生物医学工程研究2025，Vol.44Issue(6)：363-370,8.DOI:10.19529/j.cnki.1672-6278.2025.06.03
胎儿脑部核磁共振图像的自适应核回归去噪方法
Adaptive kernel regression method for fetal brain magnetic resonance imaging denoising
摘要
Abstract
For Rician noise existed in magnetic resonance imaging(MRI)of fetal brain,we designed a kernel regression denoising method.Firstly,classical kernel regression(CKR)was used to acquire gradient information.Then,the covariance matrix representing the local characteristics of the MRI image was constructed by the gradient information and the adaptive kernel regression(AKR)to a-chieve adaptive denoising of MRI data.The quantitative analysis results of MRI data of 9 sets of fetal brain MRI data and 12 sets of a-dult brain MRI data with different Rician noise levels showed that the AKR denoising algorithm could reduce the root mean square error(RMSE)by approximately 28.64%～57.57%,the peak signal-to-noise ratio(PSNR)was increased by approximately 11.67%～45.50%,and the structural similarity index measure(SSIM)was increased by approximately 7.95%～72.50%.The qualitative analysis results of the simulation data and the real MRI of fetal brain indicate that this algorithm can effectively remove the noise in MRI data of fetal and adult brain,and can maintain the global features of the images.关键词
胎儿脑部/核磁共振/自适应核回归/图像去噪Key words
Fetal brain/Magnetic resonance imaging/Adaptive kernel regression/Image denoising分类
医药卫生引用本文复制引用
倪倩,刘犇,韩远峰,余权桂,陈雄德,温铁祥..胎儿脑部核磁共振图像的自适应核回归去噪方法[J].生物医学工程研究,2025,44(6):363-370,8.基金项目
国家自然科学基金项目(61401451) （61401451）
深圳市科技计划项目(JCYJ20220530153408019) （JCYJ20220530153408019）
深圳市基础研究重点项目(JCYJ20200109114812361). （JCYJ20200109114812361）
