快速狄克逊序列联合深度学习技术在臂丛神经无对比剂高分辨率磁共振成像中的价值

The value of Fast Dixon combined with deep learning technology in contrast agent-free high-resolution magnetic resonance imaging of the brachial plexus.

作者信息

Xie Si, Du Xuehui, Zhang Zhitao, Chen Guangxiang

机构信息

Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.

Siemens Healthineers, Shanghai, China.

出版信息

Front Neurol. 2025 Jun 4;16:1558927. doi: 10.3389/fneur.2025.1558927. eCollection 2025.

DOI:10.3389/fneur.2025.1558927

PMID:40567423

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12188371/

Abstract

INTRODUCTION

This study aimed to investigate the application of Fast Dixon combined with the deep resolve gain (DRG) technique for enhancing the image quality of the brachial plexus on high-resolution MRI without the use of contrast agents.

METHODS

Heavily T2-weighted Fast Dixon high-resolution coronal thin-slice magnetic resonance imaging was conducted on 19 social volunteers. Post-scan, the original data underwent reconstruction using deep learning-based denoising technology. Subjective quality scores were assigned to both the original and MIP images, and those processed with and without denoising technology were compared. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) values for each segment of the bilateral brachial plexus were measured and analyzed to assess image quality.

RESULTS

Subjective evaluations revealed that the quality of both original thin-slice and thin-MIP images processed with the DRG significantly outperformed those processed without the DRG (original thin-slice = 0.005, thin-MIP < 0.05). The bilateral SNRs and CNRs of each anatomical structure (root, trunk, cord, branch) of the brachial plexus in the experimental group with DRG were significantly greater than those in the control group without DRG ( < 0.05), as follows: the SNRs of the bilateral nerve roots increased by 35.1-36.2%, the SNRs of the bilateral nerve trunks increased by 40.6-40.8%, and the SNRs of the bilateral nerve cords and branches increased by about 40-45%. The CNR of the bilateral nerve roots increased by 43.1-44.6%, the CNR of the bilateral nerve trunks increased by 41.8-41.7%, and the CNR of the bilateral nerve cords and branches increased by 47.3-50.6% (root < 0.001, trunk < 0.001, cord = 0.001, branch = 0.011).

CONCLUSION

Fast Dixon T2WI can enhance the visibility of brachial plexus segments to a certain extent through DRG denoising technology, which may be an effective means to visualize the brachial plexus without contrast agent.

摘要

引言

本研究旨在探讨快速狄克逊序列结合深度解析增益（DRG）技术在不使用造影剂的情况下提高高分辨率磁共振成像（MRI）臂丛神经图像质量的应用。

方法

对19名社会志愿者进行重T2加权快速狄克逊高分辨率冠状位薄层磁共振成像。扫描后，使用基于深度学习的去噪技术对原始数据进行重建。对原始图像和最大强度投影（MIP）图像进行主观质量评分，并比较有无去噪技术处理后的图像。测量并分析双侧臂丛神经各节段的信噪比（SNR）和对比噪声比（CNR）值，以评估图像质量。

结果

主观评价显示，经DRG处理的原始薄层图像和薄层MIP图像的质量均显著优于未经DRG处理的图像（原始薄层图像P = 0.005，薄层MIP图像P < 0.05）。采用DRG的实验组臂丛神经各解剖结构（神经根、神经干、神经束、分支）的双侧SNR和CNR均显著高于未采用DRG的对照组（P < 0.05），具体如下：双侧神经根的SNR提高35.1% - 36.2%，双侧神经干的SNR提高40.6% - 40.8%，双侧神经束和分支的SNR提高约40% - 45%。双侧神经根的CNR提高43.1% - 44.6%，双侧神经干的CNR提高41.8% - 41.7%，双侧神经束和分支的CNR提高47.3% - 50.6%（神经根P < 0.001，神经干P < 0.001，神经束P = 0.001，分支P = 0.011）。