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

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...