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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: Mar 1, 2026

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

Modelling Motion-Induced Signal Corruption in Steady-State Diffusion MRI.

Benjamin C Tendler¹, Wenchuan Wu¹, Karla L Miller¹

¹Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.

Magnetic Resonance in Medicine

|February 28, 2026

Summary

This summary is machine-generated.

We developed a new framework to model and correct for subject motion in diffusion-weighted steady-state free precession (DW-SSFP) imaging. This method improves diffusion tensor imaging by reducing motion-induced biases.

Keywords:

diffusion MRI diffusion‐weighted steady‐state free precession extended phase graphs in vivo motion correction steady‐state diffusion

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Last Updated: Mar 1, 2026

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

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Published on: November 8, 2012

Diffusion Imaging in the Rat Cervical Spinal Cord

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Published on: April 7, 2015

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Area of Science:

Magnetic Resonance Imaging
Diffusion Imaging
Biophysics

Background:

Diffusion-weighted steady-state free precession (DW-SSFP) offers high signal-to-noise ratio (SNR) efficiency for diffusion imaging.
Subject motion is a significant challenge in in vivo DW-SSFP, limiting its application to low-motion scenarios.
Accurate diffusion measurements are crucial for understanding tissue microstructure and disease.

Purpose of the Study:

To establish a framework for modeling and correcting subject motion in DW-SSFP imaging.
To address the motion sensitivity limitations of DW-SSFP for in vivo applications.
To enable robust diffusion tensor estimation in the presence of physiological motion.

Main Methods:

Developed an extended phase graphs (EPG) model incorporating a motion operator for DW-SSFP signals.
Validated the EPG-motion model using Monte Carlo simulations.
Integrated the model into a data fitting routine for motion estimation and correction, applied to in vivo human brain data.

Main Results:

The EPG-motion framework demonstrated excellent agreement with simulations, showing robust diffusion coefficient estimation across various motion and SNR levels.
Motion-corrected DW-SSFP tensor estimates showed good visual agreement with diffusion-weighted spin-echo (DW-SE) data.
The method significantly reduced orientation-dependent motion-induced biases in diffusion tensor imaging.

Conclusions:

Temporal signal evolution in DW-SSFP can be leveraged for retrospective motion estimation and correction.
The developed framework enables the reconstruction of motion-corrected DW-SSFP data.
Open-source software is provided to facilitate future research on motion impacts in DW-SSFP acquisitions.