Deep learning enables reduced gadolinium dose for contrast‐enhanced brain MRI

• Published in: Journal of magnetic resonance imaging Vol. 48; no. 2; pp. 330 - 340
• Main Authors: Gong, Enhao, Pauly, John M., Wintermark, Max, Zaharchuk, Greg
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.08.2018
• Subjects: Brain; Contrast agents; contrast enhanced MRI; Deep learning; Gadolinium; gadolinium deposition; Glioma; Image acquisition; Image contrast; Image enhancement; Image quality; low dose; machine learning; Magnetic resonance imaging; Medical imaging; Model testing; Neuroimaging; Patients; Population (statistical); Population studies; Quality; Statistical analysis; Synthesis; Teaching methods; Test sets; deep learning; image quality; contrast enhanced MRI; gadolinium deposition; low dose; machine learning
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.25970

Background There are concerns over gadolinium deposition from gadolinium‐based contrast agents (GBCA) administration. Purpose To reduce gadolinium dose in contrast‐enhanced brain MRI using a deep learning method. Study type Retrospective, crossover. Population Sixty patients receiving clinically indicated contrast‐enhanced brain MRI. Sequence 3D T1‐weighted inversion‐recovery prepped fast‐spoiled‐gradient‐echo (IR‐FSPGR) imaging was acquired at both 1.5T and 3T. In 60 brain MRI exams, the IR‐FSPGR sequence was obtained under three conditions: precontrast, postcontrast images with 10% low‐dose (0.01mmol/kg) and 100% full‐dose (0.1 mmol/kg) of gadobenate dimeglumine. We trained a deep learning model using the first 10 cases (with mixed indications) to approximate full‐dose images from the precontrast and low‐dose images. Synthesized full‐dose images were created using the trained model in two test sets: 20 patients with mixed indications and 30 patients with glioma. Assessment For both test sets, low‐dose, true full‐dose, and the synthesized full‐dose postcontrast image sets were compared quantitatively using peak‐signal‐to‐noise‐ratios (PSNR) and structural‐similarity‐index (SSIM). For the test set comprised of 20 patients with mixed indications, two neuroradiologists scored blindly and independently for the three postcontrast image sets, evaluating image quality, motion‐artifact suppression, and contrast enhancement compared with precontrast images. Statistical Analysis Results were assessed using paired t‐tests and noninferiority tests. Results The proposed deep learning method yielded significant (n = 50, P < 0.001) improvements over the low‐dose images (>5 dB PSNR gains and >11.0% SSIM). Ratings on image quality (n = 20, P = 0.003) and contrast enhancement (n = 20, P < 0.001) were significantly increased. Compared to true full‐dose images, the synthesized full‐dose images have a slight but not significant reduction in image quality (n = 20, P = 0.083) and contrast enhancement (n = 20, P = 0.068). Slightly better (n = 20, P = 0.039) motion‐artifact suppression was noted in the synthesized images. The noninferiority test rejects the inferiority of the synthesized to true full‐dose images for image quality (95% CI: –14–9%), artifacts suppression (95% CI: –5–20%), and contrast enhancement (95% CI: –13–6%). Data Conclusion With the proposed deep learning method, gadolinium dose can be reduced 10‐fold while preserving contrast information and avoiding significant image quality degradation. Level of Evidence: 3 Technical Efficacy: Stage 5 J. MAGN. RESON. IMAGING 2018;48:330–340.