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Machine Learning accelerates and stabilizes selective CEST fitting at 3T
{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 \textpercent reduction in computing time, allowing for quick estimation of NLLS initial conditions or initial online reconstruction of spectrally selective CEST contrasts.}
@misc{item_3054588, title = {{Machine Learning accelerates and stabilizes selective CEST fitting at 3T}}, booktitle = {{ISMRM 27th Annual Meeting \& Exhibition}}, 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 \textpercent reduction in computing time, allowing for quick estimation of NLLS initial conditions or initial online reconstruction of spectrally selective CEST contrasts.}}, year = {2019}, slug = {item_3054588}, author = {Deshmane, A and Zaiss, M and Herz, K and Bender, B and Lindig, T and Scheffler, K} }