Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
352-02-001 / 352-02-001 ISMRM Abstract

Automated assessment of internal capsule maturation in neonatal 3D-reconstructed structural T2-weighted MRI at 7T

Accepted
Chiara Casella1,2, Alena Uus 1, Luke Dedominicis1, Benjamin Clayden1,2, Jucha Willers Moore1, Philippa Bridgen2,3,4, Pierluigi Di Cio2,3,4, Ines Tomazinho1, Cidalia Da Costa1, Dario Gallo1, Sophie Arulkumaran1, Sharon L Giles3,4, Jonathan O'Muircheartaigh1,2,5,6, Maria Deprez7, Jo V Hajnal2,4, Serena Counsell1, MARY A RUTHERFORD1, Shaihan Malik2,4, Tomoki Arichi1,2,3,7
1Research Dept of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
2London Collaborative Ultra high field System (LoCUS), London, United Kingdom
3Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
4Research Dept of Imaging, Physics & Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
5Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
6MRC Centre for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
7Research Dept of Biomedical Computing, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
Presenting Author: Alena Uus

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. Hasegawa, M. et al. Development of myelination in the human fetal and infant cerebrum: A myelin basic protein immunohistochemical study. Brain and Development 14, 1–6 (1992).
2. Counsell, S. J. et al. MR Imaging Assessment of Myelination in the Very Preterm Brain. American Journal of Neuroradiology 23, 872–881 (2002).
3. Rutherford, M., Biarge, M. M., Allsop, J., Counsell, S. & Cowan, F. MRI of perinatal brain injury. Pediatr Radiol 40, 819–833 (2010).
4. Bridgen, P. et al. High resolution and contrast 7 tesla MR brain imaging of the neonate. Front. Radiol. 3, (2024).
5. Wang, S. et al. Quantitative assessment of myelination patterns in preterm neonates using T2-weighted MRI. Sci Rep 9, 12938 (2019).
6. Gruber, N. et al. A deep learning pipeline for the automated segmentation of posterior limb of internal capsule in preterm neonates. Artificial Intelligence in Medicine 132, 102384 (2022).
7. Malik, S. J., Hand, J. W., Satnarine, R., Price, A. N. & Hajnal, J. V. Specific absorption rate and temperature in neonate models resulting from exposure to a 7T head coil. Magn Reson Med 86, 1299–1313 (2021).
8. Uus, A. et al. Multi-Channel 4D Parametrized Atlas of Macro- and Microstructural Neonatal Brain Development. Front. Neurosci. 15, (2021).
9. Alexander, B. et al. White matter extension of the Melbourne Children’s Regional Infant Brain atlas: M-CRIB-WM. Human Brain Mapping 41, 2317–2333 (2020).
10. Yushkevich, P. A. et al. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. NeuroImage 31, 1116–1128 (2006).
11. Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T. & Ronneberger, O. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016 (eds. Ourselin, S., Joskowicz, L., Sabuncu, M. R., Unal, G. & Wells, W.) 424–432 (Springer International Publishing, Cham, 2016). doi:10.1007/978-3-319-46723-8_49. [doi]
12. Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare. Preprint at https://doi.org/10.48550/arXiv.2211.02701 (2022). [doi]
13. Uus, A. U. et al. BOUNTI: Brain vOlumetry and aUtomated parcellatioN for 3D feTal MRI. bioRxiv 2023.04.18.537347 (2023) doi:10.1101/2023.04.18.537347. [doi]
14. Tustison, N. J. et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29, 1310–1320 (2010).
15. Huber, P. J. The behavior of maximum likelihood estimates under nonstandard conditions. in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics vol. 5.1 221–234 (University of California Press, 1967).
16. Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological) 57, 289–300 (1995).
17. Counsell, S. J. et al. Axial and radial diffusivity in preterm infants who have diffuse white matter changes on magnetic resonance imaging at term-equivalent age. Pediatrics 117, 376–386 (2006).
18. Deoni, S. C. L., Dean, D. C., O’Muircheartaigh, J., Dirks, H. & Jerskey, B. A. Investigating white matter development in infancy and early childhood using myelin water faction and relaxation time mapping. NeuroImage 63, 1038–1053 (2012).

Cite this abstract