| Authors |
Karolis,Vycheslav;Centre for the Developing Brain, King’s College London, London, UK
Williams,Logan;School of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK
Kyriakopoulou,Vanessa;Centre for the Developing Brain, King’s College London, London, UK
Bozek,Jelena;University of Zagreb, Zagreb, Croatia
Uus,Alena;School of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK
Makropoulos,Antonios;ThinkSono, London, United Kingdom
Schuh,Andreas;Department of Computing, Imperial College London, London, UK
Cordero Grande,Lucilio;Centre for the Developing Brain, King’s College London, London, UK
Hughes,Emer;Centre for the Developing Brain, King’s College London, London, UK
Price,Anthony;Centre for the Developing Brain, King’s College London, London, UK
Deprez,Maria;Centre for the Developing Brain, King’s College London, London, UK
Rutherford,Mary;Centre for the Developing Brain, King’s College London, London, UK
Edwards,A. David;Centre for the Developing Brain, King’s College London, London, UK
Rueckert,Daniel;Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
Smith,Stephen;FMRIB, University of Oxford, Oxford, UK
Hajnal,Joseph;School of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK
Arichi,Tomoki;Centre for the Developing Brain, King’s College London, London, UK
Robinson,Emma;School of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK
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| Description |
This repository contains spatio-temporal surface atlas, spanning 21-36 weeks of gestation,
generated by adapting the procedure developed for the neonatal surface atlas in Bozek et al., 2018,
which iteratively refines templates through a repeated alignment of individuals to a common space
using MSM algorithm to the fetal data. At each iteration, the age-specific templates are obtained
through weighted averaging of co-registered surfaces, whereby weights are defined by the Gaussians
centred on the gestational weeks for which templates are calculated, and are subsequently used as a
target space for the following iteration. Adaptive kernel regression, compensating for a difference
in the number of scans available for different ages, was used to parameterise the width of the Gaussians.
The atlas was generated in three stages: first, a common reference space was initialised via affine
sulcal-depth-based registration to the dHCP neonatal GW36 template. At the next iteration, the template
was refined using sulcal-depth-based nonlinear alignment, followed by 4 iterations of curvature-based
alignment (a more fine-grained feature than the sulcal depth).
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| References |
J. Bozek, A. Makropoulos, A. Schuh, S. Fitzgibbon, R. Wright, M. F. Glasser, T. S. Coalson, J. O’Muircheartaigh, J. Hutter, A. N. Price, L. Cordero-Grande, R. P. A. G. Teixeira, E. Hughes, N. Tusor, K. P. Baruteau, M. A. Rutherford, A. D. Edwards, J. V. Hajnal, S. M. Smith, D. Rueckert, M. Jenkinson, E. C. Robinson. (2018). Construction of a neonatal cortical surface atlas using Multimodal Surface Matching in the Developing Human Connectome Project. NeuroImage. 179, 11–29 [doi:10.1016/j.neuroimage.2018.06.018] (IsSupplementTo)
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