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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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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

Deep Learning-Based Generation of DSC MRI Parameter Maps Using Dynamic Contrast-Enhanced MRI Data.

Haoyang Pei^1,2,3, Yixuan Lyu^3,4, Sebastian Lambrecht^5,6,7

¹From the BioMedical Engineering and Imaging Institute (H.P., L.F., X.X), Icahn School of Medicine at Mount Sinai, New York, New York.

AJNR. American Journal of Neuroradiology

|April 7, 2025

Summary

This summary is machine-generated.

This study developed a deep learning method to create dynamic susceptibility contrast (DSC) MRI perfusion maps from dynamic contrast-enhanced (DCE) MRI data, reducing the need for two contrast doses.

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Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Neuroimaging

Background:

Dynamic susceptibility contrast (DSC) MRI and dynamic contrast-enhanced (DCE) MRI provide valuable perfusion parameters for clinical diagnosis and research.
Current protocols often require two gadolinium contrast doses when using both DSC and DCE MRI in the same session.
Developing methods to obtain DSC-derived maps from DCE MRI data can streamline the imaging process and reduce contrast agent administration.

Purpose of the Study:

To develop and validate deep learning-based methods for synthesizing DSC-derived parameter maps from DCE MRI data.
To enable the acquisition of both DSC and DCE MRI parameter maps using a single contrast agent dose.

Main Methods:

A conditional generative adversarial network (cGAN) was designed and trained using a dataset of 64 participants (including brain tumor patients).
Reference DSC MRI parameter maps were acquired following DCE MRI.
The performance of the cGAN was evaluated by comparing synthetic DSC maps with ground truth DSC maps using linear regression and Bland-Altman analysis.

Main Results:

The cGAN successfully synthesized realistic DSC parameter maps from DCE MRI data.
Synthesized parameters showed similar distributions to ground truth values in healthy controls.
In brain tumor patients, synthesized parameters in the tumor region demonstrated a strong linear correlation with ground truth values.
DCE-derived DSC maps visualized regions obscured by susceptibility artifacts in conventional DSC MRI.

Conclusions:

Deep learning enables the synthesis of DSC-derived parameter maps from DCE MRI data, even in artifact-prone areas.
This approach holds significant potential for obtaining comprehensive perfusion information (both DSC and DCE) with a single contrast agent injection.
The findings suggest a more efficient and patient-friendly approach to advanced neuroimaging.