生物医学工程研究2025，Vol.44Issue(3)：162-169,8.DOI:10.19529/j.cnki.1672-6278.2025.03.04
基于注意力残差UNet和加速非均值滤波的低场弥散加权成像Ghost伪影综合处理方法
Comprehensive processing method for Ghost artifacts in low-field diffusion-weighted imaging based on attention residual UNet and accelerated non-mean filtering
摘要
Abstract
To address the N/2 Ghost artifacts caused by phase encoding errors in diffusion-weighted imaging(DWI)for low-field(<1 T)magnetic resonance imaging(MRI)systems,we proposed an approach integrating deep learning and optimized filtering to e-liminate Ghost artifacts and enhance image quality.Firstly,an AR-UNet model incorporating dense residual connection and attention gate mechanism was developed to achieve precise segmentation of craniocerebral anatomical structures through feature reuse and dynam-ic weight allocation.Then,an edge-constrained accelerated non-local means filtering(SCNLM)was used to improve the computation-al efficiency of the model.The results showed that the average Dice similarity coefficient,accuracy rate and specificity of the model reached 0.932 1,0.943 6 and 0.943 0,respectively.SCNLM could increase the computational efficiency of the traditional NLM algo-rithm by approximately 50%,while maintaining the peak signal-to-noise ratio of 29.50 dB and the structural similarity of 0.88.This research can effectively suppress Ghost artifacts in low-field MRI systems and significantly enhances image quality.关键词
低场磁共振/弥散加权成像/Ghost伪影/注意力残差UNet/加速非局部均值滤波Key words
Low-field MRI/Diffusion-weighted imaging/Ghost artifacts/Attention residual UNet/Accelerated non-local means filtering分类
医药卫生引用本文复制引用
徐扬,韦静,Kim Siseung,Zhang Huiyao,Li Bingkeong..基于注意力残差UNet和加速非均值滤波的低场弥散加权成像Ghost伪影综合处理方法[J].生物医学工程研究,2025,44(3):162-169,8.基金项目
江苏省重点研发计划(社会发展)专项基金项目(BE2021682). （社会发展）
