Deep neural networks for low-dose CT image reconstruction via cooperative meta-learning strategy

Manman Zhu; Sui Li; Danyang Li; Qi Gao; Shanli Zhang; Haiyun Huang; Zhaoying Bian; Jing Huang; Dong Zeng; Jianhua Ma

doi:10.1117/12.2548950

16 March 2020 Deep neural networks for low-dose CT image reconstruction via cooperative meta-learning strategy

Manman Zhu, Sui Li, Danyang Li, Qi Gao, Shanli Zhang, Haiyun Huang, Zhaoying Bian, Jing Huang, Dong Zeng, Jianhua Ma

Proceedings Volume 11312, Medical Imaging 2020: Physics of Medical Imaging; 1131243 (2020) https://doi.org/10.1117/12.2548950
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Recently, deep neural networks (DNNs) have been widely applied in low-dose computed tomography (LDCT) imaging field. Their performances are highly related to the number of the pre-collected training data. Meanwhile, the training data is usually hard to obtain, especially for the high-dose CT (HDCT) images. And HDCT images sometimes contain undesired noises, which easily result in network overfitting. To address the two issues, we proposed a cooperative meta-learning strategy for CT image reconstruction (CmetaCT) combining the metalearning strategy and Co-teaching strategy. The meta-learning (teacher/student model) strategy allows for training network with a large number of LDCT images without the corresponding HDCT images and only a small number of labeled CT data in a semi-supervised learning manner. And the Co-teaching strategy is able to make a trade-off between overfitting and introducing extra errors, which includes a part of samples in every minibatch for updating model parameters. Due to the capacity of meta-learning, the presented CmetaCT method is flexible enough to utilize any existing CT restoration/reconstruction network in meta-learning framework. Finally, both quantitative and visual results indicated that the proposed CmetaCT method achieves a superior performance on low-dose CT imaging compared with the DnCNN method.

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Manman Zhu, Sui Li, Danyang Li, Qi Gao, Shanli Zhang, Haiyun Huang, Zhaoying Bian, Jing Huang, Dong Zeng, and Jianhua Ma "Deep neural networks for low-dose CT image reconstruction via cooperative meta-learning strategy", Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 1131243 (16 March 2020); https://doi.org/10.1117/12.2548950

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KEYWORDS

Computed tomography

Data modeling

X-ray computed tomography

CT reconstruction

Neural networks

Image quality

Image processing

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