基于未增强T1 mapping的深度学习模型早期评价肥厚型心肌病心肌纤维化的初步研究

师轲; 李颖; 张天静; 李真林; 黎海霞; 彭婉琳; 夏春潮

doi:10.12182/20210960506

基于未增强T1 mapping的深度学习模型早期评价肥厚型心肌病心肌纤维化的初步研究

Early Assessment of Myocardial Fibrosis of Hypertrophic Cardiomyopathy with Native-T1-Mapping-Based Deep Learning: A Preliminary Study

摘要

摘要:
目的通过深度学习（deep learning, DL）模型分析心脏磁共振检查中不伴有心肌延迟强化（late gadolinium enhancement, LGE）的肥厚型心肌病（hypertrophic cardiomyopathy, HCM）未增强T1 mapping图像，探讨其早期识别心肌间质性纤维化的能力。

方法纳入接受心脏磁共振检查的60例HCM患者及44例正常对照者，以有无LGE判断并标记对应的未增强T1 mapping图像，将其与正常对照者的未增强T1 mapping图像经过预处理后作为矩阵形式输入SE-ResNext-50模型进行训练、验证及测试。

结果总共241幅未增强T1 mapping图像输入SE-ResNext-50模型，该模型识别测试集中LGE（−）未增强T1 mapping图像的特异性0.87，敏感性0.79，曲线下面积0.83（P<0.05）。

结论基于SE-ResNext-50的DL模型可较准确地识别LGE（−）未增强T1 mapping图像，可在不依赖对比剂的情况下早期发现HCM心肌纤维化。

Abstract:
Objective To explore the diagnostic performance of deep learning (DL) model in early detection of the interstitial myocardial fibrosis using native T1 maps of hypertrophic cardiomyopathy (HCM) without late gadolinium enhancement (LGE).
Methods Sixty HCM patients and 44 healthy volunteers who underwent cardiac magnetic resonance were enrolled in this study. Each native T1 map was labeled according to its LGE status. Then, native T1 maps of LGE (−) and those of the controls were preprocessed and entered in the SE-ResNext-50 model as the matrix for the DL model for training, validation and testing.
Results A total of 241 native T1 maps were entered in the SE-ResNext-50 model. The model achieved a specificity of 0.87, sensitivity of 0.79, and area under curve (AUC) of 0.83 (P<0.05) in distinguishing native T1 maps of LGE (−) from those of the controls in the testing set.
Conclusion The DL model based on SE-ResNext-50 could be used for identifying native T1 maps of LGE (−) with relatively high accuracy. It is a promising approach for early detection of myocardial fibrosis in HCM without the use of contrast agent.

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