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

Fetal MRI Low field T2*

Journal

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Titre abrégé: Med Image Comput Comput Assist Interv
Pays: Germany
ID NLM: 101249582

Informations de publication

Date de publication:
2023
Historique:
medline: 1 1 2023
pubmed: 1 1 2023
entrez: 15 10 2024
Statut: ppublish

Résumé

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

Identifiants

pubmed: 39404664
doi: 10.1007/978-3-031-43990-2_34
pmc: PMC7616578
doi:

Types de publication

Journal Article

Langues

eng

Pagination

358-367

Auteurs

Kelly Payette (K)

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

Alena Uus (A)

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

Jordina Aviles Verdera (JA)

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

Carla Avena Zampieri (CA)

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

Megan Hall (M)

Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Department of Women & Children's Health, King's College London, London, UK: MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK.

Lisa Story (L)

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

Maria Deprez (M)

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

Mary A Rutherford (MA)

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

Joseph V Hajnal (JV)

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

Sebastien Ourselin (S)

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

Raphael Tomi-Tricot (R)

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

Jana Hutter (J)

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

Classifications MeSH