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Magnetic Resonance Imaging

Magnetic Resonance Imaging

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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Updated: Apr 1, 2026

Phase Contrast Magnetic Resonance Imaging in the Rat Common Carotid Artery

Phase Contrast Magnetic Resonance Imaging in the Rat Common Carotid Artery

Published on: September 5, 2018

A Bayesian model for highly accelerated phase-contrast MRI.

Adam Rich¹, Lee C Potter^1,2, Ning Jin³

¹Department of Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio, USA.

Magnetic Resonance in Medicine

|October 8, 2015

Summary

This summary is machine-generated.

Reconstructing Velocity Encoded MRI with Approximate message passing aLgorithms (ReVEAL) accelerates cardiovascular imaging by exploiting data structure. This method enables accurate blood flow quantification from undersampled data, improving clinical applications.

Keywords:

Bayesian inference approximate message passing cardiac MRI factor graph flow imaging minimum mean squared error estimation peak blood flow velocity

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Last Updated: Apr 1, 2026

Phase Contrast Magnetic Resonance Imaging in the Rat Common Carotid Artery

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Area of Science:

Medical Imaging
Cardiovascular Imaging
Biomedical Engineering

Background:

Phase-contrast MRI is crucial for cardiovascular disease assessment by quantifying blood flow.
Current limitations in data acquisition efficiency hinder spatial/temporal resolution and real-time applications.
Extending to 4D flow imaging is challenging due to efficiency constraints.

Purpose of the Study:

To introduce Reconstructing Velocity Encoded MRI with Approximate message passing aLgorithms (ReVEAL) for accelerated MRI acquisition.
To enhance data acquisition efficiency in phase-contrast MRI.
To improve spatial and temporal resolutions for cardiovascular flow imaging.

Main Methods:

Developed a novel data processing approach, ReVEAL, leveraging unique data structures in phase-contrast MRI.
Modeled physical correlations across space, time, and velocity encodings using a Bayesian approach.
Implemented a fast iterative recovery algorithm based on message passing for data reconstruction.

Main Results:

ReVEAL demonstrated good agreement with reference data for peak velocity and stroke volume (SV) at acceleration rates up to R=10.
Achieved high correlation coefficients for SV: Pearson r≥0.99 (phantom) and r≥0.96 (in vivo).
Validated using a pulsatile flow phantom and prospectively undersampled data from healthy volunteers.

Conclusions:

ReVEAL enables accurate blood flow quantification from highly undersampled phase-contrast MRI data.
The technique is extensible to 4D flow imaging, potentially allowing for higher acceleration.
This approach addresses key limitations in current cardiovascular MRI techniques.