Evidence map›Paper›PMID 38429666›Full record

ArticleBMC medical imaging2024

Craniofacial phenotyping with fetal MRI: a feasibility study of 3D visualisation, segmentation, surface-rendered and physical models.

Jacqueline Matthew, Alena Uus, Leah De Souza, Robert Wright, Abi Fukami-Gartner, Gema Priego, Carlo Saija, Maria Deprez, Alexia Egloff Collado, Jana Hutter and 5 more

Abstract read
In one paragraph

Article in BMC medical imaging, 2024. 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.

  1. Review
  2. Article
  3. Review
  4. Automated Craniofacial Biometry with 3D T2w Fetal MRI.medRxiv : the preprint server for health sciences · 2024
    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

15 authors.

Jacqueline Matthew *School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK. jacqueline.matthew@kcl.ac.uk.
Alena Uus *School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.
Leah De SouzaSchool of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.
Robert WrightSchool of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.
Abi Fukami-GartnerSchool of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.
Gema PriegoSchool of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.
Carlo SaijaSchool of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.
Maria DeprezSchool of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.
Alexia Egloff ColladoSchool of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.
Jana HutterSchool of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.
Lisa StorySchool of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.
Christina MalamateniouDivision of Midwifery and Radiography, City University of London, London, UK.
Kawal RhodeSchool of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.
Jo HajnalSchool of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.
Mary A RutherfordSchool of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.

Funding

Structure and function of the placenta from implantation to delivery: a next generation MRI approachU01HD087202 · NICHD · KING'S COLLEGE LONDON · PI ALEXANDER, DANIEL, ALJABAR, PAUL · 2015 to 2015
$3.2M
Medical Research Council MR/T018119/1Medical Research Council MR/W006251/1NICHD NIH HHS U01 HD087202
6 · The paper itself

Abstract

This study explores the potential of 3D Slice-to-Volume Registration (SVR) motion-corrected fetal MRI for craniofacial assessment, traditionally used only for fetal brain analysis. In addition, we present the first description of an automated pipeline based on 3D Attention UNet trained for 3D fetal MRI craniofacial segmentation, followed by surface refinement. Results of 3D printing of selected models are also presented.Qualitative analysis of multiplanar volumes, based on the SVR output and surface segmentations outputs, were assessed with computer and printed models, using standardised protocols that we developed for evaluating image quality and visibility of diagnostic craniofacial features. A test set of 25, postnatally confirmed, Trisomy 21 fetal cases (24-36 weeks gestational age), revealed that 3D reconstructed T2 SVR images provided 66-100% visibility of relevant craniofacial and head structures in the SVR output, and 20-100% and 60-90% anatomical visibility was seen for the baseline and refined 3D computer surface model outputs respectively. Furthermore, 12 of 25 cases, 48%, of refined surface models demonstrated good or excellent overall quality with a further 9 cases, 36%, demonstrating moderate quality to include facial, scalp and external ears. Additional 3D printing of 12 physical real-size models (20-36 weeks gestational age) revealed good/excellent overall quality in all cases and distinguishable features between healthy control cases and cases with confirmed anomalies, with only minor manual adjustments required before 3D printing.Despite varying image quality and data heterogeneity, 3D T2w SVR reconstructions and models provided sufficient resolution for the subjective characterisation of subtle craniofacial features. We also contributed a publicly accessible online 3D T2w MRI atlas of the fetal head, validated for accurate representation of normal fetal anatomy.Future research will focus on quantitative analysis, optimizing the pipeline, and exploring diagnostic, counselling, and educational applications in fetal craniofacial assessment.

Indexed as

FetusMagnetic Resonance ImagingFeasibility StudiesGestational AgeHumansImage Processing, Computer-AssistedImaging, Three-DimensionalScalp3D printingAutomated segmentationCraniofacial featuresFace renderingFetal MRISlice to volume reconstruction

Identifiers

PMID38429666
PMCPMC10905839

What OpenQuestion holds

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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.