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Anomaly-aware multi-contrast deep learning model for reduced gadolinium dose in contrast-enhanced brain MRI - a feasibility study

Srivathsa Pasumarthi¹, Enhao Gong¹, Greg Zaharchuk¹, and Tao Zhang¹
¹Subtle Medical Inc., Menlo Park, CA, United States

This work investigates the feasibility of improving the performance of such DL algorithms using multi-contrast MRI data, combined with an unsupervised anomaly detection based attention mechanism.

GBCA dose-reduction model with T2W image and the generated UAD mask as attention mechanism. The model is trained with a combination of SSIM, perceptual, adversarial and UAD-mask weighted L1-loss.

Examples of cases where the proposed model has a better tumor/lesion enhancement pattern compared to the T1-only model.