Visual analysis of image features in CT reconstruction based on convolutional neural network

Linlin Zhu; Yu Han; Lei Li; Xiaoqi Xi; Bin Yan

doi:10.1117/12.2626905

2 March 2022 Visual analysis of image features in CT reconstruction based on convolutional neural network

Linlin Zhu, Yu Han, Lei Li, Xiaoqi Xi, Bin Yan

Proceedings Volume 12158, International Conference on Computer Vision and Pattern Analysis (ICCPA 2021); 1215806 (2022) https://doi.org/10.1117/12.2626905
Event: 2021 International Conference on Computer Vision and Pattern Analysis, 2021, Guangzhou, China

Abstract

Computed Tomography (CT) is one of the essential techniques for non-destructive testing. The acquisition of accurate reconstructed images is the basis for the subsequent analytical processing tasks. This paper proposes a convolutional neural network-based CT reconstruction algorithm to generate reconstructed CT images directly from sinogram by the feature coding and decoding capability. The reconstruction of abdominal scanning data is carried out by this method, and the results show that we can quickly obtain corresponding reconstruction results. During the network training, we designed different data pre-processing methods. We analysed the role of each module in the network by visualizing the output features of each module. Finally, the role of different modules in feature extraction and image generation is further analysed. We found that the conversion from projection to image can be effectively achieved using only convolution operations. It is essential for reconstructing CT images using deep learning techniques.

Citation Download Citation

Linlin Zhu, Yu Han, Lei Li, Xiaoqi Xi, and Bin Yan "Visual analysis of image features in CT reconstruction based on convolutional neural network", Proc. SPIE 12158, International Conference on Computer Vision and Pattern Analysis (ICCPA 2021), 1215806 (2 March 2022); https://doi.org/10.1117/12.2626905

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