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Simultaneous PET/MRI Imaging During Mouse Cerebral Hypoxia-ischemia

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AI-Assisted Post Contrast Brain MRI: Eighty Percent Reduction in Contrast Dose.

Mohadese Ahmadzade¹, Fanny Emilia Moron², Ravi Shastri³

¹Department of Radiology, Section of Vascular and Interventional Radiology, Baylor College of Medicine, Houston, TX (M.A., M.G.R.).

Academic Radiology

|November 26, 2024

Summary

A novel deep learning method successfully generated high-quality contrast-enhanced MRI images using significantly reduced gadolinium contrast doses. This approach maintains diagnostic accuracy while addressing safety concerns associated with contrast agents.

Keywords:

Deep learning Gadolinium-based contrast agents Low-dose Magnetic resonance imaging

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Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

Growing safety concerns regarding gadolinium-based contrast agents (GBCAs) necessitate dose reduction strategies in MRI.
Maintaining diagnostic accuracy is crucial when reducing GBCA doses.

Purpose of the Study:

To evaluate a deep learning (DL) method for synthesizing full-dose contrast-enhanced T1-weighted (T1w) MRI images from low-dose (20% standard) acquisitions.
To assess the diagnostic performance of DL-generated images compared to standard-dose images.

Main Methods:

A multicentric prospective study included 101 patients with various brain pathologies.
A DL network processed pre-contrast and low-dose T1w sequences to generate synthesized full-dose T1w images (DL-T1w).
DL-T1w images were compared to actual full-dose T1w images via quantitative metrics (SSIM, PSNR) and a reader study by three neuroradiologists.

Main Results:

No significant difference in overall image quality between DL-T1w and full-dose T1w images (P=0.08).
DL-T1w images showed significantly higher image Signal-to-Noise Ratio (SNR) and vessel conspicuity (P<0.05).
Statistical noninferiority was demonstrated for border delineation, internal morphology, and contrast enhancement (P<0.001), with DL-T1w achieving 86% SSIM and 27 dB PSNR.

Conclusions:

The proposed DL method effectively synthesizes postcontrast T1w MRI images comparable to full-dose images.
This DL approach enables significant reduction in gadolinium contrast agent dose without compromising diagnostic quality.
The findings support the potential of DL for safer and more efficient contrast-enhanced MRI.