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Deep iterative reconstruction estimation (DIRE): approximate iterative reconstruction estimation for low dose CT imaging

Abstract : The image quality in low dose computed tomography (LDCT) can be severely degraded by amplified mottle noise and streak artifacts. Although the iterative reconstruction (IR) algorithms bring sound improvements, their high computation cost remains a major inconvenient. In this work, a deep iterative reconstruction estimation (DIRE) strategy is developed to estimate IR images from LDCT analytic reconstructions images. Within this DIRE strategy, a 3D residual convolutional network (3D ResNet) architecture is proposed. Experiments on several simulated and real datasets as well as comparisons with state-of-the-art methods demonstrate that the proposed approach is effective in providing improved LDCT images.
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-02378828
Contributor : Laurent Jonchère <>
Submitted on : Monday, November 25, 2019 - 1:43:44 PM
Last modification on : Monday, August 17, 2020 - 2:20:04 PM

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Jin Liu, Yi Zhang, Qianlong Zhao, Tianling Lv, Weiwen Wu, et al.. Deep iterative reconstruction estimation (DIRE): approximate iterative reconstruction estimation for low dose CT imaging. Physics in Medicine and Biology, IOP Publishing, 2019, 64 (13), pp.135007. ⟨10.1088/1361-6560/ab18db⟩. ⟨hal-02378828⟩

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