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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: Jun 22, 2026

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

Published on: April 18, 2015

Quantifying spatial heterogeneity in dynamic contrast-enhanced MRI parameter maps.

Chris J Rose¹, Samantha J Mills, James P B O'Connor

¹School of Medicine, The University of Manchester, Manchester, United Kingdom. chris.rose@manchester.ac.uk

Magnetic Resonance in Medicine

|May 26, 2009

Summary

This summary is machine-generated.

New biomarkers for dynamic contrast-enhanced MRI (DCE-MRI) capture spatial patterns in cancer imaging, improving detection of treatment effects and tumor grades compared to traditional methods.

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Last Updated: Jun 22, 2026

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

Published on: April 18, 2015

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

Radiology
Medical Imaging
Oncology

Background:

Dynamic contrast-enhanced MRI (DCE-MRI) is crucial for cancer drug trials evaluating anti-angiogenic therapies.
Current DCE-MRI biomarkers often overlook spatial information in parameter maps, relying solely on summary statistics.
This spatial information may hold significant diagnostic and prognostic value.

Purpose of the Study:

To introduce novel heterogeneity biomarkers for DCE-MRI parameter maps.
These biomarkers are designed to be sensitive to both parameter values and their spatial arrangement.
To evaluate the utility of these biomarkers in cancer drug trials and tumor grading.

Main Methods:

Development of heterogeneity biomarkers based on Rényi fractal dimensions.
Development of heterogeneity biomarkers based on geometrical properties of DCE-MRI parameter maps.
Validation using simulated data and experimental DCE-MRI data from glioma patients.

Main Results:

Proposed biomarkers detect changes missed by traditional distribution-based summary statistics.
Heterogeneity biomarkers show potential for application in anti-angiogenic drug trial settings.
Biomarkers successfully differentiate between low- and high-grade gliomas using DCE-MRI parameter maps.

Conclusions:

Novel heterogeneity biomarkers offer a more comprehensive analysis of DCE-MRI parameter maps.
These biomarkers enhance the assessment of anti-vascular/angiogenic agents in cancer clinical trials.
Spatial heterogeneity analysis using these biomarkers aids in glioma grading.