A Relative Motion Model for Myocardial Infarction Detection

The kinematic and morphological abnormalities can be used for accurate detection of myocardial infarction without contrast agents. It has important implications for the early treatment of patients. However, methods based on motion tracking are time-consuming, and the complex movements of the heart make them difficult to implement. In this paper, we propose a deep learning constrained framework based on relative motion features. It can detect myocardial infarction areas through cine cardiac magnetic resonance imaging(CMRI) images. It includes one relative motion extraction component and one deep neural network component. In the relative motion model, a U-Net model is used to segment the myocardial contour. After that, the motion features and pixel features of the myocardium are extracted and fused. Finally, the extracted relative features are further learned via the deep neural network model based on ConvLSTM to predict the myocardial infarction area. Our method doesn't need a pre-find position match and is more suitable for the physiological process of the myocardium. We validated the performance of our framework in 276 cine CMRI sequences datasets, and it yielded a high consistency with manual delineation (90.8% detection accuracy). The results demonstrate that our proposed method can be an attractive tool for the diagnosis of myocardial infarction in the clinic.