Abstract
Fetal dynamic MRI acquisitions allow insights into fetal motor behaviour, serving as a surrogate for fetal cognitive development. Systematic assessment of these highly temporally resolved 2D images might hence allow novel insights into both normal development and contain crucial information in pathology. Here, we present an automatic deep learning-based assessment for masking of the uterine and fetal structures and fetal and maternal motion quantification tool, applied to 61 low field 0.55T fetal cine MRI datasets acquired between 24 and 40 weeks gestational age at two sites using two different MR contrasts. The lack of cine labels was overcome by imitating cine acquisitions from high-resolution labelled static 3D anatomical balanced steady-state free precession datasets. Results illustrate high segmentation accuracy for the larger uterine structures (mean dice coefficient of 0.86 for the fetal body, 0.88 for the fetal head, 0.61 for the placenta) as well as the ability to detect fetal motion.
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Acknowledgements
The authors thank all pregnant women and their families for taking part in this study, all research midwives, radiographers and clinical fellows at both the Research Department of Early Life Imaging at King’s College London and St Thomas’ Hospital, as well as at Friedrich-Alexander University Erlangen-Nuremberg and University Hospital Erlangen. This work was supported by the Wellcome Trust, Sir Henry Wellcome Fellowship [201374/Z/16/Z], the MRC grants [MR/X010007/1] and [MR/W019469/1], the UKRI FLF [MR/T018119/1], DFG Heisenberg [502024488], and the NIHR Advanced Fellowship [NIHR3016640]. The views presented in this study represent those of the authors and not of Guy’s and St Thomas’ NHS Foundation Trust and University Hospital Erlangen.
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Appendix
Appendix
Graphical display of the training loss from the multi-class segmentation network.
Representative time frame including the considered points of interest.
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Kitzberger, M. et al. (2025). Automatic Disentanglement of Motion in Fetal Low Field MRI 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_3
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DOI: https://doi.org/10.1007/978-3-031-73260-7_3
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