基于深度学习的1.5T心脏磁共振Cine序列在肥厚型心肌病和扩张型心肌病患者左心室功能评估中的应用  

Application of 1.5T cardiac Cine MR image based on deep learning in left ventricular function evaluation of patients with hypertrophic cardiomyopathy and dilated cardiomyopathy

作　　者：郭家鋆 鲁鸿飞 佘佳琦 吴东 曾蒙苏 金航 GUO Jia-jun;LU Hong-fei;She Jia-qi;WU Dong;ZENG Meng-su;JIN Hang(Shanghai Institute of Medical Imaging,Shanghai 200032,China;Department of Radiology,Zhongshan Hospital,Fudan University,Shanghai 200032,China)

机构地区：[1]上海市影像医学研究所,上海200032 [2]复旦大学附属中山医院放射科,上海200032

出　　处：《中国临床医学》2021年第4期675-681,共7页Chinese Journal of Clinical Medicine

基　　金：上海市卫生健康委员会课题(202040349).

摘　　要：目的:探讨一种基于深度学习的1.5T心脏磁共振Cine序列自动量化不同心肌病左心室功能的性能。方法:回顾性分析2014年3月至2019年11月393例心脏MRI受检者的相关临床资料。对肥厚型心肌病(HCM)患者(HCM组,n=125)、扩张型心肌病(DCM)患者(DCM组,n=133)和健康个体(对照组,n=135)的左心室功能,分别通过手动和自动测量进行评估。手动分析由2位经验丰富的医师完成;自动分析后,从左心室分割精度和左心室功能参数准确性两方面对卷积神经网络(CNN)的性能进行评价。采用Pearson相关分析、Bland-Altman分析和受试者工作特征曲线(ROC),评价手动与自动方法诊断HCM和DCM的相关性与一致性。结果:CNN评估左心室功能时,在HCM组中与手动分析的一致性最好,对照组次之,在DCM中表现最差。HCM组左心室功能4个参数的自动分析与手动分析结果具有较高的相关性(P<0.01);DCM组所有参数自动与手动分析的相关性均弱于HCM,特别是射血分数和每搏输出量。ROC曲线分析表明,自动分割算出的射血分数对DCM、HCM的诊断灵敏度分别为92.31%和78.05%,特异度分别为82.96%和54.07%。结论:在不同心肌疾病中,基于CNN的心功能分析性能可能不同,在HCM中表现优于DCM,但对DCM的诊断价值优于HCM。Objective:To evaluate the performance of a deep learning 1.5T cardiac Cine MR images in automatically quantification of the left ventricular(LV)function of patients with different cardiomyopathy.Methods:The cardiac MRI data of 393 subjects from March 2014 to November 2019 were retrospectively analyzed.The LV function of patients with hypertrophic cardiomyopathy(HCM group,n=125),patients with dilated cardiomyopathy(DCM group,n=133),and normal individuals(control group,n=135)was evaluated by manual and automatic measurements.The manual analysis was completed by two experienced physicians.After automatic measurement,the performance of convolutional neural network(CNN)was evaluated from aspects of left ventricular segmentation accuracy and LV functional parameter accuracy.Pearson correlation analysis,Bland-Altman analysis,and receiver operator curve(ROC)were used to evaluate the correlation and consistency between automatic and manual diagnosis for HCM and DCM.Results:When deep learning CNN was used to evaluate the LV function,the consistency with manual measurement was best in the HCM group,followed by the normal group,and the worst in the DCM group.The results of automatic and manual analysis of the four parameters of LV function in the HCM group had higher correlations(P<0.01),and the correlations in the DCM group were weaker than those in the HCM group,especially ejection fraction and stroke volume.ROC curve analysis showed that the ejection fraction calculated by automatic segmentation has a certain diagnostic efficiency for DCM and HCM,with a sensitivity of 92.31%and 78.05%,and a specificity of 82.96%and 54.07%,respectively.Conclusions:Among different myocardial lesions,deep learning CNN-based cardiac function analysis may have different performances,which is worse in DCM and better in HCM,but has a greater diagnosis value for DCM than HCM.

关 键 词：心血管磁共振 卷积神经网络 扩张型心肌病 肥厚型心肌病 人工智能 深度学习 

分 类 号：R542.2[医药卫生—心血管疾病]