A generic deep learning model for reduced gadolinium dose in contrast‐enhanced brain MRI
Srivathsa Pasumarthi, Jonathan I. Tamir, Sören Christensen, Greg Zaharchuk, Tao Zhang, Enhao Gong
Abstract
Purpose With rising safety concerns over the use of gadolinium‐based contrast agents (GBCAs) in contrast‐enhanced MRI, there is a need for dose reduction while maintaining diagnostic capability. This work proposes comprehensive technical solutions for a deep learning (DL) model that predicts contrast‐enhanced images of the brain with approximately 10% of the standard dose, across different sites and scanners. Methods The proposed DL model consists of a set of methods that improve the model robustness and generalizability. The steps include multi‐planar reconstruction, 2.5D model, enhancement‐weighted L1, perceptual, and adversarial losses. The proposed model predicts contrast‐enhanced images from corresponding pre‐contrast and low‐dose images. With IRB approval and informed consent, 640 heterogeneous patient scans (56 train, 13 validation, and 571 test) from 3 institutions consisting of 3D T1‐weighted brain images were used. Quantitative metrics were computed and 50 randomly sampled test cases were evaluated by 2 board‐certified radiologists. Quantitative tumor segmentation was performed on cases with abnormal enhancements. Ablation study was performed for systematic evaluation of proposed technical solutions. Results The average peak signal‐to‐noise ratio (PSNR) and structural similarity index measure (SSIM) between full‐dose and model prediction were dB and , respectively. Radiologists found the same enhancing pattern in 45/50 (90%) cases; discrepancies were minor differences in contrast intensity and artifacts, with no effect on diagnosis. The average segmentation Dice score between full‐dose and synthesized images was (median = 0.91). Conclusions We have proposed a DL model with technical solutions for low‐dose contrast‐enhanced brain MRI with potential generalizability under diverse clinical settings.