Evidence map›Paper›PMID 40180124›Full record

ArticleJournal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance2025

Automatic flow planning for fetal cardiovascular magnetic resonance imaging.

Sara Neves Silva, Tomas Woodgate, Sarah McElroy, Michela Cleri, Kamilah St Clair, Jordina Aviles Verdera, Kelly Payette, Alena Uus, Lisa Story, David Lloyd and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance, 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. Review
  2. Article
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.

Sara Neves SilvaResearch Department for Early Life Imaging, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Research Department for Medical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Tomas WoodgateResearch Department for Medical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Sarah McElroyResearch Department for Early Life Imaging, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; MR Research Collaborations, Siemens Healthcare Limited, Camberley, United Kingdom.
Michela CleriResearch Department for Medical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; London Collaborative Ultra high field System (LoCUS), King's College London, London, UK.
Kamilah St ClairResearch Department for Early Life Imaging, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Research Department for Medical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Jordina Aviles VerderaResearch Department for Early Life Imaging, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Research Department for Medical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Kelly PayetteResearch Department for Early Life Imaging, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Research Department for Medical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Alena UusResearch Department for Early Life Imaging, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Research Department for Medical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Lisa StoryResearch Department for Medical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Department of Women & Children's Health, King's College London, London, UK.
David LloydResearch Department for Medical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Mary A RutherfordResearch Department for Early Life Imaging, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Research Department for Medical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Joseph V HajnalResearch Department for Early Life Imaging, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Research Department for Medical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Kuberan PushparajahResearch Department for Medical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Jana HutterResearch Department for Early Life Imaging, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Research Department for Medical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Smart Imaging Lab, Radiological Institute, University Hospital Erlangen, Erlangen, Germany.

Funding

Wellcome Trust
6 · The paper itself

Abstract

backgroundWidening access to fetal flow imaging by automating real-time planning of two-dimensional (2D) phase-contrast flow imaging (OWL).

methodsTwo subsequent deep learning networks for fetal body localization and cardiac landmark detection on a coronal whole-uterus scan were trained on 167 and 71 fetal datasets, respectively, and implemented for real-time automatic planning of phase-contrast sequences. OWL was evaluated retrospectively in ten datasets and prospectively in seven fetal subjects (36+3-39+3 gestational weeks), with qualitative and quantitative comparisons to manual planning.

resultsOWL was successfully implemented in 6/7 prospective cases. Fetal body localization achieved a Dice score of 0.94±0.05, and cardiac landmark detection accuracies were 5.77±2.91 mm (descending aorta), 4.32±2.44 mm (spine), and 4.94±3.82 mm (umbilical vein). Planning quality was 2.73/4 (automatic) and 3.0/4 (manual). Indexed flow measurements differed by -1.8% (range -14.2% to 14.9%) between OWL and manual planning.

conclusionOWL achieved real-time automated planning of 2D phase-contrast cardiovascular magnetic resonance (CMR) for two major vessels, demonstrating feasibility at 0.55T with potential generalization across field strengths, extending access to this modality beyond specialized centers.

Indexed as

Deep LearningFetal HeartImage Interpretation, Computer-AssistedMagnetic Resonance ImagingPrenatal DiagnosisAutomationBlood Flow VelocityFemaleGestational AgeHumansPredictive Value of TestsPregnancyProspective StudiesRegional Blood FlowReproducibility of ResultsRetrospective StudiesFetal cardiac MRIMotion correctionPhase-contrastTracking

Identifiers

PMID40180124
PMCPMC12182827

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.