@inbook{a03086619979420383beca5dc4e9d276,
title = "Combined Quantitative T2* Map and Structural T2-Weighted Tissue-Specific Analysis for Fetal Brain MRI: Pilot Automated Pipeline",
abstract = "Over the past decade, automated 3D reconstruction and segmentation has been widely applied to processing and analysis of fetal MRI. While the majority of reported methods primarily focus on structural brain imaging, additional quantitative T2* information could improve characterisation of changes in functional tissue properties. In this work, we propose a first solution for automated combined tissue-specific analysis of 3D quantitative T2* map and structural T2-weighted (T2w) fetal brain MRI. We build upon the existing 3D structural brain analysis pipeline from SVRTK by adding fully automated 3D T2* reconstructions globally aligned to 3D segmented T2w images (already reconstructed in the standard radiological space) followed by deep learning T2* tissue parcellation. In addition, we assess the general applicability the proposed pipeline by analysing brain growth trajectories in 26 control T2w+T2* fetal MRI datasets from 20–28 weeks GA range.",
keywords = "Automated segmentation, Brain T2*, Fetal MRI, Multi-contrast alignment, Tissue parcellation",
author = "Uus, {Alena U.} and Megan Hall and Kelly Payette and Hajnal, {Joseph V.} and Maria Deprez and Rutherford, {Mary A.} and Jana Hutter and Lisa Story",
note = "Funding Information: We thank everyone who was involved in acquisition and analysis of the datasets and all participating mothers and families. This work was supported by NIHR Advanced Fellowship awarded to Lisa Story [NIHR30166], MRC Confidence in concept [MC PC 19041], the NIH Human Placenta Project grant [1U01HD087202-01], the Wellcome/ EPSRC Centre for Medical Engineering at King{\textquoteright}s College London [WT 203148/Z/16/Z], the NIHR Clinical Research Facility (CRF) at Guy{\textquoteright}s and St Thomas{\textquoteright} and by the National Institute for Health Research Biomedical Research Centre based at Guy{\textquoteright}s and St Thomas{\textquoteright} NHS Foundation Trust and King{\textquoteright}s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.; 8th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2023 ; Conference date: 12-10-2023 Through 12-10-2023",
year = "2023",
doi = "10.1007/978-3-031-45544-5_3",
language = "English",
isbn = "9783031455438",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "28--38",
editor = "Daphna Link-Sourani and {Abaci Turk}, Esra and Christopher Macgowan and Jana Hutter and Jana Hutter and Andrew Melbourne and Roxane Licandro and Roxane Licandro",
booktitle = "Perinatal, Preterm and Paediatric Image Analysis - 8th International Workshop, PIPPI 2023, Held in Conjunction with MICCAI 2023, Proceedings",
address = "Germany",
}