  The selection of an optimal pacing site, which is ideally scar-free and late
activated, is critical to the response of cardiac resynchronization therapy
(CRT). Despite the success of current approaches formulating the detection of
such late mechanical activation (LMA) regions as a problem of activation time
regression, their accuracy remains unsatisfactory, particularly in cases where
myocardial scar exists. To address this issue, this paper introduces a
multi-task deep learning framework that simultaneously estimates LMA amount and
classify the scar-free LMA regions based on cine displacement encoding with
stimulated echoes (DENSE) magnetic resonance imaging (MRI). With a newly
introduced auxiliary LMA region classification sub-network, our proposed model
shows more robustness to the complex pattern cause by myocardial scar,
significantly eliminates their negative effects in LMA detection, and in turn
improves the performance of scar classification. To evaluate the effectiveness
of our method, we tests our model on real cardiac MR images and compare the
predicted LMA with the state-of-the-art approaches. It shows that our approach
achieves substantially increased accuracy. In addition, we employ the
gradient-weighted class activation mapping (Grad-CAM) to visualize the feature
maps learned by all methods. Experimental results suggest that our proposed
model better recognizes the LMA region pattern.
