摘要
目的:探讨基于深度学习重建(deep learning reconstruction,DLR)技术的快速自旋回波(turbo spin echo,TSE)T2加权成像(T2 weighted imaging,T2WI)序列提高纵隔T2WI图像质量和减少扫描时间的应用价值。方法:前瞻性收集35例纵隔病变的患者进行纵隔常规T2WI序列和基于DLR的T2WI(T2WIDLR)序列扫描。由2位放射科医师各自独立对两组图像的伪影、图像清晰度、病灶的细节显示和整体图像质量进行定性评分,并测量和比较两组图像的病灶信噪比(SNR_(病灶))和病灶与竖直肌的对比噪声比(CNR_(病灶/肌肉))。结果:T2WIDLR在伪影、图像清晰度、病灶细节显示和整体图像质量的评分均优于常规T2WI(P均<0.001)。T2WIDLR序列图像的SNR和CNR均优于常规T2WI序列(P均<0.001)。结论:与常规T2WI序列相比,T2WI_(DLR)序列的运动伪影更少,图像清晰度、病灶细节显示和整体图像质量更优,图像SNR和CNR更高,在纵隔疾病的临床应用中有较大潜力。
Objective:To explore the application value of the turbo spin echo(TSE)T2-weighted imaging based on deep learning reconstruction(DLR)technology in improving mediastinum image quality and reducing scanning time.Methods:35 patients with mediastinal lesions were prospectively collected for mediastinal conventional T2WI and DLR-based T2WI scanning.The artifacts,image clarity,display of the detail of the lesion and overall image quality of the two groups were qualitatively scored by two radiologists independently.The signal-to-noise ratio of the lesion(SNR_(lesion))and the contrast to noise ratio of the lesion to vertical muscle(CNR_(lesion/muscle))of the two groups of images were measured and compared.Results:Qualitative analysis:T2WIDLR was superior to conventional T2WI in the scores of artifact,image sharpness,display of the detail of the lesion,and overall image quality(P<0.001).Quantitative analysis:The SNR and CNR of the image of T2WI_(DLR) sequence were better than those of conventional T2WI(P<0.001).Conclusion:Compared with conventional T2WI sequences,T2WI_(DLR) sequence has fewer motion artifacts,better image clarity,lesion detail display and overall image quality,and higher SNR and CNR,which has great potential in clinical application of mediastinal diseases.
作者
武鹏飞
徐海
李泓渊
沈杰
王建伟
WU Pengfei;XU Hai;LI Hongyuan;SHEN Jie;WANG Jianwei(Department of Radiology,the First Affiliated Hospital of Nanjing Medical University,Nanjing 210029,China)
出处
《南京医科大学学报(自然科学版)》
CAS
北大核心
2024年第6期797-801,共5页
Journal of Nanjing Medical University(Natural Sciences)
基金
江苏省卫生健康委科研项目(K2023057)。
关键词
纵隔
深度学习
磁共振成像
快速自旋回波
mediastinum
deep learning
magnetic resonance imaging
fast spin echo