计算机科学与探索2025，Vol.19Issue(11)：2895-2912,18.DOI:10.3778/j.issn.1673-9418.2501023
胎儿脑核磁共振图像处理技术进展
Progress in Fetal Brain Magnetic Resonance Image Processing Technologies
摘要
Abstract
Fetal brain MRI,due to its non-invasiveness,absence of radiation,and high soft-tissue contrast,has become an important tool for assessing fetal brain development and diagnosing congenital brain abnormalities.High-quality fetal brain MR images play an important role in clinical diagnosis,treatment,and scientific research of fetal brain develop-ment.Image processing techniques can enhance the quality of fetal brain MR images,meeting the requirements for diag-nosis and research.Thus,the studies in this field hold significant importance.This paper provides a brief introduction to fetal brain structure and its MR image datasets,and elaborates on six techniques,including image quality assessment,image registration,image denoising,image bias field correction,image artifact correction,and super-resolution reconstruction.Firstly,the importance of image processing technologies for fetal brain MR images is presented.Subsequently,the structure of the fetal brain and its MR image datasets are introduced.Then the six image processing techniques are introduced respectively.The research status both at home and abroad is systematically described.The performance of different methods is compared and analyzed.And the current achievements and challenges are summarized respectively.Finally,the existing issues and future research directions in the field of fetal brain MR image processing are discussed from the perspectives of technology and clinical application.关键词
胎儿脑/核磁共振图像/图像处理/深度学习Key words
fetal brain/magnetic resonance image/image processing/deep learning分类
计算机与自动化引用本文复制引用
刘梦宇,罗琴,姚雄,王健华,陈健..胎儿脑核磁共振图像处理技术进展[J].计算机科学与探索,2025,19(11):2895-2912,18.基金项目
福建省自然科学基金(2022J01952,2023J01953) （2022J01952,2023J01953）
2024年教育部产学合作协同育人项目(XY202401007). This work was supported by the Natural Science Foundation of Fujian Province(2022J01952,2023J01953),and the Industry-University Collaborative Education Program of Ministry of Education of China in 2024(XY202401007). （XY202401007）
