This is a list of single-voxel MRS datasets.
Basis sets for PRESS, STEAM and sLASER for multiple vendors
These basis sets were generated using the exact waveforms, timings and 128^3 spatial points to appropriately accommodate the sidebands for each vendor/sequence combination. More details about the acquisition can be found in the reference below.
|Basis sets for PRESS, STEAM and sLASER for multiple vendors|
|Developer||Karl Landheer, Kelley Swanberg, Christoph Juchem|
|Format||Both .RAW and .mat (INSPECTOR, readable in MATLAB) are provided. All files were generated using MATLAB|
|Sequence||PRESS, sLASER, STEAM|
|Credit||Please cite the following publication if you use this dataset: Landheer K, Swanberg, K, Juchem C. Magnetic resonance Spectrum simulator (MARSS), a novel software package for fast and computationally efficient basis set simulation. NMR Biomed. 2019, e4219. doi.org/10.1002/nbm.4129|
This repository contains a subset of a large-scale multi-vendor, multi-site collection of single-voxel MRS and structural MRI datasets that were acquired internationally across 26 research sites on 3T MRI scanners from the three major vendors (GE, Philips, Siemens). Each site acquired up to 12 datasets.
|Developer||Richard AE Edden|
|Format||SDAT, TWIX, P|
|Credit||If using these data, please cite: (Mikkelsen M et al. Big GABA: Edited MR spectroscopy at 24 research sites. NeuroImage 2017;159:32–45. doi:10.1016/j.neuroimage.2017.07.021), (Mikkelsen M et al. Big GABA II: Water-referenced edited MR spectroscopy at 25 research sites. NeuroImage 2019;191:537–548. doi:10.1016/j.neuroimage.2019.02.059), and (Povazan M et al. Comparison of multivendor single-voxel MR spectroscopy data acquired in healthy brain at 26 sites. Radiology 2020. doi:10.1148/radiol.2020191037), and acknowledge NIH grant R01 EB016089.|
Fitting challenge data, MRS Workshop 2016
Synthetic data mimicking single-voxel 3 T PRESS TE = 30 ms MRS spectra. Data were simulated using ideal pulses and the known metabolites concentrations in the human brain. The experimentally acquired macromolecular contribution was also used. The following things were modulated in the datasets: non-Lorentzian broadening, concentrations of GABA, GSH and macromolecules, signal-to-noise (SNR) variations, and artifacts. These data was used in the Fitting Challenge organized as part of the ISMRM Workshop on MR Spectroscopy: From Current Best Practice to Latest Frontiers, Lake Constance, Germany, 2016.
|Fitting challenge data, MRS Workshop 2016|
|Developer||Malgorzata Marjanska, Dinesh Deelchand, Roland Kreis|
|Format||text, LCModel (.RAW and .h2o), and jMRUI (ascii text)|
|Sequence||PRESS TE = 30 ms, simulated|
|Credit||Please acknowledge developers as listed above and list following URL: https://www.ismrm.org/workshops/Spectroscopy16/mrs_fitting_challenge/|
MM human brain spectra 9.4T - TE series / Fit Settings LCModel
This are TE series (TE = 24,32,40,52,60 ms) DIR semi-LASER macromolecular spectra. They were measured in the occipital lobe (in GM rich and WM voxels) of the human brain. The provided data are averages from 11 subjects. Additionally, the fitsettings and sample fits are provided. An explanation on how to set up the fit settings file is provided in the Readme.MD.
|MM human brain spectra 9.4T - TE series / Fit Settings LCModel|
|Credit||Please cite the following publication if you use the MM human brain spectra 9.4T dataset. Murali Manohar S, Borbath T, Wright AM, Soher B, Mekle R, Henning A. T2 relaxation times of macromolecules and metabolites in the human brain at 9.4 T. Magnetic resonance in medicine. 2020;84:542–58.|
MM Consensus Data Collection
This repository contains a number of pre-processed macromolecule (metabolite-nulled) spectra in Varian format (sometimes converted from Siemens format), acquired at different field strengths from different mammals, including human. These data are represented in Figure 1 of the recently published consensus on macromolecule acquisition and handling.
|MM Consensus Data Collection|
|Developer||Ivan Tkáč (4 and 7T, human; 9.4T rat, mouse, cat), Anke Henning (9.4T human), Cristina Cudalbu (14.1T, rat), Dinesh Deelchand (3T, human)|
|Format||Varian or Siemens converted in Varian|
|Sequence||STEAM, SPECIAL, sLASER|
|Credit||Please cite the following publications if you use data from this dataset collection: Tkáč et al., Magn Reson Med 2003; 50: 24-32 (MM 9.4T rat); Tkáč et al., Magn Reson Med 2004; 52: 478-484 (MM 9.4T mouse, cat); Tkáč et al., Magn Reson Med 2009; 62: 868-879 (MM and spectra 4T and 7T); Cudalbu et al., J Alzheimers Dis. 2012;31 Suppl 3:S101-15. doi: 10.3233/JAD-2012-120100 (MM 14.1T rat)|
Monte Carlo simulations of synthetic data
Monte Carlo simulation to investigate the relationship between CRLB and SD for all fitted parameters from linear combination modeling. More details about the acquisition can be found in the reference below.
|Monte Carlo simulations of synthetic data|
|Developer||Karl Landheer, Christoph Juchem|
|Format||.mat file (readable in MATLAB). Synthetic spectra were generated from MARSS, fitting was performed in INSPECTOR. Both MARSS and INSPECTOR are based in MATLAB.|
|Sequence||Synthesized spectra were simulated for a TE = 20.1 ms sLASER sequence|
|Credit||Please cite the following publication if you use this dataset: Landheer K, Juchem C. Are Cramér-Rao Lower Bounds an Accurate Estimate for Standard Deviations in In Vivo Magnetic Resonance Spectroscopy? NMR Biomed. [In press]|
9.4T MR Spectra from rat hippocampus with LCModel quantification and the corresponding basis set
This repository contains the LCModel quantifications of 9.4T spectra acquired in hippocampus from 7 rats. The spectra were quntified using six different DKNTMN (spline stiffness) values (0.1, 0.25, 0.4, 0.5, 1, 5). In the folder Control_files_Basis_set you can find all the control files used in this quantification along with the corresponding basis set (metabolites/simulated using NMRScopeB from jMRUI and in vivo parameters + full MM spectrum).
|9.4T MR Spectra from rat hippocampus with LCModel quantification and the corresponding basis set|
|Developer||Dunja Simicic and Cristina Cudalbu (CIBM/EPFL)|
|Format||Varian, .RAW, .mat, .txt, .pdf, .control, .basis|
|Credit||Please cite the Zenodo resource linked below if you use the dataset (publication in progress).|