Evidence map›Paper›PMID 31721294›Full record

ArticleMagnetic resonance in medicine2020

Intra-session and inter-subject variability of 3D-FID-MRSI using single-echo volumetric EPI navigators at 3T.

Philipp Moser, Korbinian Eckstein, Lukas Hingerl, Michael Weber, Stanislav Motyka, Bernhard Strasser, Andre van der Kouwe, Simon Robinson, Siegfried Trattnig, Wolfgang Bogner

Open access · hybridAbstract read
In one paragraph

Article in Magnetic resonance in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
2.7field-weighted citation impact, top 10% of its field
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

22 citing papers in PubMed, 40 citations in OpenAlex.

  1. Review
  2. Correcting motion-related BScientific reports · 2026
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  4. Accelerated Navigator for Rapid ∆BNMR in biomedicine · 2025
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  7. Accelerated 3D metabolite TMagnetic resonance in medicine · 2024
    Article
  8. BMagnetic resonance in medicine · 2024
    Article
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  12. Nature biomedical engineering · 2023
    Article
  13. Reproducibility of 3D MRSI for imaging human brain glucose metabolism using direct (medRxiv : the preprint server for health sciences · 2023
    Article
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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

10 authors at 3 institutions in 2 countries.

Philipp MoserHigh-Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.ORCID 0000-0002-9717-6197
Korbinian EcksteinHigh-Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Lukas HingerlHigh-Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Michael WeberDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Stanislav MotykaHigh-Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Bernhard StrasserHigh-Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.ORCID 0000-0001-9542-3855
Andre van der KouweAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
Simon RobinsonHigh-Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.ORCID 0000-0001-7463-5162
Siegfried TrattnigHigh-Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Wolfgang BognerHigh-Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Medical University of Vienna · ATHarvard University · USChristian Doppler Laboratory for Thermoelectricity · AT

Funding

Austrian Science Fund FWF KLI 718Austrian Science Fund FWF P 30701
6 · The paper itself

Abstract

purposeIn this study, we demonstrate the first combination of 3D FID proton MRSI and spatial encoding via concentric-ring trajectories (CRTs) at 3T. FID-MRSI has many benefits including high detection sensitivity, in particular for J-coupled metabolites (e.g., glutamate/glutamine). This makes it highly attractive, not only for clinical, but also for, potentially, functional MRSI. However, this requires excellent reliability and temporal stability. We have, therefore, augmented this 3D-FID-MRSI sequence with single-echo, imaging-based volumetric navigators (se-vNavs) for real-time motion/shim-correction (SHMOCO), which is 2× quicker than the original double-echo navigators (de-vNavs), hence allowing more efficient integration also in short-TR sequences.

methodsThe tracking accuracy (position and B

resultsPhantom and in vivo scans showed highly consistent tracking performance for se-vNavs compared to the original de-vNavs, but lower frequency drift. Up to ~30% better intra-subject CVs were obtained for SHMOCO (P < 0.05), with values of 9.3/6.9/6.5/7.8% over the full VOI for Glx/tNAA/tCho/m-Ins ratios to tCr. ICCs were good-to-high (91% for Glx/tCr in motor cortex), whereas the inter-subject variability was ~11-19%.

conclusionReal-time motion/shim corrected 3D-FID-MRSI with time-efficient CRT-sampling at 3T allows reliable, high-resolution metabolic imaging that is fast enough for clinical use and even, potentially, for functional MRSI.

Indexed as

BrainHeadHumansPhantoms, ImagingReproducibility of Resultsconcentric ringsdynamic functional magnetic resonance spectroscopic imagingintra-subject reproducibilityreal time motion correctionreliability

Identifiers

PMID31721294
PMCPMC7065144
OpenAlexW2983303456

What OpenQuestion holds

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Registered trials

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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.