Adaptive semi-supervised learning material estimation network in dual-energy CT

Zheng Duan; Jingyi Liao; Yongbo Wang; Danyang Li; Zhaoying Bian; Dong Zeng; Jianhua Ma

doi:10.1117/12.2654207

7 April 2023 Adaptive semi-supervised learning material estimation network in dual-energy CT

Zheng Duan, Jingyi Liao, Yongbo Wang, Danyang Li, Zhaoying Bian, Dong Zeng, Jianhua Ma

Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging; 124633F (2023) https://doi.org/10.1117/12.2654207
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Dual Energy CT (DECT) has ability to characterize different materials and quantify the densities or proportions of different contrast agents. However, the basis images decomposition is an ill-posed problem and the traditional model-based and image-domain direct inversion methods always suffer from serious degradation of the signal-to-noise ratios (SNRs). To this issue, we propose a new strategy by combining model-based and learning-based methods, which suppresses noise in the material images after direct inverse, and design a semi-supervised framework, Adaptive Semi-supervised Learning Material Estimation Network (ASLME-Net), to balance the detail structure preservation and noise suppression when fed little paired data in training stage of the deep learning. Specifically, the ASLME-Net contains two sub-networks, i.e., supervise sub-network and unsupervised sub-network. The supervised sub-network aims at capturing key features learned by with the labeled data, and the unsupervised sub-network adaptively learns the transferred feature distribution from supervised sub-network with Kullback-Leibler (KL) divergence. Experiment shows that the presented method can suppress the noise propagation in decomposition and yield qualitatively and quantitatively accurate results during the process of material decomposition. To this issue, we propose a new strategy by combining model-based and learning-based methods, which suppresses noise in the material images after direct inverse, and design a semi-supervised framework, Adaptive Semi-supervised Learning Material Estimation Network (ASLME-Net), to balance the detail structure preservation and noise suppression when fed little paired data in training stage of the deep learning.

Citation Download Citation

Zheng Duan, Jingyi Liao, Yongbo Wang, Danyang Li, Zhaoying Bian, Dong Zeng, and Jianhua Ma "Adaptive semi-supervised learning material estimation network in dual-energy CT", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 124633F (7 April 2023); https://doi.org/10.1117/12.2654207

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