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

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ID: 39452
2019
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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.
Reference Key
liu2019deepphysics Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Liu, Jin;Zhang, Yi;Zhao, Qianlong;Lv, Tianling;Wu, Weiwen;Cai, Ning;Quan, Guotao;Yang, Wei;Chen, Yang;Luo, Limin;Shu, Huazhong;Coatrieux, Jean-Louis;
Journal Physics in medicine and biology
Year 2019
DOI
10.1088/1361-6560/ab18db
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