Evidence map›Paper›PMID 28643447›Full record

ArticleMagnetic resonance in medicine2018

Simultaneous mapping of metabolites and individual macromolecular components via ultra-short acquisition delay

Michal Považan, Bernhard Strasser, Gilbert Hangel, Eva Heckova, Stephan Gruber, Siegfried Trattnig, Wolfgang Bogner

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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  12. Neuroimaging at 7 Tesla: a pictorial narrative review.Quantitative imaging in medicine and surgery · 2022
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  13. In vivo Human MR Spectroscopy Using a Clinical Scanner: Development, Applications, and Future Prospects.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022
    Review
  14. Article
  15. Article
  16. In vivo macromolecule signals in rat brainMagnetic resonance in medicine · 2021
    Article
  17. Review
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  19. Article
  20. Contribution of macromolecules to brainNMR in biomedicine · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Michal PovažanHigh Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria.
Bernhard StrasserHigh Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria.
Gilbert HangelHigh Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria.
Eva HeckovaHigh Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria.
Stephan GruberHigh Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria.
Siegfried TrattnigHigh Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria.
Wolfgang BognerHigh Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria.

Funding

Austrian Science Fund FWF KLI 61
6 · The paper itself

Abstract

purposeShort-echo-time proton MR spectra at 7T feature nine to 10 distinct macromolecule (MM) resonances that overlap with the signals of metabolites. Typically, a metabolite-nulled in vivo MM spectrum is included in the quantification`s prior knowledge to provide unbiased metabolite quantification. However, this MM model may fail if MMs are pathologically altered. In addition, information about the individual MM peaks is lost. In this study, we aimed to create an improved MM model by parameterization of the in vivo MM spectrum into individual components, and to use this new model to quantify free induction decay MR spectroscopic imaging (FID-MRSI) data.

methodsThe measured in vivo MM spectrum was parameterized using advanced method for accurate, robust, and efficient spectral fitting (AMARES) and Hankel-Lanczos singular value decomposition algorithms from which six different MM models were derived. Soft constraints were applied to avoid over-parameterization. All MM models were combined with simulated metabolite spectra to form complete basis sets. FID-MRSI data from 14 healthy volunteers were quantified via LCModel, and the results were compared between all basis sets.

resultsThe MM model using nine individual AMARES-parameterized MM components with additional soft constraints achieved the most reliable results. Nine MMs and seven metabolites were mapped simultaneously over the whole slice.

conclusionThe proposed MM model may facilitate studies that involve patients with pathologically altered MMs. Magn Reson Med 79:1231-1240, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Indexed as

AdultAlgorithmsBrainBrain ChemistryBrain MappingFemaleHumansImage Processing, Computer-AssistedMacromolecular SubstancesMagnetic Resonance ImagingMaleMolecular ImagingMacromolecular Substanceshuman brainmacromoleculesMR spectroscopic imagingparameterization

Identifiers

PMID28643447
PMCPMC5811892

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.