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

arXiv: https://arxiv.org/abs/2402.18507 · HTML: https://mlsanity.com/paper/2402.18507/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 28 Feb 2024.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image](https://mlsanity.com/paper/1908.07623/) (2019)
- [Deep Networks to Automatically Detect Late-activating Regions of the Heart](https://mlsanity.com/paper/2012.04000/) (2020)
- [Multitask Learning for Improved Late Mechanical Activation Detection of Heart from Cine DENSE MRI](https://mlsanity.com/paper/2211.06238/) (2022)

## Built on by (most cited first, 3 of 3)

- [TLRN: Temporal Latent Residual Networks For Large Deformation Image Registration](https://mlsanity.com/paper/2407.11219/) (2024)
- [LaMoD: Latent Motion Diffusion Model For Myocardial Strain Generation](https://mlsanity.com/paper/2407.02229/) (2024)
- [Learning to Unify Deformable Shape and Texture Representations for Cardiac Video Classification](https://mlsanity.com/paper/2607.07518/) (2026)

## Abstract (arXiv)

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.