Title: Synthetic MR spectroscopy data
Description: Synthetic MR spectroscopy data for quantification purposes of use for model fitting and training neural networks. Brain metabolites with variation in concentration, shim, and background. For details see: doi: 10.1002/mrm.29561
Data Availability: 
Open
Contact: Kreis, Roland  
Contributor(s): Kreis, Roland  
Rizzo, Rudy 
DOI: https://doi.org/10.48620/229
Organization(s): Department for BioMedical Research (DBMR) 
Institute of Diagnostic and Interventional Neuroradiology 
Language(s): en
Subject(s): 600 - Technology > 610 - Medicine & health
Keyword(s): magnetic resonance spectroscopy
simulations
training data
Rights: CC-BY
BORIS Publications: Rizzo, Rudy; Dziadosz, Martyna; Kyathanahally, Sreenath P; Shamaei, Amirmohammad; Kreis, Roland (2023). Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias. Magnetic resonance in medicine, 89(5), pp. 1707-1727. Wiley-Liss 10.1002/mrm.29561
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