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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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Related Experiment Video

Updated: Jul 16, 2026

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

Many heads are better than one: jointly removing bias from multiple MRIs using nonparametric maximum likelihood.

Erik G Learned-Miller¹, Vidit Jain

¹Department of Computer Science, University of Massachusetts, Amherst, MA 01003, USA. elm@cs.umass.edu

Information Processing in Medical Imaging : Proceedings of the ... Conference

|March 16, 2007

Summary

This study introduces a novel method for correcting magnetic resonance imaging bias fields by analyzing voxel statistics across multiple patients. This approach improves quantitative measurements by simultaneously correcting bias in a set of images.

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Last Updated: Jul 16, 2026

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Published on: October 24, 2012

Area of Science:

Medical Image Processing
Computational Biology
Radiology

Background:

Multiplicative bias in magnetic resonance (MR) images hinders quantitative analysis.
Existing methods often estimate bias fields within a single image, ignoring spatial location.
Previous techniques rely on tissue models or nonparametric pixel value distributions.

Purpose of the Study:

To develop a novel method for correcting multiplicative bias in MR images.
To improve the accuracy of quantitative measurements in medical imaging.
To address limitations of single-image bias correction methods.

Main Methods:

A novel approach that simultaneously corrects bias fields across a set of MR images from different patients.
Utilizes statistics from the same anatomical location across multiple patient scans.
Evaluates voxel likelihoods across patient cohorts to distinguish bias from anatomy.

Main Results:

Demonstrates superior performance over existing methods in correcting bias fields.
Presents successful "two-dimensional" experimental results using single images per patient.
Shows preliminary success in three-dimensional volume correction across patients.

Conclusions:

The proposed cross-patient bias correction method effectively addresses limitations of single-image approaches.
This technique enhances the reliability of quantitative MR imaging.
The method shows promise for both 2D and 3D MR image bias correction.