Sparse-view Material Decomposition for Spectral X-ray CT using Neural Radiance Fields
Photon-counting X-ray detectors, in contrast to conventional flat panel detectors, have the capability of distinguishing between photons with different energies, and have been leveraged for material decomposition tasks for materials with similar X-ray attenuation properties. However, as the energy...
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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=30727 |
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| _version_ | 1832086606986608640 |
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| author | Takumi Hotta Tatsuya Yatagawa Yutaka Ohtake Toru Aoki |
| author_facet | Takumi Hotta Tatsuya Yatagawa Yutaka Ohtake Toru Aoki |
| author_sort | Takumi Hotta |
| collection | DOAJ |
| description |
Photon-counting X-ray detectors, in contrast to conventional flat panel detectors, have the capability of distinguishing between photons with different energies, and have been leveraged for material decomposition tasks for materials with similar X-ray attenuation properties. However, as the energy resolution increases, it may suffer from a lack of photons falling into each energy bin, resulting in an inadequate material decomposition. In this study, we demonstrate the effectiveness of recent neural radiance fields (NeRF) for material decomposition tasks using spectral X-ray CT in sparse-view reconstruction scenarios. Particularly, our method exploits the known linear attenuation properties of base materials and reconstructs the fractions of base materials comprising the target object.
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| format | Article |
| id | doaj-art-ee0d8c63631446df95345c97f4a6bb3f |
| 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-ee0d8c63631446df95345c97f4a6bb3f2025-02-06T10:48:19ZdeuNDT.nete-Journal of Nondestructive Testing1435-49342025-02-0130210.58286/30727Sparse-view Material Decomposition for Spectral X-ray CT using Neural Radiance FieldsTakumi HottaTatsuya Yatagawahttps://orcid.org/0000-0003-4653-2435Yutaka Ohtakehttps://orcid.org/0000-0002-1368-9172Toru Aoki Photon-counting X-ray detectors, in contrast to conventional flat panel detectors, have the capability of distinguishing between photons with different energies, and have been leveraged for material decomposition tasks for materials with similar X-ray attenuation properties. However, as the energy resolution increases, it may suffer from a lack of photons falling into each energy bin, resulting in an inadequate material decomposition. In this study, we demonstrate the effectiveness of recent neural radiance fields (NeRF) for material decomposition tasks using spectral X-ray CT in sparse-view reconstruction scenarios. Particularly, our method exploits the known linear attenuation properties of base materials and reconstructs the fractions of base materials comprising the target object. https://www.ndt.net/search/docs.php3?id=30727 |
| spellingShingle | Takumi Hotta Tatsuya Yatagawa Yutaka Ohtake Toru Aoki Sparse-view Material Decomposition for Spectral X-ray CT using Neural Radiance Fields e-Journal of Nondestructive Testing |
| title | Sparse-view Material Decomposition for Spectral X-ray CT using Neural Radiance Fields |
| title_full | Sparse-view Material Decomposition for Spectral X-ray CT using Neural Radiance Fields |
| title_fullStr | Sparse-view Material Decomposition for Spectral X-ray CT using Neural Radiance Fields |
| title_full_unstemmed | Sparse-view Material Decomposition for Spectral X-ray CT using Neural Radiance Fields |
| title_short | Sparse-view Material Decomposition for Spectral X-ray CT using Neural Radiance Fields |
| title_sort | sparse view material decomposition for spectral x ray ct using neural radiance fields |
| url | https://www.ndt.net/search/docs.php3?id=30727 |
| work_keys_str_mv | AT takumihotta sparseviewmaterialdecompositionforspectralxrayctusingneuralradiancefields AT tatsuyayatagawa sparseviewmaterialdecompositionforspectralxrayctusingneuralradiancefields AT yutakaohtake sparseviewmaterialdecompositionforspectralxrayctusingneuralradiancefields AT toruaoki sparseviewmaterialdecompositionforspectralxrayctusingneuralradiancefields |