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Abstract #2167

Automated quantitative evaluation of deep learning model for reduced gadolinium dose in contrast-enhanced brain MRI

Srivathsa Pasumarthi1, Jon Tamir2, Enhao Gong2, Greg Zaharchuk2, and Tao Zhang2
1Subtle Medical Inc, Menlo Park, CA, United States, 2Subtle Medical Inc., Menlo Park, CA, United States

Deep learning (DL) has recently become a promising technique to address the safety concerns for Gadolinium-based Contrast Agents (GBCAs) in MRI. Studies have shown that high quality contrast-enhanced images can be generated by DL with only a small fraction of the standard dose, and in some cases no dose at all. To build upon existing research that has heavily focused on qualitative evaluation by radiologists, this work proposes an automated quantitative evaluation scheme for the GBCA dose reduction using DL.

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