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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: Apr 20, 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

Models and methods for analyzing DCE-MRI: a review.

Fahmi Khalifa¹, Ahmed Soliman², Ayman El-Baz²

¹BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292 and Electronics and Communication Engineering Department, Mansoura University, Mansoura 35516, Egypt.

Medical Physics

|December 5, 2014

Summary

This summary is machine-generated.

Dynamic Contrast-Enhanced MRI (DCE-MRI) analysis techniques quantify tissue perfusion. These methods are vital for diagnosing diseases like cancer and monitoring therapy effectiveness.

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Last Updated: Apr 20, 2026

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

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Published on: November 8, 2012

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Area of Science:

Medical Imaging
Radiology
Biomedical Engineering

Background:

Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) enables noninvasive, quantitative analysis of contrast agent dynamics in soft tissues.
It is a crucial tool for assessing microvasculature and tissue perfusion across diverse clinical scenarios.

Purpose of the Study:

To review common DCE-MRI analysis techniques, evaluating their respective strengths and limitations.
To outline recent clinical uses of findings derived from these DCE-MRI analysis approaches.

Main Methods:

Development of numerous nonparametric and parametric models over three decades to quantify contrast agent perfusion.
Estimation of perfusion-related parameters from signal- or concentration-time curves.

Main Results:

DCE-MRI analysis techniques show promising results in various clinical applications.
These methods facilitate early diagnosis of diseases including breast and prostate cancer, renal rejection, and liver tumors.

Conclusions:

Both nonparametric and parametric analysis approaches for DCE-MRI effectively quantify tissue perfusion.
DCE-MRI is a clinically relevant imaging modality for disease detection, characterization, and therapy monitoring.