Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
461-02-007 ISMRM Abstract

Real-time MRI-based fetal femur length measurement

Primary: Acquisition & Reconstruction - AI methods
Secondary: Pediatrics - Fetal
461-02-007 · AI To Make Protocols, Plan, QC, and Correct Motion · Tuesday, 12 May, 9:15 AM–10:10 AM · Digital Posters Row B
Keywords: Real-time MRI Fetal MRI Automatic Protocoling Deep Learning in Medical Imaging
Accepted
Johannes Barcsay1, Sara Neves Silva 2,3, Jordina Aviles Verdera2,3,4, Charline Bradshaw2,5, MARY A RUTHERFORD2,3, Susanne Schulz-Heise6, Jana Hutter2,3,4,6
1Faculty of Computer Science, Technische Hochschule Deggendorf, Germany
2Early Life Imaging Research Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
3Imaging Physics and Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
4Smart Imaging Lab, Uniklinikum Erlangen, Erlangen, Germany
5Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, United Kingdom
6Institute of Radiology, Uniklinikum Erlangen, Erlangen, Germany
Presenting Author: Sara Neves Silva

Synopsis

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References

1. Aboughalia H, Noda S, Chapman T, Revzin MV, Deutsch GH, Browd SR, et al. Multimodality imaging evaluation of fetal spine anomalies with postnatal correlation. Radiographics. 2021 Nov;41(7):2176–92.
2. Parilla BV, Leeth EA, Kambich MP, Chilis P, MacGregor SN. Antenatal detection of skeletal dysplasias. J Ultrasound Med. 2003 Mar;22(3):255–8; quiz 259–61.
3. Poyner SE, Bradshaw WT. Jeune syndrome: considerations for management of asphyxiating thoracic dystrophy. Neonatal Network. 2013;32.
4. Levine D. Ultrasound versus magnetic resonance imaging in fetal evaluation. Top Magn Reson Imaging. 2001 Feb;12(1):25–38.
5. Chow K, Kellman P, Xue H. Prototyping image reconstruction and analysis with FIRE. In: Proceedings of the SCMR 24th Annual Scientific Sessions, Virtual Meeting. 2021.
6. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021 Feb;18(2):203–11.
7. Neves Silva S, Uus A, Waheed H, Bansal S, St Clair K, Norman W, et al. Scanner-based real-time automated volumetry reporting of the fetus, amniotic fluid, placenta, and umbilical cord for fetal MRI at 0.55T. Magn Reson Med [Internet]. 2025 Sep 24;(mrm.70097). Available from: http://dx.doi.org/10.1002/mrm.70097 [doi]
8. Chen J, Mei J, Li X, Lu Y, Yu Q, Wei Q, et al. TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers. Med Image Anal. 2024 Oct;97(103280):103280.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/461-02-007