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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