Learning-Based Image Restorations of Sparse-View CT Data: Is It Reliable?
Learning-based methods for the restoration of computed tomography (CT) images promise very good image quality even in areas with insufficient data sampling and thus suggest enormous savings in measurement time. This work shows by means of restorations of sparse-view CT data that such methods must...
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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=30734 |
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| _version_ | 1832086610440617984 |
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| author | Philip Maurice Trapp Elias Eulig Joscha Maier Frederic Ballach Raoul Christoph Ralf Christoph Marc Kachelrieß |
| author_facet | Philip Maurice Trapp Elias Eulig Joscha Maier Frederic Ballach Raoul Christoph Ralf Christoph Marc Kachelrieß |
| author_sort | Philip Maurice Trapp |
| collection | DOAJ |
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Learning-based methods for the restoration of computed tomography (CT) images promise very good image quality even in areas with insufficient data sampling and thus suggest enormous savings in measurement time. This work shows by means of restorations of sparse-view CT data that such methods must be evaluated thoroughly and in a task-specific manner, as details of the workpiece may not be exactly reconstructed. In addition, this work examines the influence of these methods on metrological specification measurements of CTs and the conclusions that can be drawn with regard to the objective specification of such algorithms.
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| format | Article |
| id | doaj-art-f509bc88421643d1ab52fd2d0cc964c0 |
| 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-f509bc88421643d1ab52fd2d0cc964c02025-02-06T10:48:19ZdeuNDT.nete-Journal of Nondestructive Testing1435-49342025-02-0130210.58286/30734Learning-Based Image Restorations of Sparse-View CT Data: Is It Reliable?Philip Maurice TrappElias EuligJoscha MaierFrederic BallachRaoul ChristophRalf ChristophMarc Kachelrieß Learning-based methods for the restoration of computed tomography (CT) images promise very good image quality even in areas with insufficient data sampling and thus suggest enormous savings in measurement time. This work shows by means of restorations of sparse-view CT data that such methods must be evaluated thoroughly and in a task-specific manner, as details of the workpiece may not be exactly reconstructed. In addition, this work examines the influence of these methods on metrological specification measurements of CTs and the conclusions that can be drawn with regard to the objective specification of such algorithms. https://www.ndt.net/search/docs.php3?id=30734 |
| spellingShingle | Philip Maurice Trapp Elias Eulig Joscha Maier Frederic Ballach Raoul Christoph Ralf Christoph Marc Kachelrieß Learning-Based Image Restorations of Sparse-View CT Data: Is It Reliable? e-Journal of Nondestructive Testing |
| title | Learning-Based Image Restorations of Sparse-View CT Data: Is It Reliable? |
| title_full | Learning-Based Image Restorations of Sparse-View CT Data: Is It Reliable? |
| title_fullStr | Learning-Based Image Restorations of Sparse-View CT Data: Is It Reliable? |
| title_full_unstemmed | Learning-Based Image Restorations of Sparse-View CT Data: Is It Reliable? |
| title_short | Learning-Based Image Restorations of Sparse-View CT Data: Is It Reliable? |
| title_sort | learning based image restorations of sparse view ct data is it reliable |
| url | https://www.ndt.net/search/docs.php3?id=30734 |
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