Evidence map›Paper›PMID 37608939›Full record

ArticleArXiv2023

An automated pipeline for quantitative T2* fetal body MRI and segmentation at low field.

Kelly Payette, Alena Uus, Jordina Aviles Verdera, Carla Avena Zampieri, Megan Hall, Lisa Story, Maria Deprez, Mary A Rutherford, Joseph V Hajnal, Sebastien Ourselin and 2 more

Abstract readPreprint
In one paragraph

Article in ArXiv, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Kelly PayetteCentre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Alena UusCentre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Jordina Aviles VerderaCentre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Carla Avena ZampieriCentre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Megan HallCentre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Lisa StoryCentre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Maria DeprezCentre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Mary A RutherfordCentre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Joseph V HajnalCentre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Sebastien OurselinCentre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Raphael Tomi-TricotDepartment of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Jana HutterCentre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, 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/W019469/1NICHD NIH HHS U01 HD087202Wellcome Trust
6 · The paper itself

Abstract

Fetal Magnetic Resonance Imaging at low field strengths is emerging as an exciting direction in perinatal health. Clinical low field (0.55T) scanners are beneficial for fetal imaging due to their reduced susceptibility-induced artefacts, increased T2* values, and wider bore (widening access for the increasingly obese pregnant population). However, the lack of standard automated image processing tools such as segmentation and reconstruction hampers wider clinical use. In this study, we introduce a semi-automatic pipeline using quantitative MRI for the fetal body at low field strength resulting in fast and detailed quantitative T2* relaxometry analysis of all major fetal body organs. Multi-echo dynamic sequences of the fetal body were acquired and reconstructed into a single high-resolution volume using deformable slice-to-volume reconstruction, generating both structural and quantitative T2* 3D volumes. A neural network trained using a semi-supervised approach was created to automatically segment these fetal body 3D volumes into ten different organs (resulting in dice values > 0.74 for 8 out of 10 organs). The T2* values revealed a strong relationship with GA in the lungs, liver, and kidney parenchyma (R

Indexed as

Fetal MRILow fieldT2*

Identifiers

PMID37608939
PMCPMC10441444

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.