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Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Virtual MOLLI Target: Generative Adversarial Networks Toward Improved Motion Correction in MRI Myocardial T1 Mapping.

Nai-Yu Pan¹, Teng-Yi Huang¹, Jui-Jung Yu¹

¹Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.

Journal of Magnetic Resonance Imaging : JMRI

|April 2, 2024

Summary

A generative adversarial network (GAN) model creates virtual MOLLI images, significantly improving motion correction for cardiac T1 mapping. This technique enhances accuracy, especially for patients unable to hold their breath during scans.

Keywords:

MOLLI generative adversarial networks registration

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Area of Science:

Cardiovascular MRI
Medical Image Analysis
Artificial Intelligence in Medicine

Background:

Modified Look-Locker inversion recovery (MOLLI) is standard for myocardial T1 mapping.
Respiratory motion causes misregistration in MOLLI datasets, complicating motion correction.
Acquiring images at different inversion times exacerbates motion correction challenges.

Purpose of the Study:

To develop and evaluate a generative adversarial network (GAN) for creating virtual MOLLI target (VMT) images.
To reduce respiratory-induced misregistration in MOLLI datasets using GAN-generated VMT images.
To improve the accuracy and reliability of cardiac T1 mapping.

Main Methods:

Retrospective analysis of 1071 MOLLI datasets from 392 participants acquired at 3 Tesla.
Training a GAN model to generate VMT images from a single inversion time image.
Comparing the best VMT model with vendor-provided motion correction (MOCO) using FQI, MI, Dice coefficients, and subjective reader scores.

Main Results:

The best VMT model with iterative registration showed superior performance.
Quantitative metrics (FQI, MI, Dice) and subjective quality scores were significantly better than MOCO.
The GAN approach demonstrated robust performance despite significant respiratory-induced heart displacements.

Conclusions:

GAN-generated VMT images significantly enhance motion correction in MOLLI datasets.
This method facilitates reliable T1 mapping in the presence of respiratory motion.
The approach is particularly beneficial for patients with breath-holding difficulties.