Evidence map›Paper›PMID 40152515›Full record

ArticleMagnetic resonance in medicine2025

Motion-corrected brain MRI at ultralow field (64 mT).

Yannick Brackenier, Rui Pedro Teixeira, Lucilio Cordero-Grande, Emil Ljungberg, Niall J Bourke, Tomoki Arichi, Sean Deoni, Steve C R Williams, Joseph V Hajnal

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2025. 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. Motion-corrected brain MRI at ultralow field (64 mT).Magnetic resonance in medicine · 2025
    Article
  2. Review
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

9 authors.

Yannick BrackenierBiomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.ORCID 0000-0002-9210-5939
Rui Pedro TeixeiraHyperfine, Guilford, Connecticut, USA.
Lucilio Cordero-GrandeBiomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BNN, ISCIII, Madrid, Spain.
Emil LjungbergDepartment of Medical Radiation Physics, Lund University, Lund, Sweden.ORCID 0000-0003-1456-7967
Niall J BourkeDepartment of Neuroimaging, King's College London, London, United Kingdom.
Tomoki ArichiBiomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Sean DeoniBill & Melinda Gates Foundation, Seattle, Washington, USA.
Steve C R WilliamsDepartment of Neuroimaging, King's College London, London, United Kingdom.
Joseph V HajnalBiomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.

Funding

Bill & Melinda Gates FoundationMaudsley NHS Foundation TrustNIHR Maudsley Biomedical Research Centre at South LondonWellcome EPSRC Centre for Medical Engineering WT203148/Z/16/ZWellcome Trust
6 · The paper itself

Abstract

purposeThe study investigates the feasibility of applying a retrospective motion-correction technique to ultralow-field (ULF) MRI data to improve reconstructed image quality when there is patient motion, which is likely to be a critical challenge in portable, point-of-care imaging. THEORY &

methodsThe study tests alignedSENSE, an iterative motion correction and reconstruction method with SENSE, for ULF MRI, with additional corrections to estimate and correct within-scan phase variations. The method was applied to in vivo brain volumetric data acquired from five healthy volunteers using a 64 mT portable MRI scanner. The volunteers underwent different motion types and levels, with corrections evaluated using both visual and quantitative metrics.

resultsMotion correction, particularly when within-scan phase variations are also accounted for, showed clear improvements in image quality. Without making any assumptions about the origin of these phase variations, incorporating them into the signal model and jointly estimating with the image/motion parameters increases the data consistency. This improves the image quality and motion parameters across various levels of induced motion. Quantitative analysis confirmed that the combined motion and phase corrections outperformed conventional parallel imaging reconstruction, although extreme motion cases still pose challenges.

conclusionThe study demonstrates that alignedSENSE motion-correction techniques can be effectively applied to ULF MRI systems. The results suggest that these techniques can substantially enhance image quality without increasing scan time, which could make ULF MRI more clinically viable for point-of-care deployment.

Indexed as

BrainImage Processing, Computer-AssistedMagnetic Resonance ImagingAdultAlgorithmsArtifactsFemaleHealthy VolunteersHumansMaleMotionReproducibility of Resultsbrainmotion correctionreconstructionultralow field

Identifiers

PMID40152515
PMCPMC12137760

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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.