Sparse-view Material Decomposition for Spectral X-ray CT using Neural Radiance Fields
Abstract
We extend our NeRF-based material decomposition method for spectral X-ray CT (SXCT) to eliminate the need for pre-defined LAC for materials and detector calibration. By introducing a learnable Attenuation coefficient dictionary and an Energy Transformer, the method jointly estimates spatial compositions of the materials and effective detector sensitivity with almost no prior information on materials and detectors. Experimental results on real SXCT data show that our approach achieves sparse-view material decomposition comparable to our previous method that requires extensive prior knowledge.
Fields of Study & Subjects
Advanced X-ray and CT Imaging
Nuclear Physics and Applications
Advanced X-ray Imaging Techniques
Publication details
- Journal / source
- e-Journal of Nondestructive Testing
- Volume
- 31
- Issue
- 3
- Publication date
- 9 February 2026
- Language
- EN