Evidence map›Paper›PMID 42231525›Full record

ArticleMagnetic resonance in medicine2026

Reducing Motion Artifact in High Resolution 7 T MRI Using the Magnetic Resonance Minimal Motion ("MR-MinMo") Head Stabilization Device.

Jyoti Mangal, Simon Richardson, Yannick Brackenier, Matthew Gardner, Pierluigi Di Cio, Chiara Casella, Shaihan Malik, Jo Hajnal, Martina F Callaghan, Fred Dick and 1 more

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. 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

11 authors.

Jyoti MangalResearch Department of Imaging Physics and Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.ORCID https://orcid.org/0009-0002-1394-6447
Simon RichardsonDivision of Psychology and Language Sciences, University College London, London, UK.
Yannick BrackenierResearch Department of Imaging Physics and Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.ORCID https://orcid.org/0000-0002-9210-5939
Matthew GardnerDivision of Psychology and Language Sciences, University College London, London, UK.
Pierluigi Di CioResearch Department of Imaging Physics and Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Chiara CasellaResearch Department of Imaging Physics and Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Shaihan MalikResearch Department of Imaging Physics and Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.ORCID https://orcid.org/0000-0001-8925-9032
Jo HajnalResearch Department of Imaging Physics and Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Martina F CallaghanWellcome Centre for Human Neuroimaging, University College London, London, UK.ORCID https://orcid.org/0000-0003-0374-1659
Fred DickDivision of Psychology and Language Sciences, University College London, London, UK.
David W CarmichaelResearch Department of Imaging Physics and Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.

Funding

EPSRC smart medical imaging CDT studentship (Dr Jyoti Mangal, EP/S022104/1)National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College LondonWellcome/EPSRC Centre for Medical Engineering [WT203148/Z/16/Z]
6 · The paper itself

Abstract

purposeTo evaluate the effectiveness of the MR-MinMo head stabilization device in mitigating motion artifacts during long-duration, high-resolution 7 T MRI scans, with and without retrospective motion correction

methodsThe MR-MinMo was tested on seven pediatric and 12 adult healthy volunteers using 0.6 mm isotropic, 3D Multi-Echo Gradient Echo (ME-GRE) scans. A ∼10-min ME-GRE scan (linear sampling acceleration factor 2 × 2) and a ∼20-min scan (DISORDER sampling acceleration factor 1.4 × 1.4) were obtained. Both scans were acquired with and without the MR-MinMo in a 2 × 2 factorial study design. Qualitative and quantitative assessment of image quality of the first echo image was obtained via visual inspection and the normalized gradient squared (NGS) metric, respectively. A repeated measures ANOVA was used to determine individual and combined effects of the device and DISORDER images with retrospective motion correction on NGS values, with age as a binary factor (pediatric/adult). T2* maps were generated and the standard deviation of white matter R2*(=1/T2*) values assessed across conditions.

resultsThe MR-MinMo significantly reduced motion artifacts both visually and in terms of NGS scores, particularly in pediatric volunteers. There was a significant interaction between MR-MinMo and DISORDER motion correction, suggesting that MR-MinMo improved retrospective motion correction. T2* maps demonstrated improved visual appearance and reduced WM variance with the MR-MinMo.

conclusionThe MR-MinMo can improve image quality via motion reduction in high-resolution 7 T scans. By keeping motion within a correctable regime, the device can also improve the performance of retrospective motion correction methods.

Indexed as

ArtifactsBrainHeadMagnetic Resonance ImagingAdultChildEquipment DesignFemaleHead MovementsHumansImage EnhancementImage Processing, Computer-AssistedMaleMotionReproducibility of ResultsSensitivity and Specificityhead MRImotion reductionMRI hardwareultra‐high field MRI

Identifiers

PMID42231525
PMCPMC13327473

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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.