Review of Deep Learning Methods Applied to Medical CT Super-Resolution

Image super resolution (SR) is one of the important processing methods to improve image resolution in the field of computer vision, which has important research significance and application value in the field of medical image. High quality and high-resolution medical CT images are very important in the current clinical process. In recent years, the technology of medical CT image super-resolution reconstruction based on deep learning has made remarkable progress. This paper reviews the representative methods in this field and systematically reviews the development of medical CT image super-resolution reconstruction technology. Firstly, the basic theory of SR is introduced, and the commonly used evaluation indexes are given. Then, it focuses on the innovation and progress of super-resolution reconstruction of medical CT images based on deep learning, and makes a comprehensive comparative analysis of the main characteristics and performance of each method. Finally, the difficulties and challenges in the direction of medical CT image super-resolution reconstruction are discussed, and the future development trend is summarized and prospected, hoping to provide reference for related research. © The Author(s) 2024.