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Domain Progressive 3D Residual Convolution Network to Improve Low-Dose CT Imaging

Abstract : The wide applications of X-ray computed tomography (CT) bring low-dose CT (LDCT) into a clinical prerequisite, but reducing the radiation exposure in CT often leads to significantly increased noise and artifacts, which might lower the judgment accuracy of radiologists. In this paper, we put forward a domain progressive 3D residual convolution network (DP-ResNet) for the LDCT imaging procedure that contains three stages sinogram domain network (SD-net), filtered back projection (FBP), and image domain network (ID-net). Though both are based on the residual network structure, the SD-net and ID-net provide complementary effect on improving the final LDCT quality. The experimental results with both simulated and real projection data show that this domain progressive deep-learning network achieves significantly improved performance by combing the network processing in the two domains.
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-02498047
Contributor : Laurent Jonchère <>
Submitted on : Wednesday, March 4, 2020 - 9:57:03 AM
Last modification on : Thursday, March 5, 2020 - 1:30:18 AM

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Xiangrui Yin, Qianlong Zhao, Jin Liu, Wei Yang, Jian Yang, et al.. Domain Progressive 3D Residual Convolution Network to Improve Low-Dose CT Imaging. IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2019, 38 (12), pp.2903-2913. ⟨10.1109/TMI.2019.2917258⟩. ⟨hal-02498047⟩

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