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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: Jun 13, 2025

In vitro Assessment of Aortic Regurgitation Using Four-Dimensional Flow Magnetic Resonance Imaging

In vitro Assessment of Aortic Regurgitation Using Four-Dimensional Flow Magnetic Resonance Imaging

Published on: February 25, 2022

4D flow MRI velocity uncertainty quantification.

Sean M Rothenberger¹, Jiacheng Zhang², Michael Markl³

¹Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA.

Magnetic Resonance in Medicine

|September 13, 2024

Summary

This summary is machine-generated.

A new method quantifies uncertainty in 4D flow MRI velocity measurements using the velocity distance (VD) metric. This improves the reliability of 4D flow MRI data for clinical comparisons.

Keywords:

hemodynamics magnetic resonance velocimetry (MRV)phase contrast magnetic resonance imaging (PC‐MRI)

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Last Updated: Jun 13, 2025

In vitro Assessment of Aortic Regurgitation Using Four-Dimensional Flow Magnetic Resonance Imaging

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Area of Science:

Medical Imaging
Cardiovascular Imaging
Biomedical Engineering

Background:

4D flow MRI provides valuable hemodynamic information.
Quantifying velocity measurement uncertainty is crucial for reliable interpretation.
Existing methods lack comprehensive uncertainty assessment.

Purpose of the Study:

To develop an automatic method for voxel-wise uncertainty estimation in 4D flow MRI.
Introduce the velocity distance (VD) metric as a measure of 4D flow MRI velocity quality.

Main Methods:

Utilized mass conservation for local velocity error variance.
Employed standardized difference of means (SDM) for velocity error correlations.
Evaluated VD as Mahalanobis distance; validated synthetically and in vitro.

Main Results:

Method sensitive to aliasing, noise, resolution, and phase wrapping.
Accurate assessment of velocity error correlations (<3.2%).
Demonstrated increased velocity quality with contrast and systolic flow in vivo.

Conclusions:

The method quantifies velocity errors from noise, phase dispersion, and aliasing.
Enables rigorous comparison of 4D flow MRI datasets across studies and systems.