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Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...

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Updated: Mar 29, 2026

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Published on: February 12, 2011

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

Dayeong An¹, Andrew Nencka², Patrick Clarysse³

¹Department of Radiology, Northwestern University, Chicago, IL 60611, USA.

Bioengineering (Basel, Switzerland)

|March 28, 2026

Summary

This summary is machine-generated.

MyoNet, a deep learning network, accurately measures heart function from cardiac MRI scans. This advanced tool offers precise myocardial strain analysis, improving cardiac imaging for pre-clinical and clinical applications.

Keywords:

CMR tagging cardiac dysfunction cardiac motion estimation cine cardiac MRI circumferential strain deep learning myocardial strain radial strain regional myocardial function

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High-frequency High-resolution Echocardiography: First Evidence on Non-invasive Repeated Measure of Myocardial Strain, Contractility, and Mitral Regurgitation in the Ischemia-reperfused Murine Heart

High-frequency High-resolution Echocardiography: First Evidence on Non-invasive Repeated Measure of Myocardial Strain, Contractility, and Mitral Regurgitation in the Ischemia-reperfused Murine Heart

Published on: July 9, 2010

Area of Science:

Cardiovascular Imaging
Artificial Intelligence in Medicine
Biomedical Engineering

Background:

Assessing myocardial regional function from cine cardiac magnetic resonance (CMR) images is crucial for diagnosing cardiac conditions.
Existing methods for myocardial strain analysis require efficient and accurate alternatives for pre-clinical and clinical applications.

Purpose of the Study:

To develop and evaluate MyoNet, a deep learning (DL) network for measuring myocardial regional function from cine CMR images.
To compare MyoNet's performance against ResMyoNet and SinMod-derived reference strains.

Main Methods:

MyoNet and ResMyoNet were developed using DL, employing advanced convolution operations for spatial and temporal analysis of cine CMR images.
Both networks were optimized for detailed myocardial deformation and utilized robust loss functions.
Performance was assessed on datasets from Dahl salt-sensitive rat models undergoing radiation therapy (RT).

Main Results:

MyoNet demonstrated superior performance in myocardial strain measurement, showing high consistency with SinMod-derived reference strains.
MyoNet achieved higher performance metrics than ResMyoNet, including SSIM (0.961/0.960), ICC (0.973/0.975), and Pearson CC (0.973/0.953) for circumferential (Ecc) and radial (Err) strains.
Statistical analyses validated MyoNet's accuracy and efficiency in generating strain measurements.

Conclusions:

MyoNet represents a significant advancement in myocardial strain analysis from cine CMR images.
Its accuracy, efficiency, and reliability position it as a valuable tool for pre-clinical studies and clinical applications, especially for monitoring cardiac health in cancer patients.