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DeepCEST: 9.4 T Chemical exchange saturation transfer MRI contrast predicted from 3 T data –a proof of concept study
{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 \textequals 0.6 $\micro$T. 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 \textequals 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 \textequals 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.}
@article{item_3030103, title = {{DeepCEST: 9.4 T Chemical exchange saturation transfer MRI contrast predicted from 3 T data \textendash a proof of concept study}}, journal = {{Magnetic Resonance in Medicine}}, 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 \textequals 0.6 $\micro$T. 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 \textequals 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 \textequals 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.}}, volume = {81}, number = {6}, pages = {3901--3914}, publisher = {Wiley-Liss}, address = {New York}, year = {2019}, slug = {item_3030103}, author = {Zaiss, M and Deshmane, A and Schuppert, M and Herz, K and Glang, F and Ehses, P and Lindig, T and Bender, B and Ernemann, U and Scheffler, K} }