Evaluation of the sparse coding super-resolution method for improving image quality of up-sampled images in computed tomography

摘要

As the capability of high-resolution displays grows, high-resolution images are often required in Computed Tomography (CT). However, acquiring high-resolution images takes a higher radiation dose and a longer scanning time. In this study, we applied the Sparse-coding-based Super-Resolution (ScSR) method to generate high-resolution images without increasing the radiation dose. We prepared the over-complete dictionary learned the mapping between low- and high-resolution patches and seek a sparse representation of each patch of the low-resolution input. These coefficients were used to generate the high-resolution output. For evaluation, 44 CT cases were used as the test dataset. We up-sampled images up to 2 or 4 times and compared the image quality of the ScSR scheme and bilinear and bicubic interpolations, which are the traditional interpolation schemes. We also compared the image quality of three learning datasets. A total of 45 CT images, 91 non-medical images, and 93 chest radiographs were used for dictionary preparation respectively. The image quality was evaluated by measuring peak signal-to-noise ratio (PSNR) and structure similarity (SSIM). The differences of PSNRs and SSIMs between the ScSR method and interpolation methods were statistically significant. Visual assessment confirmed that the ScSR method generated a high-resolution image with sharpness, whereas conventional interpolation methods generated over-smoothed images. To compare three different training datasets, there were no significance between the CT, the CXR and non-medical datasets. These results suggest that the ScSR provides a robust approach for application of up-sampling CT images and yields substantial high image quality of extended images inCT.