MRI-Based Characterization of Left Ventricle Dyssynchrony with Correlation to CRT Outcomes

Cardiac resynchronization therapy (CRT) can improve cardiac function in 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 (LBBB) pattern) 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. We introduce a novel hybrid model-based and machine learning approach to characterize regional 3D cardiac motion in dyssynchrony from MRI, using deformable models and deep learning. 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, a neural network is used to detect and classify abnormalities of cardiovascular 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.