Mri-Based Characterization of Left Ventricle Dyssynchrony with Correlation to Crt Outcomes

Cardiac resynchronization therapy (CRT) can improve cardiac functions of some patients with heart failure (HF) and dyssynchrony. However, as many as half of patients selected for CRT by conventional criteria (HF and ECG QRS broadening greater than 150 ms, preferably with left bundle branch block) do not benefit from it. LBBB leads to characteristic motion changes seen with echocardiography and magnetic resonance imaging (MRI). Attempts to use echocardiography to quantitatively characterize dyssynchrony have failed to improve prediction of response to CRT. In this paper, we introduce a novel hybrid approach using deformable model and deep learning to characterize regional 3D cardiac motion in dyssynchrony from MRI. First, 3D left ventricle (LV) models of the moving heart are constructed from multiple planes of cine MRI. Using the conventional 17-segment model (AHA), we capture the regional 3D motion of each segment of the LV wall. Then, abnormalities of cardiovascular regional motions can be further detected and categorized via analyzing the regional motions. Using over 100 patient data, we show that different types of dyssynchrony can be accurately demonstrated in 3D + t space and their correlation to CRT response.