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Abstract #2017

Deep Learning Using Synthetic Data for Signal Denoising and Spectral Fitting in Deuterium Metabolic Imaging

Abidemi Adebayo1, Keshav Datta2, Ronald Watkins2, Shie-Chau Liu2, Ralph Hurd2, and Daniel Mark Spielman2
1Mechanical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States

Deuterium metabolic imaging, a promising tool to probe in vivo glucose metabolism, is severely limited by SNR due to the low gyromagnetic ratio of 2H and the low concentration of metabolites. Recent advances in machine learning techniques to reduce noise is a promising option but obtaining training datasets with good SNR requires prohibitively long scan times. In this work we show that an autoencoder network trained using only synthetic data can reduce noise and provide a good spectral fit for in vivo 3T spectra obtained from human brain after ingestion of deuterated glucose.

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