Evidence map›Paper›PMID 40534653›Full record

ArticleFrontiers in neuroimaging2025

AI improves consistency in regional brain volumes measured in ultra-low-field MRI and 3T MRI.

Kh Tohidul Islam, Shenjun Zhong, Parisa Zakavi, Helen Kavnoudias, Shawna Farquharson, Gail Durbridge, Markus Barth, Andrew Dwyer, Katie L McMahon, Paul M Parizel and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neuroimaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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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

14 authors.

Kh Tohidul IslamMonash Biomedical Imaging, Monash University, Clayton, VIC, Australia.
Shenjun ZhongMonash Biomedical Imaging, Monash University, Clayton, VIC, Australia.
Parisa ZakaviMonash Biomedical Imaging, Monash University, Clayton, VIC, Australia.
Helen KavnoudiasDepartment of Radiology, The Alfred, Melbourne, VIC, Australia.
Shawna FarquharsonAustralian National Imaging Facility, Brisbane, QLD, Australia.
Gail DurbridgeHerston Imaging Research Facility, University of Queensland, Brisbane, QLD, Australia.
Markus BarthSchool of Electrical Engineering and Computer Science, University of Queensland, Brisbane, QLD, Australia.
Andrew DwyerSouth Australian Health and Medical Research Institute, Adelaide, SA, Australia.
Katie L McMahonSchool of Clinical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia.
Paul M ParizelDavid Hartley Chair of Radiology, Royal Perth Hospital, Perth, WA, Australia.
Richard McIntyreMonash Biomedical Imaging, Monash University, Clayton, VIC, Australia.
Gary F EganMonash Biomedical Imaging, Monash University, Clayton, VIC, Australia.
Meng LawDepartment of Radiology, The Alfred, Melbourne, VIC, Australia.
Zhaolin ChenMonash Biomedical Imaging, Monash University, Clayton, VIC, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study compares volumetric measurements of various brain regions using different magnetic resonance imaging (MRI) modalities and deep learning models, specifically 3T MRI, ultra-low field (ULF) MRI at 64mT, and AI-enhanced ULF MRI using SynthSR and HiLoResGAN. The aim is to evaluate the alignment and agreement among field strengths and ULF MRI with and without AI. Descriptive statistics, paired

Indexed as

accessible MRIbrain volume measurementdeep learning in neuroimagingquantitative MRI analysisultra-low-field MRI

Identifiers

PMID40534653
PMCPMC12174951

What OpenQuestion holds

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LicenceCC BY
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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.