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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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Updated: Aug 3, 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

Automatic 4D Flow MRI Segmentation Using the Standardized Difference of Means Velocity.

Sean M Rothenberger, Neal M Patel, Jiacheng Zhang

IEEE Transactions on Medical Imaging

|April 7, 2023

Summary

This summary is machine-generated.

A new method automatically segments 4D flow MRI using standardized difference of means (SDM) velocity, improving vessel detection. This robust approach enhances cardiovascular disease metric computation.

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Area of Science:

Medical Imaging
Biomedical Engineering
Cardiovascular Research

Background:

Accurate segmentation of 4D flow MRI is crucial for hemodynamic analysis.
Existing methods like pseudo-complex difference (PCD) and convolutional neural networks (CNNs) have limitations in robustness and accuracy.
Identifying net flow effects offers a promising avenue for improved vessel segmentation.

Purpose of the Study:

To introduce and validate a novel automated segmentation method for 4D flow MRI based on standardized difference of means (SDM) velocity.
To compare the performance of the SDM segmentation algorithm against PCD and CNN methods in various vascular datasets.
To assess the repeatability and applicability of the SDM algorithm for reliable hemodynamic analysis.

Main Methods:

Developed an automated segmentation algorithm utilizing standardized difference of means (SDM) velocity to identify net flow effects.
Employed an F-test to distinguish voxels with significant SDM velocity values from background.
Validated the SDM algorithm against PCD segmentation in in vitro cerebral aneurysm and Circle of Willis (CoW) models, and against CNN segmentation in thoracic vasculature datasets.

Main Results:

The SDM algorithm demonstrated superior robustness compared to PCD and CNN methods across different vascular territories.
SDM segmentation showed a significant increase in sensitivity over PCD (48% in vitro, 70% in CoW); SDM and CNN sensitivities were comparable.
Vessel surfaces derived from SDM were substantially closer to ground truth geometries than those from PCD.

Conclusions:

The SDM velocity-based segmentation is a repeatable and robust method for 4D flow MRI.
This algorithm facilitates accurate computation of hemodynamic metrics essential for diagnosing and managing cardiovascular diseases.
The SDM method shows potential for application across diverse vascular territories and imaging scenarios.