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...
Saved in:
Main Authors: | Takumi Hotta, Tatsuya Yatagawa, Yutaka Ohtake, Toru Aoki |
---|---|
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=30727 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Learning-Based Image Restorations of Sparse-View CT Data: Is It Reliable?
by: Philip Maurice Trapp, et al.
Published: (2025-02-01) -
Compensating Sparse-view Inline Computed Tomography Artifacts with Neural Representation and Incremental Forward-Backward Network Architecture
by: Manuel Buchfink, et al.
Published: (2025-02-01) -
Noise filtering method in images in sparse-view covers
by: Y.V. Goshin, et al.
Published: (2024-06-01) -
Proximal Neural Networks based reconstruction for few-view CT applications
by: Hoang Trieu Vy Le, et al.
Published: (2025-02-01) -
Simulating X-ray beam energy and detector signal processing of an industrial CT using implicit neural representations
by: Edwin Blum, et al.
Published: (2025-02-01)