Multimodal Learning To Improve Cardiac Late Mechanical Activation Detection From Cine MR Images

• URL: http://arxiv.org/abs/2402.18507v1
• Date: Wed, 28 Feb 2024 17:34:58 GMT
• Title: Multimodal Learning To Improve Cardiac Late Mechanical Activation Detection From Cine MR Images
• Authors: Jiarui Xing, Nian Wu, Kenneth Bilchick, Frederick Epstein, Miaomiao Zhang
• Abstract summary: This paper presents a multimodal deep learning framework to improve the performance of clinical analysis heavily dependent on routinely acquired standard images. We develop a joint learning network that for the first time leverages the accuracy and accuracy of myocardial strains obtained from Displacement with Stimulated Echo (DENSE) to guide the analysis of cine cardiac magnetic resonance (CMR) imaging in late mechanical activation (LMA) detection.
• Score: 3.9111646862781826
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a multimodal deep learning framework that utilizes advanced image techniques to improve the performance of clinical analysis heavily dependent on routinely acquired standard images. More specifically, we develop a joint learning network that for the first time leverages the accuracy and reproducibility of myocardial strains obtained from Displacement Encoding with Stimulated Echo (DENSE) to guide the analysis of cine cardiac magnetic resonance (CMR) imaging in late mechanical activation (LMA) detection. An image registration network is utilized to acquire the knowledge of cardiac motions, an important feature estimator of strain values, from standard cine CMRs. Our framework consists of two major components: (i) a DENSE-supervised strain network leveraging latent motion features learned from a registration network to predict myocardial strains; and (ii) a LMA network taking advantage of the predicted strain for effective LMA detection. Experimental results show that our proposed work substantially improves the performance of strain analysis and LMA detection from cine CMR images, aligning more closely with the achievements of DENSE.

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