This readme.txt file was generated on 20210518 by DRUM curator & Deelchand, Dinesh ------------------- GENERAL INFORMATION ------------------- 1. Title of Dataset MRS fitting challenge data setup by ISMRM MRS study group 2. Author Information Principal Investigator Contact Information Name: Marjanska, Malgorzata Institution: University of Minnesota Department of Radiology Email: gosia@umn.edu Associate or Co-investigator Contact Information Name: Deelchand, Dinesh K Associate or Co-investigator Contact Information Name: Kreis, Roland 3. Date of data collection 2016-03-01 to 2016-03-01 4. Geographic location of data collection Lake Constance, Germany ISMRM Workshop on MR Spectroscopy: From Current Best Practice to Latest Frontiers https://www.ismrm.org/workshops/Spectroscopy16/overview.htm 5. Information about funding sources that supported the collection of the data National Institutes of Health -------------------------- SHARING/ACCESS INFORMATION -------------------------- 1. Licenses/restrictions placed on the data: CC0 1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/ 2. Links to publications that cite or use the data: Clarke WT, Stagg CJ, Jbabdi S. FSL-MRS: An end-to-end spectroscopy analysis package. Magn Reson Med. 2021 Jun;85(6):2950-2964. doi: 10.1002/mrm.28630. Epub 2020 Dec 6. PMID: 33280161; PMCID: PMC7116822. https://pubmed.ncbi.nlm.nih.gov/33280161/ Das, D., Coello, E., Schulte, R. F., & Menze, B. H. (2017, September). Quantification of metabolites in magnetic resonance spectroscopic imaging using machine learning. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 462-470). Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-319-66179-7_53 3. Links to other publicly accessible locations of the data: https://www.ismrm.org/workshops/Spectroscopy16/mrs_fitting_challenge/ 4. Recommended citation for the data: Marjanska, Malgorzata; Deelchand, Dinesh K; Kreis, Roland. (2021). MRS fitting challenge data setup by ISMRM MRS study group. Retrieved from the Data Repository for the University of Minnesota, https://doi.org/10.13020/kw61-3j13. --------------------- DATA & FILE OVERVIEW --------------------- 1. File List A. Filename: 2016_MRS_fitting_challenge.zip Short description: 28 datasets, basis sets, Cr dataset for testing scaling, and ground truth values for all datasets B. Filename: Directory tree for 2016_MRS_fitting_challenge.txt Short description: Directory tree of files contained within 2016_MRS_fitting_challenge.zip 2. Relationship between files: Directory tree maps the files contained within the zip file -------------------------- METHODOLOGICAL INFORMATION -------------------------- 1. Description of methods used for collection/generation of data: Fitting of the magnetic resonance spectroscopy (MRS) data plays an important role in the quantification of metabolite concentrations. A number of commercial and home-built packages are available and used by the MRS community to fit spectra. The question arose whether any one of these packages was superior to the others or whether they all perform similarly if appropriately used. Hence, in preparation for a workshop of the ISMRM MRS study group on MR Spectroscopy: from Current Best Practice to Latest Frontiers, which took place in August 2016, it was decided by the organizing committee, that this question should be tackled by a fitting challenge open to everybody, where a set of spectra would be evaluated. For this purpose, synthetic MRS data were generated for 28 datasets. Short-echo time PRESS spectra were simulated using ideal pulses for the common metabolites at mostly near-normal brain concentrations. A macromolecular contribution was also included. Modulations of signal-to-noise ratio (SNR), lineshape type and width, concentrations of gamma-aminobutyric acid, glutathione and macromolecules, and inclusion of artifacts and lipid signals to mimic tumor spectra were included as challenges to be coped with. 2. Methods for processing the data: 28 datasets are provided in three formats: 1. text file (column 1: real part of water suppressed FID; column 2: imaginary part of water suppressed FID; column 3: real part of water FID; column 4: imaginary part of water FID) 2. LCModel (.RAW and .h2o) 3. jMRUI (ASCII txt formats for both water suppressed [WS] and water spectra) The metabolite basis sets with macromolecular baseline are provided in these four formats: 1. text file (column 1: real part of FID; column 2: imaginary part of FID) for each metabolite and macromolecular baseline (MMBL) 2. text file with no reference peak at 0 ppm (column 1: real part of FID; column 2: imaginary part of FID) for each metabolite and MMBL 3. LCModel (.RAW) for each metabolite and MMBL and .BASIS 4. jMRUI (ASCII txt file format) for each metabolite and MMBL Cr scaling. An additional dataset, Cr_10mM_test_water_scaling (text, LCModel, jMRUI), is provided for testing water scaling. This dataset contains a Cr spectrum which - with correct water scaling - should come out to be 10.83 mM. metabolite concentration = (relaxation corrected water concentration) * (metabolite relaxation correction) * metabolite area / water area relaxation corrected water concentration = 1 mol / 18.015 g * 0.6 * 0.78 g/ml * exp(-30 ms/110 ms) + 1 mol/18.015 g * 0.4 *0.65 g/ml * exp(-30 ms/80 ms) = 29697 mM metabolite relaxation correction = 1/(exp(-30 ms/160 ms)) = 1.206 The following parameters describe the simulated data: Frequency = 123.2 MHz Sequence: PRESS TE = 30 ms, TE1 = 11 ms, TE2 = 19 ms TR >> T1’s Spectral width = 4000 Hz Number of points = 2048 Tissue content: GM = 60%, WM = 40% Water content: GM = 0.78 g/ml, WM = 0.65 g/ml T2 of water: GM = 110 ms, WM = 80 ms T2 of metabolites = 160 ms All metabolites datasets were generated using ideal pulses except for MMBL which was obtained experimentally. Glucose was simulated using both anomers, 0.36 alpha-glucose and 0.64 beta-glucose. The datasets may contain some or all of the metabolites listed among the basis spectra. In addition, lipid resonances and artifacts may be present. 3. Instrument- or software-specific information needed to interpret the data: Provided ASCII files can be opened with a text editor or any programming language (MATLAB, python, C++) and the corresponding FID constructed for further analyzed using commercial and home-built fitting packages. Text files written in jMRUI file format are also provided which can be used as input to jMRUI fitting software (http://www.jmrui.eu/). Similarly, RAW/H2O files are provided which can be used as input to LCModel software (http://s-provencher.com/lcmodel.shtml). 4. Standards and calibration information, if appropriate: The dataset, Cr_10mM_test_water_scaling (text, LCModel, jMRUI), is provided for testing water scaling, where the Cr concentration is 10.83 mM. 5. Environmental/experimental conditions: MRS spectra contain common metabolites at mostly near-normal brain concentrations. 6. People involved with sample collection, processing, analysis and/or submission: Dinesh K. Deelchand, Ph.D. Malgorzata Marjanska, Ph.D. Roland Kreis, Ph.D. Johannes Slotboom, Ph.D.: a spectrum from a patient with a brain tumor Christine Bolliger, PhD.: data used to create the macromolecular spectrum,