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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: Jun 18, 2026

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

Predictive Models in Differentiating Vertebral Lesions Using Multiparametric MRI.

R Rathore¹, A Parihar², D K Dwivedi¹

¹From the Departments of Radiodiagnosis (R.R., A.P., D.K.D., N.K.).

AJNR. American Journal of Neuroradiology

|October 14, 2017

Summary

This summary is machine-generated.

Multiparametric MRI effectively differentiates spinal vertebral lesions. Statistical models using apparent diffusion coefficient and signal intensity ratio accurately predict malignancy, aiding diagnosis.

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Last Updated: Jun 18, 2026

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Area of Science:

Radiology
Medical Imaging
Oncology

Background:

Conventional MRI has limitations in differentiating vertebral lesions.
Need for improved specificity in diagnosing spinal tumors.
Assessing multiparametric MRI for enhanced diagnostic accuracy.

Purpose of the Study:

Evaluate multiparametric MRI for differentiating spinal vertebral lesions.
Develop statistical models to predict malignant vertebral lesions.
Improve diagnostic specificity beyond conventional MRI.

Main Methods:

126 patients with vertebral lesions underwent multiparametric MRI.
Included conventional MRI, diffusion-weighted imaging, and in-phase/opposed-phase imaging.
Calculated apparent diffusion coefficient (ADC) and signal intensity ratio for lesion classification.

Main Results:

Multiparametric MRI demonstrated strong discriminatory ability for lesion types.
Predictive models achieved high accuracy (AUC 0.92 and 0.91) in differentiating lesion subtypes.
Established automated statistical models based on ADC and signal intensity ratio.

Conclusions:

Multiparametric MRI is effective in differentiating various vertebral lesions.
Developed prediction models aid in classifying spinal vertebral lesions.
Enhances diagnostic capabilities for spinal pathology.