Explainable multi-contrast deep learning model with anomaly-aware attention for reduced gadolinium dose in CE brain MRI - a feasibility study
Srivathsa Pasumarthi Venkata1, Ben Andrew Duffy1, Enhao Gong2, Greg Zaharchuk3, and Keshav Datta1
1R&D, Subtle Medical Inc, Menlo Park, CA, United States, 2R&D, Subtle Medical Inc., Menlo Park, CA, United States, 3Department of Radiology, Stanford University, Stanford, CA, United States
Complementary information from multi-contrast MRI data is used in deep learning algorithms for reducing contrast dosage in brain MRI. Though existing models produce clinically equivalent post-contrast images, they lack explainability in terms of mapping the source of contrast information from input to output. In this work we explore the feasibility of an explainable deep learning model for gadolinium dose reduction in contrast-enhanced brain MRI.
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