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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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Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Published on: February 12, 2011

Brightness-Invariant Tracking Estimation in Tagged MRI.

Zhangxing Bian¹, Shuwen Wei¹, Xiao Liang²

¹Johns Hopkins University, Baltimore, MD, USA.

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

|December 24, 2025

Summary

Magnetic resonance (MR) tagging tracks tissue motion but faces accuracy issues due to changing brightness. The new brightness-invariant tracking estimation (BRITE) technique improves motion and strain estimation in tagged MRI.

Keywords:

MR tagging Motion tracking Spectral overlap Strain

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Last Updated: Jun 27, 2026

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

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Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

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Published on: August 11, 2016

Area of Science:

Medical Imaging
Biophysics
Computational Biology

Background:

Magnetic resonance (MR) tagging is a noninvasive technique to track tissue motion in vivo.
Conventional optical flow and Fourier methods for tagged MR image analysis are susceptible to errors caused by changing tag and tissue brightness over time.
Tag fading and spectral spreading due to motion further complicate accurate motion tracking.

Purpose of the Study:

To introduce a novel technique, brightness-invariant tracking estimation (BRITE), for accurate motion tracking in tagged MRI.
To address the limitations of existing methods in handling brightness variations and tag fading.
To disentangle anatomy from tag patterns and simultaneously estimate Lagrangian motion.

Main Methods:

BRITE utilizes denoising diffusion probabilistic models to represent anatomy and physics-informed neural networks for biologically plausible motion estimation.
The technique disentangles anatomical information from the tag pattern within tagged MR image sequences.
Experiments were conducted on a gel phantom with varying tag periods and flip angles to assess performance under different brightness conditions.

Main Results:

BRITE demonstrated superior accuracy in motion and strain estimation compared to state-of-the-art methods.
The proposed method exhibits significant resistance to tag fading, a common artifact in tagged MRI.
Validation using a gel phantom confirmed the effectiveness of BRITE in diverse imaging scenarios.

Conclusions:

BRITE offers a robust solution for accurate motion and strain quantification in tagged MRI, overcoming challenges posed by brightness variations and tag fading.
The integration of deep learning models (diffusion models and physics-informed neural networks) enhances the reliability and accuracy of the technique.
BRITE represents a significant advancement for in vivo tissue motion analysis using tagged MR imaging.