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Abstract #5002

Machine Learning accelerates and stabilizes selective CEST fitting at 3T

Anagha Deshmane1, Moritz Zaiss1, Kai Herz1, Benjamin Bender2, Tobias Lindig2, and Klaus Scheffler1

1High-field magnetic resonance center, Max Planck Institute for biological cybernetics, Tübingen, Germany, 2Diagnostic & Interventional Neuroradiology, University Clinic Tuebingen, Tübingen, Germany

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.

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