医学CT影像超分辨率深度学习方法综述  

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

作　　者：田苗苗 支力佳[1] 张少敏[1] 晁代福 TIAN Miaomiao;ZHI Lijia;ZHANG Shaomin;CHAO Daifu(School of Computer Science and Engineering,North Minzu University,Yinchuan 750021,China)

机构地区：[1]北方民族大学计算机科学与工程学院,银川750021

出　　处：《计算机工程与应用》2024年第3期44-60,共17页Computer Engineering and Applications

基　　金：宁夏自然科学基金(2021AAC03198);国家自然科学基金(61561002,61962002);宁夏医学影像临床研究中心创新平台建设项目(2018DPG05006)。

摘　　要：图像超分辨率(SR)是计算机视觉领域提高图像分辨率的重要处理方法之一,在医学图像领域有重要的研究意义和应用价值。高质量和高分辨率的医学CT影像在当前的临床过程中非常重要。近年来,基于深度学习的医学CT影像超分辨率重建技术取得了显著的进展,对该领域内的代表性方法进行了梳理,系统回顾了医学CT影像超分辨率重建技术的发展。介绍了SR基本理论,给出常用的评价指标;重点阐述基于深度学习的医学CT影像超分辨率重建方向的创新与进展,对各个方法的主要特点和性能进行了综合比较分析。最后,讨论了医学CT影像超分辨率重建方向上存在的困难和挑战,并对未来的发展趋势进行了总结与展望,希望能为相关研究提供参考。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.

关 键 词：超分辨率 医学CT影像 深度学习 计算机视觉 神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]