9 March 2018 High-resolution CT image retrieval using sparse convolutional neural network
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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.
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Yang Lei, Dong Xu, Zhengyang Zhou, Kristin Higgins, Xue Dong, Tian Liu, Hyunsuk Shim, Hui Mao, Walter J. Curran, 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); doi: 10.1117/12.2292891; https://doi.org/10.1117/12.2292891
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