Regression analysis for assessment of myelination status in preterm brains with magnetic resonance imaging
Wang S, Kuklisova-Murgasova M, Hajnal JV, Ledig C, Schnabel JA (2016)
Publication Type: Conference contribution
Publication year: 2016
Journal
Publisher: IEEE Computer Society
Book Volume: 2016-June
Pages Range: 278-281
Conference Proceedings Title: Proceedings - International Symposium on Biomedical Imaging
Event location: Prague, CZE
ISBN: 9781479923502
DOI: 10.1109/ISBI.2016.7493263
Abstract
Myelination is considered an important developmental process during human brain maturation and to be closely correlated with gestational age. Assessment of the myelination status generally requires dedicated imaging, yet the conventional T2-weighted acquisitions routinely obtained during clinical imaging of neonates carry signatures that are thought to be directly associated with myelination. In this work, we propose a method to identify these signatures which could potentially be used to assess brain maturation of preterm neonates directly from T2-weighted magnetic resonance images. First we segment the tissue that is likely to contain myelin from 96 preterm neonates. We then construct a spatio-temporal atlas based on the registered segmentations by fitting a voxelwise logistic regression model. Finally, the atlas is utilized to estimate the gestational ages of individual subjects in a leave-one-out procedure. The logistic model yields a root mean squared error of 10 days, as compared to 13 days for the ages predicted using a kernel regression atlas.
Involved external institutions
University of Oxford
United Kingdom (GB)
King’s College London
United Kingdom (GB)
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
United Kingdom (GB)
King’s College London
United Kingdom (GB)
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
How to cite
APA:
Wang, S., Kuklisova-Murgasova, M., Hajnal, J.V., Ledig, C., & Schnabel, J.A. (2016). Regression analysis for assessment of myelination status in preterm brains with magnetic resonance imaging. In Proceedings - International Symposium on Biomedical Imaging (pp. 278-281). Prague, CZE: IEEE Computer Society.
MLA:
Wang, Siying, et al. "Regression analysis for assessment of myelination status in preterm brains with magnetic resonance imaging." Proceedings of the 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016, Prague, CZE IEEE Computer Society, 2016. 278-281.
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