基于自监督深度学习的心肌磁共振T1量化成像运动校正算法研究  

Self-Supervised Deep Learning Method for Myocardial MRI T1 Mapping Motion Correction

作　　者：吴春燕 李雨泽 丁海艳[1] 陈慧军[1] WU Chunyan;LI Yuze;DING Haiyan;CHEN Huijun(Department of Biomedical Engineering,School of Medicine,Tsinghua University,Beijing 100084,China)

机构地区：[1]清华大学医学院生物医学工程系,北京100084

出　　处：《中国医疗设备》2022年第3期13-17,共5页China Medical Devices

基　　金：国家重点研发计划(2017YFC0108702)。

摘　　要：目的为了去除运动的影响并得到准确的心肌磁共振T1参数成像,本研究提出一种基于自监督深度学习的运动校正算法。方法该算法采用卷积神经网络结构来估计运动场,通过空间变换层将运动场作用到待配准图像上得到运动校正后的图像,并利用图像相似度结合运动场平滑约束的损失函数来进行网络优化。结果本研究在47例健康志愿者的心脏磁共振图像上进行训练和测试,并与一种传统配准方法进行比较。结果显示,本方法的Dice相似系数、心肌边界误差和心肌T1量化值的误差分别为0.79,0.925 mm和59.22 ms,均优于未配准图像和传统方法。结论本文提出的方法为心脏疾病的临床诊断提供了一个高效准确的自动化工具,有望大规模临床使用。Objective To eliminate the influence of motion and obtain accurate myocardial T1 mapping,this research proposes a motion correction algorithm based on self-supervised deep learning.Methods In this algorithm,a convolutional neural network was employed to estimate motion field and a spatial transform layer was used to obtain the motion corrected image by applying the motion field on the moving image.The loss function including the image similarity combined with the smoothness of the motion field was used to optimize the network.Results In this study,cardiac MRI of 47 healthy volunteers were trained and tested and compared with a traditional registration method.The results showed that the Dice similarity coefficient,myocardial boundary error and myocardial T1 quantization error were 0.79,0.925 mm and 59.22 ms,respectively,which were better than the unregistered images and traditional method.Conclusion The proposed method provides an efficient and accurate tool for clinical diagnosis of heart disease,which holds a potential for wide clinical application.

关 键 词：磁共振成像 T1参数量化成像 运动校正 自监督深度学习 

分 类 号：R445.2[医药卫生—影像医学与核医学]