@inbook{6e19a08f83484492a3d6660f3533cd74,
title = "A Fast Anatomical and Quantitative MRI Fetal Exam at Low Field",
abstract = "Fetal Magnetic Resonance Imaging (Fetal MRI) allows insights into human development before birth, complementing conventional Ultrasound imaging with its high resolution and available numerous contrast options. Significant challenges still exist including geometric distortion caused by maternal bowel gas in echo-planar imaging, and restrictions in bore size limiting access to MRI in the obese and or claustrophobic population. Recent developments of clinical low-field scanners can meet these challenges and thus render fetal MRI more accessible. This study shows anatomical imaging and quantitative T2* mapping on a 0.55T system with an analysis pipeline for both placenta and fetal brain. Results show an expected increased overall T2* compared to higher fields, with values decreasing over gestation as shown at higher field. Future work will be directed towards exploring additional types of relaxometry and the use of the presented techniques in subjects with higher Body Mass Index. Included data and analysis code are publicly available.",
keywords = "Fetal and Placental MRI, Low field",
author = "Jordina Aviles and Kathleen Colford and Megan Hall and Massimo Marenzana and Alena Uus and Sharon Giles and Philippa Bridgen and Rutherford, {Mary A.} and Malik, {Shaihan J.} and Hajnal, {Joseph V.} and Raphael Tomi-Tricot and Jana Hutter",
note = "Funding Information: The authors thank all the participating families as well as the midwives and radiographers involved in this study. This work was supported by the NIH (Human Placenta Project—grant 1U01HD087202-01), Wellcome Trust Sir Henry Wellcome Fellowship (201374/Z/16/Z and /B), UKRI FLF (MR/T018119/1), Wellcome-EPSRC Center for Medical Engineering, the NIHR Clinical Research Facility (CRF) at Guy{\textquoteright}s and St Thomas{\textquoteright}. The views expressed are those of the authors and not necessarily those of the NHS or the NIHR. Funding Information: Acknowledgments. The authors thank all the participating families as well as the midwives and radiographers involved in this study. This work was supported by the NIH (Human Placenta Project—grant 1U01HD087202-01), Wellcome Trust Sir Henry Wellcome Fellowship (201374/Z/16/Z and /B), UKRI FLF (MR/T018119/1), Wellcome-EPSRC Center for Medical Engineering, the NIHR Clinical Research Facility (CRF) at Guy{\textquoteright}s and St Thomas{\textquoteright}. The views expressed are those of the authors and not necessarily those of the NHS or the NIHR. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 7th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2022, held in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022 ; Conference date: 18-09-2022 Through 18-09-2022",
year = "2022",
doi = "10.1007/978-3-031-17117-8_2",
language = "English",
isbn = "9783031171161",
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 = "13--24",
editor = "Roxane Licandro and Roxane Licandro and Andrew Melbourne and Jana Hutter and {Abaci Turk}, Esra and Christopher Macgowan",
booktitle = "Perinatal, Preterm and Paediatric Image Analysis - 7th International Workshop, PIPPI 2022, Held in Conjunction with MICCAI 2022, Proceedings",
address = "Germany",
}