摘要
为了提高欠采样并行磁共振成像的图像重建质量,提出基于变分模型和Transformer的多尺度并行磁共振成像重建模型(VNTM).该模型利用欠采样多线圈k空间数据来估计灵敏度图,并利用中期增强策略以提高灵敏度图的准确性.将欠采样多线圈k空间数据和估计的灵敏度图输入变分模型进行重建,在变分模型中,通过前处理模块对图像数据进行降分辨率处理,以减少计算负担.通过具有Transformer的多尺度U型网络,实现多尺度特征的有效融合.使用后处理模块恢复分辨率,并对输出数据进行数据一致性操作以确保保真度.在公开数据集进行大量定量和定性实验以验证所提方法的有效性.结果表明,在峰值信噪比、结构相似度和视觉效果方面,所提出的重建模型均表现出更优的重建质量和更稳定的重建性能.多组消融实验和不同自校准信号(ACS)区域大小的鲁棒性实验,验证了VNTM在不同条件下均能保持良好的重建性能.
A multi-scale parallel MRI reconstruction model based on a variational model and Transformer(VNTM)was proposed,to enhance the quality of reconstructed MR images from undersampled multi-coil MR data.First,undersampled multi-coil k-space data were used to estimate sensitivity maps,with an intermediate-stage enhancement strategy applied to improve the accuracy of these maps.Next,the undersampled multi-coil k-space data and estimated sensitivity maps were input into a variational model for reconstruction.In the variational model,resolution was reduced through a pre-processing module to reduce computational load;multi-scale features were then effectively fused through a multi-scale U-shaped network with the Transformer.Finally,a post-processing module was applied to restore resolution,and data consistency operations were performed on the output to ensure fidelity.Extensive quantitative and qualitative experiments were conducted on publicly available datasets to validate the effectiveness of the proposed method.The experimental results indicate that the proposed reconstruction model achieves superior reconstruction quality and more stable performance in terms of peak signal-to-noise ratio,structural similarity,and visual effects.In addition,a series of ablation studies and robustness evaluations with varying auto-calibration signal(ACS)region sizes were carried out,confirming that VNTM maintained consistently high reconstruction performance under diverse conditions.
作者
段继忠
李海源
DUAN Jizhong;LI Haiyuan(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650504,China)
出处
《浙江大学学报(工学版)》
北大核心
2025年第9期1826-1837,共12页
Journal of Zhejiang University(Engineering Science)
基金
国家自然科学基金地区科学基金资助项目(61861023)
云南省基础研究计划资助项目(202301AT070452).
关键词
并行磁共振成像
深度学习
图像重建
灵敏度图
小波变换
parallel magnetic resonance imaging
deep learning
image reconstruction
sensitivity map
wavelet transforms
作者简介
段继忠(1984-),男,副教授,博士,从事图像处理和深度学习研究.orcid.org/0000-0002-5854-6239.E-mail:duanjz@kust.edu.cn。
