Evidence map›Paper›PMID 35649314›Full record

ArticleMedical image analysis2022

Automated 3D reconstruction of the fetal thorax in the standard atlas space from motion-corrupted MRI stacks for 21-36 weeks GA range.

Alena U Uus, Irina Grigorescu, Milou P M van Poppel, Johannes K Steinweg, Thomas A Roberts, Mary A Rutherford, Joseph V Hajnal, David F A Lloyd, Kuberan Pushparajah, Maria Deprez

Abstract read
In one paragraph

Article in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Article
  2. Article
  3. Observational
  4. Article
  5. Motion Mitigation Techniques for Abdominal and Cardiac MR Imaging.Journal of magnetic resonance imaging : JMRI · 2026
    Review
  6. Article
  7. Review
  8. Fetal cardiac masses.Pediatric radiology · 2025
    Review
  9. Article
  10. Review
  11. Article
  12. Article
  13. Total and Regional Brain Volumes in Fetuses With Congenital Heart Disease.Journal of magnetic resonance imaging : JMRI · 2024
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. An automated pipeline for quantitative T2* fetal body MRI and segmentation at low field.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023
    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

10 authors.

Alena U UusSchool of Imaging Sciences & Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK. Electronic address: alena.uus@kcl.ac.uk.
Irina GrigorescuSchool of Imaging Sciences & Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK.
Milou P M van PoppelSchool of Imaging Sciences & Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK; Department of Congenital Heart Disease, Evelina London Children's Hospital, London, SE1 7EH, UK.
Johannes K SteinwegSchool of Imaging Sciences & Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK; Department of Congenital Heart Disease, Evelina London Children's Hospital, London, SE1 7EH, UK.
Thomas A RobertsSchool of Imaging Sciences & Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK.
Mary A RutherfordCentre for the Developing Brain, King's College London, London, SE1 7EH, UK.
Joseph V HajnalSchool of Imaging Sciences & Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK; Centre for the Developing Brain, King's College London, London, SE1 7EH, UK.
David F A LloydSchool of Imaging Sciences & Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK; Department of Congenital Heart Disease, Evelina London Children's Hospital, London, SE1 7EH, UK.
Kuberan PushparajahSchool of Imaging Sciences & Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK; Department of Congenital Heart Disease, Evelina London Children's Hospital, London, SE1 7EH, UK.
Maria DeprezSchool of Imaging Sciences & Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK.

Funding

Wellcome Trust 102431Wellcome Trust 203148Wellcome Trust WT 203148/Z/16/Z
6 · The paper itself

Abstract

Slice-to-volume registration (SVR) methods allow reconstruction of high-resolution 3D images from multiple motion-corrupted stacks. SVR-based pipelines have been increasingly used for motion correction for T2-weighted structural fetal MRI since they allow more informed and detailed diagnosis of brain and body anomalies including congenital heart defects (Lloyd et al., 2019). Recently, fully automated rigid SVR reconstruction of the fetal brain in the atlas space was achieved in Salehi et al. (2019) that used convolutional neural networks (CNNs) for segmentation and pose estimation. However, these CNN-based methods have not yet been applied to the fetal trunk region. Meanwhile, the existing rigid and deformable SVR (DSVR) solutions (Uus et al., 2020) for the fetal trunk region are limited by the requirement of manual input as well the narrow capture range of the classical gradient descent based registration methods that cannot resolve severe fetal motion frequently occurring at the early gestational age (GA). Furthermore, in our experience, the conventional 2D slice-wise CNN-based brain masking solutions are reportedly prone to errors that require manual corrections when applied on a wide range of acquisition protocols or abnormal cases in clinical setting. In this work, we propose a fully automated pipeline for reconstruction of the fetal thorax region for 21-36 weeks GA range T2-weighted MRI datasets. It includes 3D CNN-based intra-uterine localisation of the fetal trunk and landmark-guided pose estimation steps that allow automated DSVR reconstruction in the standard radiological space irrespective of the fetal trunk position or the regional stack coverage. The additional step for generation of the common template space and rejection of outliers provides the means for automated exclusion of stacks affected by low image quality or extreme motion. The pipeline was quantitatively evaluated on a series of experiments including fetal MRI datasets and simulated rotation motion. Furthermore, we performed a qualitative assessment of the image reconstruction quality in terms of the definition of vascular structures on 100 early (median 23.14 weeks) and late (median 31.79 weeks) GA group MRI datasets covering 21 to 36 weeks GA range.

Indexed as

Imaging, Three-DimensionalMagnetic Resonance ImagingFemaleGestational AgeHumansImage Processing, Computer-AssistedMotionPregnancyThoraxAutomated localisationAutomated pose estimationDeformable slice-to-volume registrationFetal heartFetal MRI

Identifiers

PMID35649314
PMCPMC7614011

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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.