Work

NeMCoF: Neural Material Composition Fields for Material Decomposition in Sparse-View Spectral X-ray CT

Year: 2025

Type: article

Abstract: Spectral X-ray computed tomography enables material decomposition by leveraging energy-dependent X-ray attenuation properties. However, material decomposition with spectral CT requires a longer acquisition time to obtain sufficient numbers of photons in each energy bin. Sparse-view offers a practical solution to reduce acquisition time, but it introduces ill-posedness, degrading decomposition accuracy. This study introduces a material decomposition framework based on Neural Radiance Fields where material maps are represented using a multilayer perceptron (MLP). The material maps are then optimized through a spectral forward projection process based on the Lambert–Beer’s law, while a partition of unity (PoU) loss ensures the physical constraint on material maps. Our method was evaluated using simulated and real spectral CT datasets and compared with a traditional statistical approach. The results demonstrated that our method performs well in material decomposition under sparse-view conditions. The results suggest that our “neural material composition fields” framework offers accurate material decomposition robust to sparse-view conditions without requiring labeled training data.

Source: Journal of Nondestructive Evaluation

DOI: 10.1007/s10921-025-01263-0

Authors: Takumi Hotta, Tatsuya Yatagawa, Yutaka Ohtake, Toru Aoki

Institutions: The University of Tokyo, Hitotsubashi University, Shizuoka University

Language: English

Topic: Advanced X-ray and CT Imaging

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,290

Funder: University of Tokyo