02-06T10:48:19ZdeuNDT.nete-Journal of Nondestructive Testing1435-49342025-02-0130210.58286/30749Optimization of Analytical Reconstruction Algorithms for Arbitrary CBCT Trajectory Using Deep LearningYuzhong ZhouLinda-Sophie SchneiderYipeng SunAndreas K. Maier This study addresses the challenge of applying analytical methods for Cone Beam Computed Tomography (CBCT) reconstructions along arbitrary trajectories instead of iterative methods. Traditional analytical methods like Filtered Back Projection (FBP) often fail to adequately process CBCT images due to the intricate and varied paths involved. Although iterative methods are a common solution, they require substantial computational time and resources. To address these challenges, this paper proposes two approaches, the first approach enhances the traditional FBP algorithm by using deep learning to train an optimized filter before reconstruction. The second approach improves upon the Backprojection then Filtering (BPF) algorithm by first reconstructing and then applying a deep learning-trained filter. Both methods significantly optimize the initial reconstruction results and enhance efficiency, offering promising improvements over existing iterative reconstruction techniques. https://www.ndt.net/search/docs.php3?id=30749 |
| spellingShingle | Yuzhong Zhou Linda-Sophie Schneider Yipeng Sun Andreas K. Maier Optimization of Analytical Reconstruction Algorithms for Arbitrary CBCT Trajectory Using Deep Learning e-Journal of Nondestructive Testing |
| title | Optimization of Analytical Reconstruction Algorithms for Arbitrary CBCT Trajectory Using Deep Learning |
| title_full | Optimization of Analytical Reconstruction Algorithms for Arbitrary CBCT Trajectory Using Deep Learning |
| title_fullStr | Optimization of Analytical Reconstruction Algorithms for Arbitrary CBCT Trajectory Using Deep Learning |
| title_full_unstemmed | Optimization of Analytical Reconstruction Algorithms for Arbitrary CBCT Trajectory Using Deep Learning |
| title_short | Optimization of Analytical Reconstruction Algorithms for Arbitrary CBCT Trajectory Using Deep Learning |
| title_sort | optimization of analytical reconstruction algorithms for arbitrary cbct trajectory using deep learning |
| url | https://www.ndt.net/search/docs.php3?id=30749 |
| work_keys_str_mv | AT yuzhongzhou optimizationofanalyticalreconstructionalgorithmsforarbitrarycbcttrajectoryusingdeeplearning AT lindasophieschneider optimizationofanalyticalreconstructionalgorithmsforarbitrarycbcttrajectoryusingdeeplearning AT yipengsun optimizationofanalyticalreconstructionalgorithmsforarbitrarycbcttrajectoryusingdeeplearning AT andreaskmaier optimizationofanalyticalreconstructionalgorithmsforarbitrarycbcttrajectoryusingdeeplearning |