3D T2w fetal body MRI: automated organ volumetry, growth charts and population-averaged atlas.


Journal

medRxiv : the preprint server for health sciences
Titre abrégé: medRxiv
Pays: United States
ID NLM: 101767986

Informations de publication

Date de publication:
18 Sep 2023
Historique:
pubmed: 3 7 2023
medline: 3 7 2023
entrez: 3 7 2023
Statut: epublish

Résumé

Structural fetal body MRI provides true 3D information required for volumetry of fetal organs. However, current clinical and research practice primarily relies on manual slice-wise segmentation of raw T2-weighted stacks, which is time consuming, subject to inter- and intra-observer bias and affected by motion-corruption. Furthermore, there are no existing standard guidelines defining a universal approach to parcellation of fetal organs. This work produces the first parcellation protocol of the fetal body organs for motion-corrected 3D fetal body MRI. It includes 10 organ ROIs relevant to fetal quantitative volumetry studies. We also introduce the first population-averaged T2w MRI atlas of the fetal body. The protocol was used as a basis for training of a neural network for automated organ segmentation. It showed robust performance for different gestational ages. This solution minimises the need for manual editing and significantly reduces time. The general feasibility of the proposed pipeline was also assessed by analysis of organ growth charts created from automated parcellations of 91 normal control 3T MRI datasets that showed expected increase in volumetry during 22-38 weeks gestational age range. In addition, the results of comparison between 60 normal and 12 fetal growth restriction datasets revealed significant differences in organ volumes.

Identifiants

pubmed: 37398121
doi: 10.1101/2023.05.31.23290751
pmc: PMC10312818
pii:
doi:

Types de publication

Preprint

Langues

eng

Subventions

Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : NICHD NIH HHS
ID : U01 HD087202
Pays : United States

Auteurs

Alena U Uus (AU)

School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.

Megan Hall (M)

Centre for the Developing Brain, King's College London, London, UK.
Department of Women and Children's Health, King's College London, London, UK.
Fetal Medicine Unit, Guy's and St Thomas' NHS Foundation Trust, London, UK.

Irina Grigorescu (I)

School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.

Carla Avena Zampieri (CA)

Centre for the Developing Brain, King's College London, London, UK.
Department of Women and Children's Health, King's College London, London, UK.

Alexia Egloff Collado (AE)

Centre for the Developing Brain, King's College London, London, UK.

Kelly Payette (K)

School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
Centre for the Developing Brain, King's College London, London, UK.

Jacqueline Matthew (J)

School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
Centre for the Developing Brain, King's College London, London, UK.

Vanessa Kyriakopoulou (V)

Centre for the Developing Brain, King's College London, London, UK.

Joseph V Hajnal (JV)

School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
Centre for the Developing Brain, King's College London, London, UK.

Jana Hutter (J)

School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
Centre for the Developing Brain, King's College London, London, UK.

Mary A Rutherford (MA)

Centre for the Developing Brain, King's College London, London, UK.

Maria Deprez (M)

School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.

Lisa Story (L)

Centre for the Developing Brain, King's College London, London, UK.
Department of Women and Children's Health, King's College London, London, UK.
Fetal Medicine Unit, Guy's and St Thomas' NHS Foundation Trust, London, UK.

Classifications MeSH