Energy-Flexible Network (EF-Net) for Dual-Energy CT Image Reconstruction

In dual-energy computed tomography (DECT), the X-ray tube energy pair often changes depending on the target organ or patient obesity. In practice, it makes difficult to apply deep learning (DL) based algorithms for image reconstruction since most of the existing DL-based algorithms are trained to be...

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Bibliographic Details
Published in:arXiv.org 2023-03
Main Authors: Lee, Donghyeon, Jeong, Uijin, Yun, Sungho, Hwang, Joonil, Cho, Seungryong
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Computed tomography
Domains
Image reconstruction
Iterative algorithms
Iterative methods
Machine learning
Medical imaging
X ray tubes
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Summary:In dual-energy computed tomography (DECT), the X-ray tube energy pair often changes depending on the target organ or patient obesity. In practice, it makes difficult to apply deep learning (DL) based algorithms for image reconstruction since most of the existing DL-based algorithms are trained to be used for dedicated X-ray tube energies. In this paper, we propose 1) an energy flexibility training (EFT) method, which makes a network applicable for data measured at various X-ray tube energies between two trained energies, and 2) an effective dual-domain convolutional neural network for image reconstruction. The proposed network is derived from the regularized version of the primal-dual hybrid gradient algorithm, so its architecture has an unfolded iterative dual-domain structure. For validation, we generated datasets from a lab-made polychromatic X-ray simulator. The proposed method showed promising results not only at the trained X-ray tube energies but also at untrained X-ray tube energies outperforming an iterative algorithm and other DL-based algorithms.
ISSN:2331-8422