Differentiable Few-view CT-Reconstruction for Arbitrary CT-Trajectories including Prior Knowledge
Computed tomography (CT) is widely used in non-destructive testing (NDT), but the increasing flexibility of robot-based CT systems often results in more sparse and unevenly distributed projection data. This sparsity introduces significant challenges in reconstructing high-quality images. This paper...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | deu |
| Published: |
NDT.net
2025-02-01
|
| Series: | e-Journal of Nondestructive Testing |
| Online Access: | https://www.ndt.net/search/docs.php3?id=30724 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832086571536351232 |
|---|---|
| author | Linda-Sophie Schneider Adrian Waldyra Yipeng Sun Andreas K. Maier |
| author_facet | Linda-Sophie Schneider Adrian Waldyra Yipeng Sun Andreas K. Maier |
| author_sort | Linda-Sophie Schneider |
| collection | DOAJ |
| description |
Computed tomography (CT) is widely used in non-destructive testing (NDT), but the increasing flexibility of robot-based CT systems often results in more sparse and unevenly distributed projection data. This sparsity introduces significant challenges in reconstructing high-quality images. This paper presents a novel two-step pipeline for few-view CT reconstruction that combines discrete prior generation with differentiable optimization. First, the Discrete Algebraic Reconstruction Technique generates a binary volume that provides robust prior information about the object’s structure. This prior is then integrated into a fully differentiable reconstruction framework through two distinct strategies: gradient update cropping, which focuses optimization on regions identified by the prior, and prior-informed initialization, which uses the binary volume to create an informed starting point. Together, these approaches guide the iterative refinement of the reconstruction using known operator learning. Experiments on real-world datasets demonstrate the efficacy of the approach. Compared to conventional methods, the proposed framework achieves significant improvements in reconstruction quality. The results highlight the method’s ability to leverage sparse projection data, providing high-quality reconstructions even in challenging industrial scenarios.
|
| format | Article |
| id | doaj-art-559579504a6942269b722074322c7dfe |
| 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-559579504a6942269b722074322c7dfe2025-02-06T10:48:18ZdeuNDT.nete-Journal of Nondestructive Testing1435-49342025-02-0130210.58286/30724Differentiable Few-view CT-Reconstruction for Arbitrary CT-Trajectories including Prior KnowledgeLinda-Sophie SchneiderAdrian WaldyraYipeng SunAndreas K. Maier Computed tomography (CT) is widely used in non-destructive testing (NDT), but the increasing flexibility of robot-based CT systems often results in more sparse and unevenly distributed projection data. This sparsity introduces significant challenges in reconstructing high-quality images. This paper presents a novel two-step pipeline for few-view CT reconstruction that combines discrete prior generation with differentiable optimization. First, the Discrete Algebraic Reconstruction Technique generates a binary volume that provides robust prior information about the object’s structure. This prior is then integrated into a fully differentiable reconstruction framework through two distinct strategies: gradient update cropping, which focuses optimization on regions identified by the prior, and prior-informed initialization, which uses the binary volume to create an informed starting point. Together, these approaches guide the iterative refinement of the reconstruction using known operator learning. Experiments on real-world datasets demonstrate the efficacy of the approach. Compared to conventional methods, the proposed framework achieves significant improvements in reconstruction quality. The results highlight the method’s ability to leverage sparse projection data, providing high-quality reconstructions even in challenging industrial scenarios. https://www.ndt.net/search/docs.php3?id=30724 |
| spellingShingle | Linda-Sophie Schneider Adrian Waldyra Yipeng Sun Andreas K. Maier Differentiable Few-view CT-Reconstruction for Arbitrary CT-Trajectories including Prior Knowledge e-Journal of Nondestructive Testing |
| title | Differentiable Few-view CT-Reconstruction for Arbitrary CT-Trajectories including Prior Knowledge |
| title_full | Differentiable Few-view CT-Reconstruction for Arbitrary CT-Trajectories including Prior Knowledge |
| title_fullStr | Differentiable Few-view CT-Reconstruction for Arbitrary CT-Trajectories including Prior Knowledge |
| title_full_unstemmed | Differentiable Few-view CT-Reconstruction for Arbitrary CT-Trajectories including Prior Knowledge |
| title_short | Differentiable Few-view CT-Reconstruction for Arbitrary CT-Trajectories including Prior Knowledge |
| title_sort | differentiable few view ct reconstruction for arbitrary ct trajectories including prior knowledge |
| url | https://www.ndt.net/search/docs.php3?id=30724 |
| work_keys_str_mv | AT lindasophieschneider differentiablefewviewctreconstructionforarbitrarycttrajectoriesincludingpriorknowledge AT adrianwaldyra differentiablefewviewctreconstructionforarbitrarycttrajectoriesincludingpriorknowledge AT yipengsun differentiablefewviewctreconstructionforarbitrarycttrajectoriesincludingpriorknowledge AT andreaskmaier differentiablefewviewctreconstructionforarbitrarycttrajectoriesincludingpriorknowledge |