Cross-Domain Unpaired Learning for Low-Dose CT Imaging

Supervised deep-learning techniques with paired training datasets have been widely studied for low-dose computed tomography (LDCT) imaging with excellent performance. However, the paired training datasets are usually difficult to obtain in clinical routine, which restricts the wide adoption of super...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2023-11, Vol.27 (11), p.5471-5482
Main Authors: Liu, Yang, Chen, Gaofeng, Pang, Shumao, Zeng, Dong, Ding, Youde, Xie, Guoxi, Ma, Jianhua, He, Ji
Format: Article
Language:English
Subjects:
Biomedical imaging
Computed tomography
Cross domain
Data models
data-dependent model
Datasets
Deep learning
Image processing
Image reconstruction
Image restoration
Imaging
Learning
low-dose CT
Medical imaging
Modules
Training
unpaired learning
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Summary:Supervised deep-learning techniques with paired training datasets have been widely studied for low-dose computed tomography (LDCT) imaging with excellent performance. However, the paired training datasets are usually difficult to obtain in clinical routine, which restricts the wide adoption of supervised deep-learning techniques in clinical practices. To address this issue, a general idea is to construct a pseudo paired training dataset based on the widely available unpaired data, after which, supervised deep-learning techniques can be adopted for improving the LDCT imaging performance by training on the pseudo paired training dataset. However, due to the complexity of noise properties in CT imaging, the LDCT data are difficult to generate in order to construct the pseudo paired training dataset. In this article, we propose a simple yet effective cross-domain unpaired learning framework for pseudo LDCT data generation and LDCT image reconstruction, which is denoted as CrossDuL. Specifically, a dedicated pseudo LDCT sinogram generative module is constructed based on a data-dependent noise model in the sinogram domain, and then instead of in the sinogram domain, a pseudo paired dataset is constructed in the image domain to train an LDCT image restoration module. To validate the effectiveness of the proposed framework, clinical datasets are adopted. Experimental results demonstrate that the CrossDuL framework can obtain promising LDCT imaging performance in both quantitative and qualitative measurements.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2023.3312748