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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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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Evaluation of Six Phase Encoding Based Susceptibility Distortion Correction Methods for Diffusion MRI.

Xuan Gu^1,2, Anders Eklund^1,2,3

¹Division of Medical Informatics, Department of Biomedical Engineering, Linköping University, Linköping, Sweden.

Frontiers in Neuroinformatics

|December 24, 2019

Summary

This summary is machine-generated.

Susceptibility distortion correction in diffusion MRI is crucial. DR-BUDDI and TOPUP methods provide the most accurate and robust results for correcting diffusion MRI data, outperforming other phase encoding techniques.

Keywords:

diffusion MRI diffusion MRI simulation diffusion tensor opposing phase encoding susceptibility distortion

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Diffusion Imaging in the Rat Cervical Spinal Cord

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Published on: April 7, 2015

Area of Science:

Neuroimaging
Medical Physics
Diffusion MRI

Background:

Susceptibility distortions in diffusion MRI data can significantly impact analysis.
Existing correction methods include registration, fieldmaps, and phase encoding techniques.
Phase encoding methods are superior, but a lack of systematic comparison hinders optimal tool selection.

Purpose of the Study:

To quantitatively evaluate six popular phase encoding based methods for susceptibility distortion correction in diffusion MRI.
To compare the accuracy and robustness of different correction techniques using simulated and real data.
To assess the utility of direct and indirect evaluation metrics for distortion correction.

Main Methods:

Simulated realistic diffusion MRI data with susceptibility distortions.
Quantitative evaluation of six phase encoding based correction methods.
Comparison of corrected data against ground truth using diffusion tensor metrics (FA, MD, eigenvalues, eigenvectors).
Validation of two indirect metrics: difference between corrected LR/AP data and FA standard deviation.

Main Results:

DR-BUDDI and TOPUP demonstrated the most accurate and robust susceptibility distortion correction.
EPIC and HySCO showed good b0 correction but poor diffusion-weighted volume correction and significant diffusion tensor metric errors.
Indirect metrics can provide a different quality ranking than direct metrics but are highly correlated.

Conclusions:

DR-BUDDI and TOPUP are recommended for susceptibility distortion correction in diffusion MRI.
Combined interpretation of indirect metrics (LR/AP difference, FA std dev) is advised for assessing correction quality.
Indirect metrics can serve as a reliable proxy for correction performance when direct assessment is not feasible.