Deep learning-based computed tomographic image super-resolution via wavelet embedding.

Effort to realize high-resolution medical images have been made steadily. In particular, super resolution technology based on deep learning is making excellent achievement in computer vision recently. In this study, we developed a model that can dramatically increase the spatial resolution of medical images using deep learning technology, and we try to demonstrate the superiority of proposed model by analyzing it quantitatively. We simulated the computed tomography images with various detector pixel size and tried to restore the low-resolution image to high resolution image. We set the pixel size to 0.5, 0.8 and 1 mm for low resolution image and the high-resolution image, which were used for ground truth, was simulated with 0.25 mm pixel size. The deep learning model that we used was a fully convolution neural network based on residual structure. The result image demonstrated that proposed super resolution convolution neural network improve image resolution significantly. We also confirmed that PSNR and MTF was improved up to 38 % and 65% respectively. The quality of the prediction image is not significantly different depending on the quality of the input image. In addition, the proposed technique not only increases image resolution but also has some effect on noise reduction. In conclusion, we developed deep learning architectures for improving image resolution of computed tomography images. We quantitatively confirmed that the proposed technique effectively improves image resolution without distorting the anatomical structures.