Multi-channel registration for diffusion mri: longitudinal analysis for the neonatal brain
Uus A, Pietsch M, Grigorescu I, Christiaens D, Tournier JD, Grande LC, Hutter J, Edwards D, Hajnal J, Deprez M (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Springer
Book Volume: 12120 LNCS
Pages Range: 111-121
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Portoroz, SVN
ISBN: 9783030501198
DOI: 10.1007/978-3-030-50120-4_11
Abstract
In multi-channel (MC) registration, fusion of structural and diffusion brain MRI provides information on both cortex and white matter (WM) structures thus decreasing the uncertainty of deformation fields. However, the existing solutions employ only diffusion tensor imaging (DTI) derived metrics which are limited by inconsistencies in fiber-crossing regions. In this work, we extend the pipeline for registration of multi-shell high angular resolution diffusion imaging (HARDI) [15] with a novel similarity metric based on angular correlation and an option for multi-channel registration that allows incorporation of structural MRI. The contributions of channels to the displacement field are weighted with spatially varying certainty maps. The implementation is based on MRtrix3 (MRtrix3: https://www.mrtrix.org) toolbox. The approach is quantitatively evaluated on intra-patient longitudinal registration of diffusion MRI datasets of 20 preterm neonates with 7–11 weeks gap between the scans. In addition, we present an example of an MC template generated using the proposed method.
Authors with CRIS profile
Involved external institutions
United Kingdom (GB)How to cite
APA:
Uus, A., Pietsch, M., Grigorescu, I., Christiaens, D., Tournier, J.-D., Grande, L.C.,... Deprez, M. (2020). Multi-channel registration for diffusion mri: longitudinal analysis for the neonatal brain. In Ziga Spiclin, Jamie McClelland, Jan Kybic, Orcun Goksel (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 111-121). Portoroz, SVN: Springer.
MLA:
Uus, Alena, et al. "Multi-channel registration for diffusion mri: longitudinal analysis for the neonatal brain." Proceedings of the 9th International Workshop on Biomedical Image Registration, WBIR 2020, Portoroz, SVN Ed. Ziga Spiclin, Jamie McClelland, Jan Kybic, Orcun Goksel, Springer, 2020. 111-121.
BibTeX: Download