Cardiac disease discrimination from 3d-convolutional kinematic patterns on cine-MRI sequences

Abstract

Introduction. Cine-MRI sequences are a key diagnostic tool for observing anatomical information, allowing experts to localize and determine suspicious pathologies. Nonetheless, such analysis remains subjective and prone to diagnosis errors.
Objective. To develop a binary and multi-class classification considering various cardiac conditions through a spatio-temporal model that highlights kinematic movements to characterize each disease.
Materials and methods. This research focuses on using a 3D convolutional representation to characterize cardiac kinematic patterns, during the cardiac cycle, which may be associated with pathologies. The kinematic maps are obtained from the apparent velocity maps computed from a dense optical flow strategy. Then, a 3D convolutional scheme learns to differentiate pathologies from kinematic maps.
Results. The proposed strategy was validated with respect to the capability to discriminate among myocardial infarction, dilated cardiomyopathy, hypertrophic cardiomyopathy, abnormal right ventricle, and normal cardiac sequences. The proposed method achieves 78.00% average accuracy and 75.55% average F1-score, respectively. Likewise, the approach achieved 92.31% of accuracy for binary classification between pathologies and control cases.
Conclusion. The proposed method is able to support the identification of kinematic abnormal patterns, associated with a pathological condition. The resultant descriptor, learned from the 3D convolutional net, preserves detailed spatio-temporal correlations and could emerge as possible digital biomarkers of cardiac diseases.