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Abstract #4063

Retrospective Contrast Tuning from a Single T1-weighted Image Using Deep Learning

Yan Wu1, Yajun Ma2, Jiang Du2, and Lei Xing1
1Stanford University, Palo Alto, CA, United States, 2University of California San Diego, San Diego, CA, United States

While versatile soft tissue contrasts are achievable in MRI, contrast attainable from each scan is predetermined by the imaging protocol. A retrospective tuning of contrast will provide an opportunity to normalize MRI data for radiomics analysis. In this study, we present a new paradigm to obtain a spectrum of contrasts from a single T1-weighted image. Using deep learning, T1 map, proton density map, and B1 map are predicted from every T1-weighted image, and new contrasts can be obtained with the application of Bloch equations. The method has been validated in knee MRI with high accuracy achieved.

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