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Abstract #0278

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

Deep learning (DL) has recently been proven to be effective in addressing the safety concerns of Gadolinium-based Contrast Agents (GBCAs). Recent studies have shown that DL-based algorithms are able to reconstruct contrast-enhanced MRI images with only a fraction of the standard dose. 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.

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