# MyoNet: Deep Learning-Based Myocardial Strain Quantification from Cine Cardiac MRI

## TL;DR

MyoNet is a deep learning tool that improves the accuracy of measuring heart muscle strain from MRI images, outperforming existing methods.

## Contribution

MyoNet introduces a novel deep learning network for myocardial strain quantification with superior performance over existing models.

## Key findings

- MyoNet outperformed ResMyoNet in myocardial strain measurement with high SSIM, ICC, and Pearson CC values.
- MyoNet demonstrated high consistency with SinMod-derived reference strains for both circumferential and radial strains.
- The model's accuracy and efficiency were validated through comprehensive statistical analyses.

## Abstract

To develop and assess MyoNet, a deep learning (DL)-based network for measuring myocardial regional function from cine cardiac magnetic resonance (CMR) images, and compare its efficacy with ResMyoNet as an efficient alternative to SinMod-derived reference. MyoNet was tested alongside ResMyoNet on datasets from Dahl salt-sensitive rat models undergoing radiation therapy (RT). Both networks were designed to extract displacement maps from cine images, were specifically optimized for detailed myocardial deformation, employed advanced convolution operations with alternating kernel sizes for spatial and temporal analysis, and robust loss functions. MyoNet demonstrated superior performance in myocardial strain measurement, achieving high consistency with the SinMod-derived reference strains. It outperformed ResMyoNet, achieving higher performance metrics, including SSIM of 0.961 and 0.960, ICC of 0.973 and 0.975, and Pearson CC of 0.973 and 0.953 for circumferential (Ecc) and radial (Err) strains, respectively. Its accuracy and efficiency in generating strain measurements were validated through comprehensive statistical analyses. MyoNet offers a significant advancement in myocardial strain analysis from cine CMR images, potentially revolutionizing cardiac imaging in pre-clinical studies. Its ability to provide detailed and reliable measurements positions it as a valuable tool for clinical applications, particularly in monitoring the cardiac health of cancer patients.