Imperial College London
Alternate

ProfessorDanielRueckert

Faculty of Engineering, Department of Computing

Professor of Visual Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Bastiani:2019:10.1016/j.neuroimage.2018.05.064,
author = {Bastiani, M and Andersson, JLR and Cordero-Grande, L and Murgasova, M and Hutter, J and Price, AN and Makropoulos, A and Fitzgibbon, SP and Hughes, E and Rueckert, D and Victor, S and Rutherford, M and Edwards, AD and Smith, SM and Tournier, J-D and Hajnal, JV and Jbabdi, S and Sotiropoulos, SN},
doi = {10.1016/j.neuroimage.2018.05.064},
journal = {NeuroImage},
pages = {750--763},
title = {Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project},
url = {http://dx.doi.org/10.1016/j.neuroimage.2018.05.064},
volume = {185},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The developing Human Connectome Project is set to create and make available to the scientific community a 4-dimensional map of functional and structural cerebral connectivity from 20 to 44 weeks post-menstrual age, to allow exploration of the genetic and environmental influences on brain development, and the relation between connectivity and neurocognitive function. A large set of multi-modal MRI data from fetuses and newborn infants is currently being acquired, along with genetic, clinical and developmental information. In this overview, we describe the neonatal diffusion MRI (dMRI) image processing pipeline and the structural connectivity aspect of the project. Neonatal dMRI data poses specific challenges, and standard analysis techniques used for adult data are not directly applicable. We have developed a processing pipeline that deals directly with neonatal-specific issues, such as severe motion and motion-related artefacts, small brain sizes, high brain water content and reduced anisotropy. This pipeline allows automated analysis of in-vivo dMRI data, probes tissue microstructure, reconstructs a number of major white matter tracts, and includes an automated quality control framework that identifies processing issues or inconsistencies. We here describe the pipeline and present an exemplar analysis of data from 140 infants imaged at 38-44 weeks post-menstrual age.
AU  - Bastiani,M
AU  - Andersson,JLR
AU  - Cordero-Grande,L
AU  - Murgasova,M
AU  - Hutter,J
AU  - Price,AN
AU  - Makropoulos,A
AU  - Fitzgibbon,SP
AU  - Hughes,E
AU  - Rueckert,D
AU  - Victor,S
AU  - Rutherford,M
AU  - Edwards,AD
AU  - Smith,SM
AU  - Tournier,J-D
AU  - Hajnal,JV
AU  - Jbabdi,S
AU  - Sotiropoulos,SN
DO  - 10.1016/j.neuroimage.2018.05.064
EP  - 763
PY  - 2019///
SN  - 1053-8119
SP  - 750
TI  - Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project
T2  - NeuroImage
UR  - http://dx.doi.org/10.1016/j.neuroimage.2018.05.064
UR  - https://www.ncbi.nlm.nih.gov/pubmed/29852283
UR  - http://hdl.handle.net/10044/1/60368
VL  - 185
ER  -
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