DeepCEST: 9.4 T Chemical exchange saturation transfer MRI contrast predicted from 3 T data – a proof of concept study. Issue 6 (25th February 2019) - Zaiss, Moritz; Deshmane, Anagha; Schuppert, Mark; Herz, Kai; Glang, Felix; Ehses, Philipp; Lindig, Tobias; Bender, Benjamin; Ernemann, Ulrike; Scheffler, Klaus
Abstract : Purpose: To determine the feasibility of employing the prior knowledge of well‐separated chemical exchange saturation transfer (CEST) signals in the 9.4 T Z‐spectrum to separate overlapping CEST signals acquired at 3 T, using a deep learning approach trained with 3 T and 9.4 T CEST spectral data from brains of the same subjects. Methods: Highly spectrally resolved Z‐spectra from the same volunteer were acquired by 3D‐snapshot CEST MRI at 3 T and 9.4 T at low saturation power of B1 = 0.6 µT. The volume‐registered 3 T Z‐spectra‐stack was then used as input data for a three layer deep neural network with the volume‐registered 9.4 T fitted parameter stack as target data. Results: An optimized neural net architecture could be found and verified in healthy volunteers. The gray‐/white‐matter contrast of the different CEST effects was predicted with only small deviations (Pearson R = 0.89). The 9.4 T prediction was less noisy compared to the directly measured CEST maps, although at the cost of slightly lower tissue contrast. Application to an unseen tumor patient measured at 3 T and 9.4 T revealed that tumorous tissue Z‐spectra and corresponding hyper‐/hypointensities of different CEST effects can also be predicted (Pearson R = 0.84). Conclusion: The 9.4 T CEST signals acquired at low saturation power can be accurately estimated from CEST imaging at 3 T using a neural network trained with coregistered 3 T and 9.4 T data of healthy subjects. The deepCEST approach generalizes to Z‐spectra of tumor areas and might indicate whether additional ultrahigh‐field (UHF) scans will be beneficial.