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
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. In this work we show that an autoencoder network trained using only synthetic data can reduce noise and provide a good spectral fit.
Figure 3 Reconstructed spectra from the neural network (dotted blue lines),
compared with the spectra from human brain obtained 15, 45, 75 and 105 minutes
post oral ingestion of deuterated glucose (solid red lines).
Figure 1 Autoencoder architecture used for denoising and fitting the spectrum.
