DeepCEST: 9.4 T Chemical exchange saturation transfer MRI contrast predicted from 3 T data - a proof of concept study.

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.

journal_name

Magn Reson Med

authors

Zaiss M,Deshmane A,Schuppert M,Herz K,Glang F,Ehses P,Lindig T,Bender B,Ernemann U,Scheffler K

doi

10.1002/mrm.27690

subject

Has Abstract

pub_date

2019-06-01 00:00:00

pages

3901-3914

issue

6

eissn

0740-3194

issn

1522-2594

journal_volume

81

pub_type

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