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

• Published in: Magnetic resonance in medicine Vol. 81; no. 6; pp. 3901 - 3914
• Main Authors: Zaiss, Moritz, Deshmane, Anagha, Schuppert, Mark, Herz, Kai, Glang, Felix, Ehses, Philipp, Lindig, Tobias, Bender, Benjamin, Ernemann, Ulrike, Scheffler, Klaus
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.06.2019
• Subjects: Artificial neural networks; Brain - diagnostic imaging; Brain Neoplasms - diagnostic imaging; Contrast Media; Data processing; Exchanging; Feasibility studies; Humans; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Neural networks; Neural Networks, Computer; Organic chemistry; Predictions; Proof of Concept Study; Saturation; Spectra; Substantia alba; Tumors; Very high frequencies
• ISSN: 0740-3194; 1522-2594; 1522-2594
• DOI: 10.1002/mrm.27690

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.