Meeting Banner
Abstract #4995

Can a neural network predict B0 maps from uncorrected CEST-spectra?

Felix Glang1, Anagha Deshmane1, Florian Martin1, Kai Herz1, Klaus Scheffler1,2, and Moritz Zaiss1

1High-Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, 2Department of Biomedical Magnetic Resonance, Eberhard Karls University Tuebingen, Tuebingen, Germany

Analysis of chemical exchange saturation transfer (CEST) effects suffers from B0 inhomogeneity. Common correction methods involve computationally expensive algorithms or even additional measurements. Here we demonstrate that deep neural networks are able to predict B0 maps from raw Z-spectra by training the networks with measured B0 maps. Moreover, we show that CEST contrast parameters representing amide, amine and NOE resonance peaks can be directly predicted from uncorrected Z-spectra in a fast single step. This provides a shortcut to conventional evaluation procedures and will be useful to guide nonlinear model fitting.

This abstract and the presentation materials are available to members only; a login is required.

Join Here