A representation-based method for continuous CT image reconstruction

In recent years, computed tomography (CT) imaging require high-resolution reconstructions that can reveal more details of patients. However, noise from X-ray and blurring effect caused by detector binning disrupts acquiring high-resolution images. These limitations can be addressed using image restoration techniques, and in particular, implicit neural representation-based techniques that allow flexible adjust output image resolution have been proposed in the field of low-level computer vision. In this work, we propose a method to remove noise and blurring artifacts while performing continuous CT image reconstruction with consuming less memory and time. Our method allows radiologists to modify the resolution of the selected region of interest (ROI) image in order to help the diagnosis of more detailed parts.