Cooperative data fusion of transmission and surface scan for improving limited-angle computed tomography reconstruction
Limited-angle computed tomography allows faster inspection during production, but the reconstruction from limited-angle transmission data is an underdetermined problem which cannot be solved without any prior knowledge of the sample. In this paper, surface data from an optical scan is selected as pr...
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| Main Authors: | , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
26 May 2016
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| In: |
NDT & E international
Year: 2016, Volume: 83, Pages: 24-31 |
| ISSN: | 1879-1174 |
| DOI: | 10.1016/j.ndteint.2016.05.003 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.ndteint.2016.05.003 Verlag, lizenzpflichtig, Volltext: http://www.sciencedirect.com/science/article/pii/S0963869516300408 
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| Author Notes: | Yu Liu, Andreas Beyer, Philipp Schuetz, Juergen Hofmann, Alexander Flisch, Urs Sennhauser |
| Summary: | Limited-angle computed tomography allows faster inspection during production, but the reconstruction from limited-angle transmission data is an underdetermined problem which cannot be solved without any prior knowledge of the sample. In this paper, surface data from an optical scan is selected as prior information due to its high accuracy and availability. To incorporate this information, we have developed a new cooperative data fusion model in the compressed sensing framework. The model has been applied to numerical and experimental data and solved with a tailored algorithm. We demonstrate the benefit of the data fusion model and prove the robustness of the algorithm. The results from this study indicate that the data fusion process combines features resolved by both modalities and gives a significant increase in image quality. These improvements enable metrological measurements that are impossible with data acquired with any single modality. |
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| Item Description: | Gesehen am 27.05.2020 |
| Physical Description: | Online Resource |
| ISSN: | 1879-1174 |
| DOI: | 10.1016/j.ndteint.2016.05.003 |