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Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Published on: February 13, 2011

Accelerated deep learning-based function assessment in cardiovascular magnetic resonance.

Domenico De Santis¹, Federica Fanelli¹, Luca Pugliese¹

¹Department of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy.

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Summary

This summary is machine-generated.

Deep learning (DL) cine sequences provide fast and accurate quantification of left and right ventricular parameters in cardiovascular magnetic resonance (CMR), matching the image quality of conventional balanced steady-state free precession (bSSFP) sequences.

Keywords:

Artificial intelligence Cardiac MRI Cardiac function Cardiovascular magnetic resonance Deep learning Fast imaging

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Published on: July 20, 2022

Area of Science:

Cardiovascular Imaging
Artificial Intelligence in Medicine
Medical Diagnostics

Background:

Cardiovascular magnetic resonance (CMR) is crucial for assessing cardiac function.
Conventional balanced steady-state free precession (bSSFP) cine sequences are standard for CMR but can be time-consuming.
Deep learning (DL) offers potential for faster and efficient image acquisition and analysis.

Purpose of the Study:

To compare the diagnostic accuracy and image quality of DL cine sequences against conventional bSSFP cine sequences for left ventricular (LV) and right ventricular (RV) parameter quantification in CMR.
To evaluate the speed of DL cine sequences compared to bSSFP sequences.

Main Methods:

Prospective inclusion of patients undergoing clinically indicated CMR.
Segmentation of LV and RV from both bSSFP and DL cine sequences.
Calculation of LV and RV volumetric and functional parameters (EDV, ESV, SV, EF, LV mass).
Comparison of parameters using statistical tests and Bland-Altman analysis for agreement.
Assessment of image quality by two readers using a 5-point Likert scale.

Main Results:

No significant differences in LV and RV parameters were found between DL cine and bSSFP sequences (P ≥ .176).
DL cine sequences were significantly faster (1.35 min) than bSSFP sequences (2.83 min) (P < .001).
Strong agreement was observed between DL cine and bSSFP, with acceptable limits of agreement for end-systolic volume, stroke volume, and end-diastolic volume.
Overall image quality was comparable, though endocardial edge definition was slightly lower for DL cine.

Conclusions:

Deep learning cine sequences enable rapid and accurate quantification of LV and RV parameters in CMR.
DL cine demonstrates comparable diagnostic accuracy and overall image quality to conventional bSSFP sequences.
DL cine represents a promising advancement for efficient cardiovascular magnetic resonance imaging.