Browsing by Author "Marjanska, Malgorzata"

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    10.5 T CRT-MRSI metabolic, spectral and quality maps
    (2024-10-01) Marjanska, Malgorzata; Bogner, Wolfgang; Hingerl, Lukas; Strasser, Bernhard; gosia@umn.edu; Marjanska, Malgorzata; University of Minnesota, Center for Magnetic Resonance Research
    Magnetic resonance spectroscopy data measured from a brain of a healthy volunteer at 10. 5 tesla. These data show the metabolic maps, CRLB maps and LCModel fits, input data and fitted baseline in nifti-format. The volunteer was measured three different resolutions (4.40 mm³ isotropic, 3.44 mm³ isotropic, and 2.75 mm³ isotropic) using a concentric ring trajectory (CRT) FID-based sequence, reconstructed using a discrete Fourier transform, and fitted with LCModel (for more details, see below). The metabolic maps are in auxilliary units (a.u.) and are not in physical units. All these files can be viewed with freeview of freesurfer 7.1.1. It is being released to allow researchers to fully appreciate these 3D datasets, which are hard to otherwise visualize.
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    MRS fitting challenge data setup by ISMRM MRS study group
    (2021-04-16) Marjanska, Malgorzata; Deelchand, Dinesh K; Kreis, Roland; gosia@umn.edu; Marjanska, Malgorzata
    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 γ-aminobutyric acid, glutathione and macromolecules, and inclusion of artifacts and lipid signals to mimic tumor spectra were included as challenges to be coped with.