Abstract
Fetal magnetic resonance imaging (MRI) is a intriguing tool to gain insights into early human development. Diffusion MRI is of particular interest to study neuronal development in vivo. However, fetal motion hampers accurate quantification. We suggest an automated quality check for a combined multi-echo diffusion-weighted fetal sequence on the low field (0.55T) consisting of deep learning-based masking of the brain and quality assessment. Results from 56 fetal datasets between 17 and 41 weeks gestational age illustrate the ability to obtain high-quality masks and transparent, insightful quality scores. Next, the achieved automatic assessment will be performed in real-time to guide the scan, initiate possible field-of-view (FOV) shifts, or repeat individual volumes.
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Acknowledgments
The authors thank all pregnant women and their families for taking part in this study. The authors thank all research midwifes and radiographers for their invaluable efforts in recruiting and looking after the women in this study. This work was supported by a Wellcome Trust Collaboration in Science grant [WT201526/Z/16/Z], a UKRI FL fellowship [MR/T018119/1] and DFG Heisenberg funding [502024488] to JH and by core funding from the Wellcome/EPSRC Centre for Medical Engineering [WT203148/Z/16/Z]. The views presented in this study represent these of the authors and not of Guy’s and St Thomas’ NHS Foundation Trust.
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Bortolazzi, A. et al. (2025). Automatic Assessment of Fetal Multi-echo Diffusion Weighted Scans. In: Link-Sourani, D., Abaci Turk, E., Macgowan, C., Hutter, J., Melbourne, A., Licandro, R. (eds) Perinatal, Preterm and Paediatric Image Analysis. PIPPI 2024. Lecture Notes in Computer Science, vol 14747. Springer, Cham. https://doi.org/10.1007/978-3-031-73260-7_8
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DOI: https://doi.org/10.1007/978-3-031-73260-7_8
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