Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Erratum: Imaging- and Tumor Biomarker-Based Multivariable Model for Early Prediction of Pathologic Complete Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer.

JCO precision oncology·2026

Same author

Evolutionary trajectory of Triticeae GATA transcription factors pinpoints TaGATA26-5A and TaGATA31.1-2B as regulators for drought and phosphorus tolerance.

International journal of biological macromolecules·2026

Same author

Artificial Intelligence in Triple-Negative Breast Cancer: Applications in Diagnosis, Treatment Response, and Prognosis.

Diagnostics (Basel, Switzerland)·2026

Same author

Imaging- and Tumor Biomarker-Based Multivariable Model for Early Prediction of Pathologic Complete Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer.

JCO precision oncology·2025

Same author

TaGF14g, a wheat 14-3-3 protein, acts as a positive regulator of drought and salt tolerance.

Plant cell reports·2025

Same author

Predicting the response of triple negative breast cancer to neoadjuvant systemic therapy via biology-based modeling and habitat analysis.

Scientific reports·2025

Same journal

A Comparison of Tissue Property Values Estimated Using Conventional Cardiac MRF and MT-Cardiac MRF.

Magnetic resonance in medicine·2026

Same journal

Dependence of the Extra-Cellular Diffusion Coefficient on the Fractions of Neurites and Cell Bodies in Gray Matter.

Magnetic resonance in medicine·2026

Same journal

Triple-Pulse <sup>23</sup>Na MRI Sequence (TriNa) for Simultaneous Acquisition of Spin-Density-Weighted and Fluid-Attenuated Images.

Magnetic resonance in medicine·2026

Same journal

Evaluation of Phantom Doping Materials in Quantitative Susceptibility Mapping.

Magnetic resonance in medicine·2026

Same journal

Design of an 8-Channel Transmit 32-Channel Receive 11.7T Head Coil and Evaluation of SNR Gains.

Magnetic resonance in medicine·2026

Same journal

The Potential for Absolute Temperature Imaging Based on Brain Metabolites Using an FID-Shifting Approach in Gradient Echo Planar Spectroscopic Imaging (GREPSI).

Magnetic resonance in medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 4, 2026

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

An improved region growing algorithm for phase correction in MRI.

Jingfei Ma¹, Jong Bum Son¹, John D Hazle¹

¹Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Magnetic Resonance in Medicine

|September 13, 2015

Summary

This summary is machine-generated.

This study introduces an enhanced region-growing algorithm for magnetic resonance imaging (MRI) phase correction. The improved algorithm reliably corrects phase in images with noise, artifacts, and isolated objects.

Keywords:

Dixon imaging joint consideration phase correction quality guidance region growing segmentation

More Related Videos

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Published on: August 9, 2024

Related Experiment Videos

Last Updated: Apr 4, 2026

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Published on: August 9, 2024

Area of Science:

Medical Imaging
Image Processing

Background:

Phase correction in MRI is crucial for image quality.
Existing algorithms struggle with noise, artifacts, and isolated objects.

Purpose of the Study:

To develop an improved region-growing algorithm for robust MRI phase correction.
Address limitations of current phase correction methods.

Main Methods:

Developed an improved region-growing algorithm with automated quality guidance.
Incorporated joint consideration of candidate vectors for output selection.
Implemented automated segmentation for handling isolated objects.

Main Results:

The algorithm successfully corrected phase in all tested datasets.
Demonstrated improved performance in challenging regions compared to existing methods.
Validated on healthy volunteers at 1.5T and 3.0T using two-point Dixon imaging.

Conclusions:

The enhanced algorithm provides reliable and robust phase correction.
Effective even in the presence of high noise, artifacts, or isolated objects.
Represents a significant advancement in MRI phase correction techniques.