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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | deu |
| Published: |
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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| Summary: | 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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| ISSN: | 1435-4934 |