Super-Resolution Reconstruction of CT Images Using Neural Network Combined with Deconvolution
Xu Jun1,2), Liu Hui1,2)*, Guo Qiang1,2), and Zhang Caiming2,3, 4)
1) (School of Computer Science and Technology, Shandong University of Finance and Economics, Ji’nan 250014) 2) (Digital Media Technology Key Laboratory of Shandong Province, Ji’nan 250014)3) (School of Computer Science and Technology, Shandong University, Ji’nan 250100) 4) (Shandong Co-Innovation Center of Future Intelligent Computing, Yantai 264025)
The quality of medical images plays an important role in the diagnosis, treatment and even scientific research of patients’ diseases. However, due to the influence of medical equipment and radiation dose, the resolution of medical CT images is generally low. Therefore, this paper proposes a neural network algorithm combined with deconvolution for achieving super-resolution reconstruction of medical CT images. The proposed algorithm adds the operation of deconvolution, which effectively establishes the end-to-end mapping between low and high-resolution images. Firstly, 1 500 CT images of lung, brain, heart and spine are selected as training data, and they are down-sampled and then input into network model. Secondly, through the establishment of the convolution and deconvolution network model to learn image features, the network model is implemented using the caffe framework, and the activation function uses PReLU. Finally, the algorithm reconstructs high-resolution images using these features, where the image of reconstruction are averaged. The experimental results show that the proposed algorithm can reconstruct the contour and edge texture of the image better. Compared with the traditional super-resolution algorithm, the constructed four-layer network in this paper achieves better results in peak signal-to-noise ratio (PSNR), structural similarity (SSIM), information entropy (IE) and reconstruction speed.