A material decomposition method for dual-energy CT via dual interactive Wasserstein generative adversarial networks
arXiv:2007.11247 · doi:10.1002/mp.14828
Abstract
Dual-energy computed tomography has great potential in material characterization and identification, whereas the reconstructed material-specific images always suffer from magnified noise and beam hardening artifacts. In this study, a data-driven approach using dual interactive Wasserstein generative adversarial networks is proposed to improve the material decomposition accuracy. Specifically, two interactive generators are used to synthesize the corresponding material images and different loss functions for training the decomposition model are incorporated to preserve texture and edges in the generated images. Besides, a selector is employed to ensure the modelling ability of two generators. The results from both the simulation phantoms and real data demonstrate the advantages of this method in suppressing the noise and beam hardening artifacts.
arXiv comment40 pages, 10 figures, research article
References in corpus (6)
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
- Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising
- An algorithm for constrained one-step inversion of spectral CT data
- Image Domain Dual Material Decomposition for Dual-Energy CT using Butterfly Network
- DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering