Dual network architecture for few-view CT - trained on ImageNet data and transferred for medical imaging

Huidong Xie; Hongming Shan; Wenxiang Cong; Xiaohua Zhang; Shaohua Liu; Ruola Ning; Ge Wang

doi:10.1117/12.2531198

10 September 2019 Dual network architecture for few-view CT - trained on ImageNet data and transferred for medical imaging

Huidong Xie, Hongming Shan, Wenxiang Cong, Xiaohua Zhang, Shaohua Liu, Ruola Ning, Ge Wang

Author Affiliations +

Proceedings Volume 11113, Developments in X-Ray Tomography XII; 111130V (2019) https://doi.org/10.1117/12.2531198
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

Abstract

X-ray computed tomography (CT) reconstructs cross-sectional images from projection data. However, ionizing X-ray radiation associated with CT scanning might induce cancer and genetic damage and raises public concerns. Therefore, the reduction of radiation dose has attracted major attention. Few-view CT image reconstruction is an important topic to reduce the radiation dose. Recently, data-driven algorithms have shown great potential to solve the few-view CT problem. In this paper, we develop a dual network architecture (DNA) for reconstructing images directly from sinograms. In the proposed DNA method, a point-wise fully-connected layer learns the backprojection process requesting significantly less memory than the prior art and with O(C×N×N_C) parameters where N and N_c denote the dimension of reconstructed images and number of projections respectively. C is an adjustable parameter that can be set as low as 1. Our experimental results demonstrate that DNA produces a competitive performance over the other state-of-the-art methods.Interestingly, natural images can be used to pre-train DNA to avoid overfitting when the amount of real patient images is limited.

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Citation Download Citation

Huidong Xie, Hongming Shan, Wenxiang Cong, Xiaohua Zhang, Shaohua Liu, Ruola Ning, and Ge Wang "Dual network architecture for few-view CT - trained on ImageNet data and transferred for medical imaging", Proc. SPIE 11113, Developments in X-Ray Tomography XII, 111130V (10 September 2019); https://doi.org/10.1117/12.2531198

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