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DeepCEST: 7T Chemical exchange saturation transfer MRI contrast inferred from 3T data via deep learning with uncertainty quantification
{The deepCEST approach enables to perform CEST experiments at a lower magnetic field strength and predict the contrasts of a higher field strength. This is possible through the application of a neural network, which was trained with low and high B1 Z-spectra acquired at 3T as input data, and as target data 5-pool-Lorentzian fitted amplitudes obtained from 7T spectra were used. The network included an uncertainty quantification to verify the reliability of the predicted images.}
@misc{item_3319883, title = {{DeepCEST: 7T Chemical exchange saturation transfer MRI contrast inferred from 3T data via deep learning with uncertainty quantification}}, booktitle = {{2021 ISMRM \& SMRT Annual Meeting \& Exhibition (ISMRM 2021)}}, abstract = {{The deepCEST approach enables to perform CEST experiments at a lower magnetic field strength and predict the contrasts of a higher field strength. This is possible through the application of a neural network, which was trained with low and high B1 Z-spectra acquired at 3T as input data, and as target data 5-pool-Lorentzian fitted amplitudes obtained from 7T spectra were used. The network included an uncertainty quantification to verify the reliability of the predicted images.}}, year = {2021}, slug = {item_3319883}, author = {Hunger, LE and German, A and Glang, F and Khakzar, KM and Dang, N and Mennecke, A and Maier, A and Laun, F and Zaiss, M} }