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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: May 17, 2026

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

Towards Automatic Quantitative Quality Control for MRI.

Carolyn B Lauzon¹, Brian C Caffo, Bennett A Landman

¹Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA 37235 ; Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA 37235.

Proceedings of Spie--The International Society for Optical Engineering

|October 23, 2012

Summary

This summary is machine-generated.

This study introduces an automated pipeline for Magnetic Resonance Imaging (MRI) data quality control, specifically for diffusion tensor imaging (DTI). It enhances data reliability by assessing experiment-specific variance and bias, improving research accuracy.

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Area of Science:

Medical Imaging
Neuroimaging
Biostatistics

Background:

Magnetic Resonance Imaging (MRI) data quality is susceptible to various factors like hardware changes, software updates, and artifacts.
Current quality control methods (visual assessment, phantom scans) are often insufficient, lacking timeliness and experiment-specific relevance.

Purpose of the Study:

To develop and present a parallel processing pipeline for automatic, experiment-specific quantitative quality control of MRI data.
To utilize diffusion tensor imaging (DTI) as a test case for this novel quality control pipeline.

Main Methods:

Automatic identification of DTI scans from MRI data.
Calculation of DTI contrasts and implementation of statistical methods (wild bootstrap, SIMEX) for variance and bias assessment.
Development of DTI-specific power calculations and incorporation of bias estimates for improved statistical analysis.

Main Results:

A functional parallel processing pipeline for automated DTI quality control was successfully developed.
The pipeline enables experiment-specific quantitative assessment of MRI data quality.
Novel methods for DTI power calculations and bias estimation were integrated.

Conclusions:

The developed pipeline offers a robust solution for ensuring the quality and consistency of MRI data, particularly DTI.
Automated, experiment-specific quality control improves the reliability and statistical power of neuroimaging research.
This approach addresses limitations of traditional quality control methods, enhancing the validity of research findings.