Skip to Main content Skip to Navigation
Journal articles

Spatial normalization of brain images and beyond

J.-F. Mangin 1 J. Lebenberg 2 S. Lefranc 3 N. Labra 3 Guillaume Auzias 4 M. Labit 3 M. Guevara 5 H. Mohlberg 6 P. Roca 7 P. Guevara 5 J. Dubois 8 F. Leroy 9 Ghislaine Dehaene-Lambertz 9 A. Cachia 10 T. Dickscheid 6 O. Coulon 4 C. Poupon 9 D. Rivière 11 K. Amunts 6 Z.Y. Sun 3
1 SPMS - Laboratoire de Socio-Psychologie et Management du Sport
2 LIB - Laboratoire d'Imagerie Biomédicale
3 CATI - CATI Multicenter Neuroimaging Platform
4 INT - Institut de Neurosciences de la Timone
5 Department of Electrical Engineering
6 INM-1 - Institute of Neuroscience and Medicine [Jülich]
7 UVSQ - Université de Versailles Saint-Quentin-en-Yvelines
8 UNICOG-U992 - Neuroimagerie cognitive - Psychologie cognitive expérimentale
9 NEUROSPIN - Service NEUROSPIN
10 U894 / UMS 1266 - Institut de psychiatrie et neurosciences
11 IFR49 - Neurospin - CEA
Abstract : The deformable atlas paradigm has been at the core of computational anatomy during the last two decades. Spatial normalization is the variant endowing the atlas with a coordinate system used for voxel-based aggregation of images across subjects and studies. This framework has largely contributed to the success of brain mapping. Brain spatial normalization, however, is still ill-posed because of the complexity of the human brain architecture and the lack of architectural landmarks in standard morphological MRI. Multi-atlas strategies have been developed during the last decade to overcome some difficulties in the context of segmentation. A new generation of registration algorithms embedding architectural features inferred for instance from diffusion or functional MRI is on the verge to improve the architectural value of spatial normalization. A better understanding of the architectural meaning of the cortical folding pattern will lead to use some sulci as complementary constraints. Improving the architectural compliance of spatial normalization may impose to relax the diffeomorphic constraint usually underlying atlas warping. Manifold learning could help to aggregate subjects according to their morphology. Connectivity-based strategies could emerge as an alternative to deformation-based alignment leading to match the connectomes of the subjects rather than images.
Document type :
Journal articles
Complete list of metadatas

Cited literature [24 references]  Display  Hide  Download
https://hal-amu.archives-ouvertes.fr/hal-01464140
Contributor : Olivier Coulon <>
Submitted on : Thursday, February 16, 2017 - 1:22:27 PM
Last modification on : Wednesday, July 22, 2020 - 5:02:05 PM
Long-term archiving on: : Wednesday, May 17, 2017 - 12:27:12 PM

File

Vignette du document
MEDIA-S-16-00253.pdf
Files produced by the author(s)

Identifiers

Collections

UNIV-AMU | UNIV-PARIS5 | CNRS | UPMC | LIB | UVSQ | UNIV-BOURGOGNE | CEA | USPC | PSYDREPI | INT | UNIV-PARIS-SACLAY | JOLIOT | CEA-DRF | NEUROSPIN | CEA-UPSAY | SORBONNE-UNIVERSITE | SU-SCIENCES | UNIV-PARIS | UP-SANTE

Citation

J.-F. Mangin, J. Lebenberg, S. Lefranc, N. Labra, Guillaume Auzias, et al.. Spatial normalization of brain images and beyond. Medical Image Analysis, Elsevier, 2016, 33, pp.127-133. ⟨10.1016/j.media.2016.06.008⟩. ⟨hal-01464140⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

1373

Files downloads

1121