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Abstract #4190

Deep Learning Enables 90% Reduction in Gadolinium Dosage for Contrast Enhanced MRI

Enhao Gong1, John Pauly1, Max Wintermark2, and Greg Zaharchuk2

1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States

There are increasing concerns over gadolinium-based-contrast-agents-administration(GBCA). A deep-learning (DL) method was developed to reduce the gadolinium dose in Contrast-Enhanced-MRI (CE-MRI). The proposed method includes an acquisition step(pre-contrast, 10% low-dose and full-dose CE-MRI with T1-weighted-IR-FSPGR), a pre-processing step and a deep learning model trained to predict full-dose CE-MRI from pre-contrast and low-dose images. Evaluated on a clinical neuro CE-MRI dataset (10 patients for training and another 20 patients for evaluation), both quantitative metrics and radiologists’ ratings showed the proposed method achieved improved synthesis, with better motion-artifact-suppression and NO significant differences in contrast-enhancement quality, compared with ground-truth full-dose CE-MRI. Thus, using the proposed Deep Learning method, GBCA can be reduced, by at-least-10-fold, while preserving image quality and diagnostic information.

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