Why the study?
Does a deep learning framework improve the accuracy and speed of detecting late-activating regions of the left ventricle in heart failure patients compared to traditional algorithms?
Does a deep learning framework improve the accuracy and speed of detecting late-activating regions of the left ventricle in heart failure patients compared to traditional algorithms?
A novel deep learning framework enables fast and accurate automatic detection of late mechanical activation in heart failure patients from DENSE MR images, outperforming traditional methods.
This paper presents a novel method to automatically identify late-activating regions of the left ventricle from cine Displacement Encoding with Stimulated Echo (DENSE) MR images. We develop a deep learning framework that identifies late mechanical activation in heart failure patients by detecting the Time to the Onset of circumferential Shortening (TOS). In particular, we build a cascade network performing end-to-end (i) segmentation of the left ventricle to analyze cardiac function, (ii) prediction of TOS based on spatiotemporal circumferential strains computed from displacement maps, and (iii) 3D visualization of delayed activation maps. Our approach results in dramatic savings of manual labors and computational time over traditional optimization-based algorithms. To evaluate the effectiveness of our method, we run tests on cardiac images and compare with recent related works. Experimental results show that the proposed approach provides fast prediction of TOS with improved accuracy.
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Xing et al. (2021) studied Heart failure. Deep learning framework for detecting Time to the Onset of circumferential Shortening (TOS) vs. Traditional optimization-based algorithms was evaluated on Prediction of TOS and identification of late mechanical activation.
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