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Deep residual neural network based image enhancement algorithm for low dose CT images

  • 1210: Computer Vision for Clinical Images
  • Published:
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Abstract

Current deep learning based image enhancement algorithms attempt to learn the mapping relationship between degraded images and clear images directly. These algorithms often ignore the fidelity constraint of the observational model. In order to improve the image enhancement performance, an improved deep residual neural network based image enhancement algorithm (DRNN-IE) for low dose CT images is proposed in this paper. DRNN-IE embeds the image enhancement task into a deep neural network, and achieves data consistency using multiple enhancement modules and back-projection modules. The enhancement modules in DRNN-IE produce new features through fusing low-level and high-level features. In order to improve the algorithm’s generalization ability, a dual-parameter loss function is adopted to train and optimize the neural network. Experiments on real CT images show that the proposed algorithm has excellent enhancement performance and retains detailed information of low-dose CT images.

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Acknowledgements

This work was supported in part by the Jiangsu Committee of Health under Grant H2018071, Changshu Committee of Health under Grant csws201820, National Natural Science Foundation of China under Grants 61806026, and Natural Science Foundation of Jiangsu Province under Grant BK 20180956.

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Authors and Affiliations

  1. Affiliated Changshu Hospital of Soochow University (Changshu No.1 people’s Hospital), Changshu, 215500, Jiangsu, China

    Kaijian Xia & Bo Chen

  2. Jiangsu Key Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China

    Kaijian Xia & Yizhang Jiang

  3. Department of Informatics, University of Leicester, Leicester, LE1 7RH, UK

    Qinghua Zhou

  4. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, Jiangsu, China

    Yizhang Jiang

  5. School of Information Science and Engineering, Changzhou University, Changzhou, 213164, Jiangsu, China

    Xiaoqing Gu

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  1. Kaijian Xia
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  2. Qinghua Zhou
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  3. Yizhang Jiang
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  4. Bo Chen
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  5. Xiaoqing Gu
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Correspondence to Xiaoqing Gu.

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Xia, K., Zhou, Q., Jiang, Y. et al. Deep residual neural network based image enhancement algorithm for low dose CT images. Multimed Tools Appl 81, 36007–36030 (2022). https://doi.org/10.1007/s11042-021-11024-6

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