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Machine Learning accelerates and stabilizes selective CEST fitting at 3T

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Deshmane,  A
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zaiss,  M
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Herz,  K
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Scheffler,  K
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Deshmane, A., Zaiss, M., Herz, K., Bender, B., Lindig, T., & Scheffler, K. (2019). Machine Learning accelerates and stabilizes selective CEST fitting at 3T. Poster presented at 27th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2019), Montréal, QC, Canada.


Cite as: http://hdl.handle.net/21.11116/0000-0003-96D5-3
Abstract
Multi-Lorentzian analysis of chemical exchange saturation transfer (CEST) Z-spectra by non-linear least squares (NLLS) fitting is common at ultra-high field strengths but particularly challenging at clinical field strengths due to broad, coalesced peaks and low SNR. Here we demonstrate that a neural network (NN) trained on just 3 slices of a single subject can robustly predict CEST Lorentzian pool parameters in other subjects, in the presence of motion, and in a brain tumor patient, with a 95 % reduction in computing time, allowing for quick estimation of NLLS initial conditions or initial online reconstruction of spectrally selective CEST contrasts.