Evidence map›Paper›PMID 40065547›Full record

ArticleMagnetic resonance in medicine2025

Real-time fetal brain and placental T2* mapping at 0.55T MRI.

Jordina Aviles Verdera, Sara Neves Silva, Kelly M Payette, Raphael Tomi-Tricot, Megan Hall, Lisa Story, Shaihan J Malik, Joseph V Hajnal, Mary A Rutherford, Jana Hutter

Abstract read
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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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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.

Jordina Aviles VerderaEarly Life Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.ORCID 0009-0007-7575-4244
Sara Neves SilvaEarly Life Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.ORCID 0009-0009-7520-081X
Kelly M PayetteEarly Life Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.ORCID 0000-0001-7041-0150
Raphael Tomi-TricotEarly Life Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Megan HallWomen's Health, GSTT, London, UK.
Lisa StoryWomen's Health, GSTT, London, UK.
Shaihan J MalikEarly Life Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.ORCID 0000-0001-8925-9032
Joseph V HajnalEarly Life Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Mary A RutherfordEarly Life Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Jana HutterEarly Life Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.ORCID 0000-0003-3476-3500

Funding

Deutsche Forschungs Gemeinschaft (DFG) and Heisenberg 502024488European Research Council Starting Grant 101165242MRC MR/W019469/1MRC MR/X010007/1NIHR Advanced Fellowship NIHR3016640UKRI DTP 2604718UKRI FLF MR/T018119/1Wellcome EPSRC Centre for Medical Engineering WT203148/Z/16/ZWellcome TrustWellcome Trust Fellowship WT201526/Z/16/Z
6 · The paper itself

Abstract

purposeTo provide real-time, organ-specific quantitative information - specifically placental and fetal brain T2 * - to optimize and personalize fetal MRI examinations.

methodsA low-latency setup enables real-time processing, including segmentation, T2* fitting, and centile calculation. Two nnU-Nets were trained on 2 989 fetal brains, and 540 placental datasets for automatic segmentation. Normative T2* curves over gestation were derived from 88 healthy cases. Prospective testing included 50 fetal MRI scans: A validation cohort (10 exams with three intra-scan repetitions) and an evaluation cohort (40 participants). Validation was performed with Bland-Altman assessments and Dice coefficients between repetitions, manual/automatic segmentations, and online/offline quantification.

resultsT2* maps and centiles for the fetal brain and placenta were available in under one minute for all cases. The validation cohort showed robust reproducibility, with intra-scan mean T2* differences of 1.04, -3.17, and 5.07 ms for the fetal brain and -3.15, 4.74, and 2.45 ms for the placenta. Mean T2* differences between online and offline processing were 1.63 ms and 0.16 ms for the fetal brain and placenta, respectively. Dice coefficients were

conclusionsReal-time quantitative imaging supports personalized MR exams, optimizing sequence selection and working towards reducing recall rates. The ability to assess T2*, a potential biomarker for pregnancy complications, in real-time opens new clinical possibilities. Future research will apply this pipeline to pregnancies affected by preeclampsia and growth restriction and explore MR-guided fetal interventions.

Indexed as

BrainFetusMagnetic Resonance ImagingPlacentaAdultFemaleHumansImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedPregnancyPrenatal DiagnosisProspective StudiesReproducibility of Resultsfetal MRIlow costlow‐fieldrelaxometry

Identifiers

PMID40065547
PMCPMC12137763

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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.