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

Abstract

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.

Keywords: contrast enhanced MRI; deep learning; gadolinium deposition; image quality; low dose; machine learning.