High-resolution CT image retrieval using sparse convolutional neural network

Yang Lei; Dong Xu; Zhengyang Zhou; Kristin Higgins; Xue Dong; Tian Liu; Hyunsuk Shim; Hui Mao; Walter J. Curran; Xiaofeng Yang

doi:10.1117/12.2292891

9 March 2018 High-resolution CT image retrieval using sparse convolutional neural network

Yang Lei, Dong Xu, Zhengyang Zhou, Kristin Higgins, Xue Dong, Tian Liu, Hyunsuk Shim, Hui Mao, Walter J. Curran, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 10573, Medical Imaging 2018: Physics of Medical Imaging; 105733F (2018) https://doi.org/10.1117/12.2292891
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

We propose a high-resolution CT image retrieval method based on sparse convolutional neural network. The proposed framework is used to train the end-to-end mapping from low-resolution to high-resolution images. The patch-wise feature of low-resolution CT is extracted and sparsely represented by a convolutional layer and a learned iterative shrinkage threshold framework, respectively. Restricted linear unit is utilized to non-linearly map the low-resolution sparse coefficients to the high-resolution ones. An adaptive high-resolution dictionary is applied to construct the informative signature which is highly connected to a high-resolution patch. Finally, we feed the signature to a convolutional layer to reconstruct the predicted high-resolution patches and average these overlapping patches to generate high-resolution CT. The loss function between reconstructed images and the corresponding ground truth highresolution images is applied to optimize the parameters of end-to-end neural network. The well-trained map is used to generate the high-resolution CT from a new low-resolution input. This technique was tested with brain and lung CT images and the image quality was assessed using the corresponding CT images. Peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and mean absolute error (MAE) indexes were used to quantify the differences between the generated high-resolution and corresponding ground truth CT images. The experimental results showed the proposed method could enhance images resolution from low-resolution images. The proposed method has great potential in improving radiation dose calculation and delivery accuracy and decreasing CT radiation exposure of patients.

Citation Download Citation

Yang Lei, Dong Xu, Zhengyang Zhou, Kristin Higgins, Xue Dong, Tian Liu, Hyunsuk Shim, Hui Mao, Walter J. Curran, and Xiaofeng Yang "High-resolution CT image retrieval using sparse convolutional neural network", Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105733F (9 March 2018); https://doi.org/10.1117/12.2292891

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