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Slice-wise reconstruction for low-dose cone-beam CT using a deep residual convolutional neural network

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Abstract

Because of the growing concern over the radiation dose delivered to patients, X-ray cone-beam CT (CBCT) imaging of low dose is of great interest. It is difficult for traditional reconstruction methods such as Feldkamp to reduce noise and keep resolution at low doses. A typical method to solve this problem is using optimization-based methods with careful modeling of physics and additional constraints. However, it is computationally expensive and very time-consuming to reach an optimal solution. Recently, some pioneering work applying deep neural networks had some success in characterizing and removing artifacts from a low-dose data set. In this study, we incorporate imaging physics for a cone-beam CT into a residual convolutional neural network and propose a new end-to-end deep learning-based method for slice-wise reconstruction. By transferring 3D projection to a 2D problem with a noise reduction property, we can not only obtain reconstructions of high image quality, but also lower the computational complexity. The proposed network is composed of three serially connected sub-networks: a cone-to-fan transformation sub-network, a 2D analytical inversion sub-network, and an image refinement sub-network. This provides a comprehensive solution for end-to-end reconstruction for CBCT. The advantages of our method are that the network can simplify a 3D reconstruction problem to a 2D slice-wise reconstruction problem and can complete reconstruction in an end-to-end manner with the system matrix integrated into the network design. Furthermore, reconstruction can be less computationally expensive and easily parallelizable compared with iterative reconstruction methods.

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Authors and Affiliations

  1. Department of Engineering Physics, Tsinghua University, Beijing, 100084, China

    Hong-Kai Yang, Kai-Chao Liang, Ke-Jun Kang & Yu-Xiang Xing

  2. Key Laboratory of Particle and Radiation Imaging, Ministry of Education, Tsinghua University, Beijing, 100084, China

    Hong-Kai Yang, Ke-Jun Kang & Yu-Xiang Xing

Authors
  1. Hong-Kai Yang
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  2. Kai-Chao Liang
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  3. Ke-Jun Kang
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  4. Yu-Xiang Xing
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Corresponding author

Correspondence to Yu-Xiang Xing.

Additional information

This work was supported by the National Natural Science Foundation of China (Nos. 61771279, 11435007) and the National Key Research and Development Program of China (No. 2016YFF0101304).

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Yang, HK., Liang, KC., Kang, KJ. et al. Slice-wise reconstruction for low-dose cone-beam CT using a deep residual convolutional neural network. NUCL SCI TECH 30, 59 (2019). https://doi.org/10.1007/s41365-019-0581-7

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  • DOI: https://doi.org/10.1007/s41365-019-0581-7

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