基于多尺度T1w 图像注意力机制引导的定量磁化率重建

doi:10.12122/j.issn.1673-4254.2026.09.23

南方医科大学学报 ›› 2026, Vol. 46 ›› Issue (9): 2258-2265.doi: 10.12122/j.issn.1673-4254.2026.09.23

• • 上一篇

基于多尺度T1w 图像注意力机制引导的定量磁化率重建

李飘然¹^,², 陶泉¹^,², 欧阳富盛³, 郭保亮³, 胡秋根³, 冯衍秋¹^,²^,³()

^1.南方医科大学，生物医学工程学院，广东广州 510515
^2.南方医科大学，图像处理广东省重点实验室与医学成像诊断技术广东省工程实验室，广东广州 510515
^3.南方医科大学第八附属医院（顺德第一人民医院）放射科，广东佛山 528308

收稿日期:2026-02-25 出版日期:2026-09-20 发布日期:2026-09-30
通讯作者: 冯衍秋 E-mail:foree@163.com
基金资助:
国家自然科学基金(82372079);广东省自然科学基金(2024A1515010014);广东省自然科学基金(2023a1515110614┫Supported by National Natural Science Foundation of China ┣82372079);作者简介：李飘然，在读硕士研究生,E-mail: 2446601298@qq.com

Quantitative susceptibility reconstruction guided by a multi-scale T1-weighted image attention mechanism

Piaoran LI¹^,², Quan TAO¹^,², Fusheng OYANG³, Baoliang GUO³, Qiugen HU³, Yanqiu FENG¹^,²^,³()

^1.School of Biomedical Engineering, Eighth Affiliated Hospital of Southern Medical University (First People's Hospital of Shunde), Foshan 528308, China
^2.Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Provincial Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China, Eighth Affiliated Hospital of Southern Medical University (First People's Hospital of Shunde), Foshan 528308, China
^3.Department of Radiology, Eighth Affiliated Hospital of Southern Medical University (First People's Hospital of Shunde), Foshan 528308, China

Received:2026-02-25 Online:2026-09-20 Published:2026-09-30
Contact: Yanqiu FENG E-mail:foree@163.com

摘要/Abstract

摘要：

目的通过多尺度T1w 图像注意力引导的深度学习网络，提高定量磁化率成像（QSM）的精度与结构一致性。方法基于T1w图像的结构特性，本方法通过注意力模块引导网络关注组织边界和关键区域，以提高QSM成像中单方向重建的精度与结构保真度。实验将T1w-ADQSM、K空间截断法（TKD）、形态学约束偶极反演法（MEDI）和深度学习方法QSMnet进行比较，并使用定量参数高频误差范数（HFEN）、结构相似性指数（SSIM）、归一化均方根误差（NRMSE）和峰值信噪比（PSNR）进行图像质量的评估。结果在健康志愿者中，T1w-ADQSM重建图像与TKD、MEDI、和QSMnet相比，峰值信噪比最高（43.12±1.19）、归一化均方根误差最小（51.98±3.65）。此外，T1w-ADQSM在4项定量指标上均优于QSMnet，且该差异具有统计学意义（P<0.05）。基于多方向数据构建的COSMOS参考图的补充验证表明，T1w-ADQSM较QSMnet具有更好的结构一致性，其重建结果更接近COSMOS参考图。结论本文提出的基与多尺度T1w图像注意力驱动的深度学习网络重建方法可以提高定量磁化率重建的精度和结构一致性。

关键词: 定量磁化率成像, 图像重建, 深度学习

Abstract:

Objective To improve the accuracy and structural consistency of quantitative susceptibility mapping (QSM) reconstruction through a deep learning network guided by multi-scale T1-weighted image attention. Methods A multi-scale T1-weighted attention-driven deep QSM network (T1w-ADQSM) was proposed to enhance the precision and structural fidelity of single-orientation QSM reconstruction. The method incorporates structural features from T1-weighted images and employs an attention module to guide the network to focus on the anatomical boundaries and key regions. Experimental comparisons were conducted among T1w-ADQSM,truncated k-space division (TKD) method, morphology enabled dipole inversion (MEDI) and QSMnet. Quantitative evaluations were performed using the metrics including high-frequency error norm (HFEN), structural similarity index measure (SSIM),normalized root mean square error (NRMSE), and peak signal-to-noise ratio (PSNR). Results In healthy volunteers, T1w-ADQSM achieved the highest PSNR (43.12±1.19) and the lowest NRMSE (51.98±3.65) compared with TKD, MEDI, and QSMnet. T1w-ADQSM outperformed QSMnet across all the 4 quantitative metrics with statistically significant differences (P<0.05). Further validation based on the COSMOS reference reconstructed from multi-orientation data showed that T1w-ADQSM exhibited better structural consistency than QSMnet, and the reconstruction results were closer to the COSMOS reference. Conclusion The proposed multi-scale T1-weighted image attention-driven deep learning reconstruction method improves the accuracy and structural consistency of QSM.

Key words: quantitative susceptibility mapping, image reconstruction, deep learning

李飘然, 陶泉, 欧阳富盛, 郭保亮, 胡秋根, 冯衍秋. 基于多尺度T1w 图像注意力机制引导的定量磁化率重建[J]. 南方医科大学学报, 2026, 46(9): 2258-2265.

Piaoran LI, Quan TAO, Fusheng OYANG, Baoliang GUO, Qiugen HU, Yanqiu FENG. Quantitative susceptibility reconstruction guided by a multi-scale T1-weighted image attention mechanism[J]. Journal of Southern Medical University, 2026, 46(9): 2258-2265.

图/表 7

图1 本文提出的T1w-ADQSM方法概述

Fig.1 Overview of the proposed T1w-ADQSM method.

图2 注意力机制模块AG示意图(A)和多尺度T1w图像生成示意图(B)

Fig.2 Generation of the attention gate (A) and multi-scale T1-weighted images (B).

表1 不同T1w数据输入的T1w-ADQSM的定量性能指标和标准差

Tab.1 Means and standard deviation of the quantitative performance metrics for T1w-ADQSM with different T1w data types

Types	HFEN (%)	NRMSE (%)	PSNR	SSIM
T1w_norm	59.43±3.56	58.34±4.28	41.56±0.83	0.932±0.033
T1w_edge	58.21±1.67	59.31±3.43	39.82±1.06	0.928±0.024
T1w_fused	56.69±2.96	57.76±3.05	42.06±0.94	0.936±0.011

图3 使用基于非深度学习的偶极子反卷积方法(TKD和MEDI)和深度学习方法(QSMnet和T1w-ADQSM)的一例志愿者的单方向定量磁化率结果比较

Fig.3 Comparison of single-orientation QSM results from one volunteer using non-deep learning dipole deconvolution methods (TKD and MEDI) and deep learning-based methods (QSMnet and T1w-ADQSM). The yellow arrows in the figure indicate prominent differences between QSMnet and T1w-ADQSM.

图4 志愿者的定量磁化率结果(第1行)和对应的差异图(第2行)的轴向视图

Fig.4 Axial view of the QSM map (first row) and the corresponding difference maps (second row) of the subject in Fig.3. The last two rows show zoomed-in views. The red arrows indicate regions with obvious differences between QSMnet and T1w-ADQSM.

表2 4种重建方法的定量性能指标的均值和标准差，包括HFEN、NRMSE、PSNR和SSIM

Tab.2 Means and standard deviation of the quantitative performance metrics

Methods	HFEN (%)	NRMSE (%)	PSNR	SSIM
MEDI	62.39±4.69	79.36±7.03	37.16±0.88	0.952±0.016
TKD	59.23±4.18	69.49±5.30	39.25±0.97	0.914±0.023
QSMnet	57.47±3.28	58.89±3.14	41.39±1.05	0.931±0.008
T1w-ADQSM	56.69±2.96	57.76±3.05	42.06±0.94	0.936±0.011

图5 单方向QSM重建结果(QSMnet和T1w-ADQSM)与对应的COSMOS结果对比图

Fig.5 Comparison of single-orientation QSM reconstructions (QSMnet and T1w-ADQSM) with the corresponding COSMOS results. In terms of reconstructed structural fidelity, T1w-ADQSM outperforms the QSMnet method (red arrows).

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基于多尺度T1w 图像注意力机制引导的定量磁化率重建

Quantitative susceptibility reconstruction guided by a multi-scale T1-weighted image attention mechanism

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