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Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI
Enhao Gong1,2, John M Pauly1, Max Wintermark2
1Department of Electrical Engineering, Stanford University, Stanford, California, USA.
Journal of Magnetic Resonance Imaging : JMRI
|February 14, 2018
Summary
Deep learning reduces gadolinium dose in brain MRI by tenfold. This method preserves contrast information and image quality, minimizing concerns over gadolinium deposition from gadolinium-based contrast agents.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Concerns exist regarding gadolinium deposition in patients following administration of gadolinium-based contrast agents (GBCAs).
- Reducing the administered gadolinium dose is crucial for patient safety and minimizing long-term risks.
- Optimizing contrast-enhanced MRI protocols is essential for diagnostic accuracy while managing potential adverse effects.
Purpose of the Study:
- To develop and evaluate a deep learning method for reducing gadolinium dose in contrast-enhanced brain MRI.
- To synthesize full-dose contrast-enhanced images from low-dose acquisitions.
- To assess the feasibility of a tenfold reduction in gadolinium dose without compromising diagnostic image quality.
Main Methods:
- A retrospective crossover study involving 60 patients undergoing contrast-enhanced brain MRI.
- A deep learning model was trained to generate full-dose images from precontrast and low-dose (10% of full dose) gadobenate dimeglumine images.
- Quantitative analysis using PSNR and SSIM, alongside blinded radiologist scoring for image quality, motion artifacts, and contrast enhancement.
Main Results:
- Deep learning significantly improved low-dose images, showing >5 dB PSNR and >11.0% SSIM gains.
- Synthesized full-dose images demonstrated comparable image quality and contrast enhancement to true full-dose images, with non-significant differences.
- Motion artifact suppression was slightly improved in synthesized images compared to true full-dose images.
Conclusions:
- A tenfold reduction in gadolinium dose is achievable using the proposed deep learning method.
- The method effectively preserves essential contrast information and diagnostic image quality.
- This approach offers a promising strategy to mitigate risks associated with gadolinium deposition in MRI.
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