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Start Over Author "Service NEUROSPIN (NEUROSPIN)" Journal medical image analysis
4 results on '"Service NEUROSPIN (NEUROSPIN)"'

1. Population shrinkage of covariance (PoSCE) for better individual brain functional-connectivity estimation

Author

Bertrand Thirion, Mehdi Rahim, Gaël Varoquaux, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), ANR-11-BINF-0004,NiConnect,Outils pour la Recherche Clinique par cartographie de la connectivité cérébrale fonctionnelle(2011), European Project: 785907,H2020,HBP SGA2(2018), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)

Subjects
Computer science, Rest, Population, population models, Twins, Health Informatics, Positive-definite matrix, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Estimation of covariance matrices, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Connectome, Image Processing, Computer-Assisted, Statistics::Methodology, Humans, Radiology, Nuclear Medicine and imaging, education, Shrinkage, education.field_of_study, Models, Statistical, Radiological and Ultrasound Technology, Covariance, [SCCO.NEUR]Cognitive science/Neuroscience, Siblings, functional connectivity, Probabilistic logic, Age Factors, Estimator, Reproducibility of Results, Computer Graphics and Computer-Aided Design, Magnetic Resonance Imaging, [STAT]Statistics [stat], shrinkage, Phenotype, Population model, Computer Vision and Pattern Recognition, Algorithm, 030217 neurology & neurosurgery, Algorithms
Abstract

International audience; Estimating covariances from functional Magnetic Resonance Imaging at rest (r-fMRI) can quantify interactions between brain regions. Also known as brain functional connectivity, it reflects inter-subject variations in behavior and cognition, and characterizes neuropathologies. Yet, with noisy and short time-series, as in r-fMRI, covariance estimation is challenging and calls for penalization, as with shrinkage approaches. We introduce population shrinkage of covariance estimator (PoSCE) : a covariance estimator that integrates prior knowledge of covariance distribution over a large population, leading to a non-isotropic shrinkage. The shrinkage is tailored to the Riemannian geometry of symmetric positive definite matrices. It is coupled with a probabilistic modeling of the individual and population covariance distributions. Experiments on two large r-fMRI datasets (HCP n=815, Cam-CAN n=626) show that PoSCE has a better bias-variance trade-off than existing covariance estimates: this estimator relates better functional-connectivity measures to cognition while capturing well intra-subject functional connectivity.

Published
2018

2. Spatial normalization of brain images and beyond

Author

Pauline Roca, Miguel Guevara, Hartmut Mohlberg, François Leroy, Jean-François Mangin, Olivier Coulon, Ghislaine Dehaene-Lambertz, Denis Rivière, Nicole Labra, Jessica Dubois, Mickael Labit, Katrin Amunts, Guillaume Auzias, Sandrine Lefranc, Arnaud Cachia, Jessica Lebenberg, Pamela Guevara, Zhong Yi Sun, Cyril Poupon, Timo Dickscheid, Laboratoire de Socio-Psychologie et Management du Sport (SPMS), Université de Bourgogne (UB), Laboratoire d'Imagerie Biomédicale [Paris] (LIB), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), CATI Multicenter Neuroimaging Platform (CATI), Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Universidad de Concepción - University of Concepcion [Chile], Institute of Neuroscience and Medicine [Jülich] (INM-1), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Neuroimagerie cognitive - Psychologie cognitive expérimentale (UNICOG-U992), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Centre de Psychiatrie et Neurosciences (U894), Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), IFR49 - Neurospin - CEA, Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Laboratoire d'Imagerie Biomédicale (LIB), Department of Electrical Engineering, Universidad de Concepción [Chile], Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Saclay (COmUE)-Institut National de la Santé et de la Recherche Médicale (INSERM), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Institut de psychiatrie et neurosciences (U894 / UMS 1266), Coulon, Olivier, Laboratoire de Socio-Psychologie et Management du Sport ( SPMS ), Université de Bourgogne ( UB ), Laboratoire d'Imagerie Biomédicale ( LIB ), Centre National de la Recherche Scientifique ( CNRS ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ), CATI Multicenter Neuroimaging Platform ( CATI ), Institut de Neurosciences de la Timone ( INT ), Aix Marseille Université ( AMU ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Institute of Neuroscience and Medicine [Jülich] ( INM-1 ), Université de Versailles Saint-Quentin-en-Yvelines ( UVSQ ), Neuroimagerie cognitive ( UNICOG-U992 ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Université Paris-Sud - Paris 11 ( UP11 ), NeuroSpin, Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Institut d'imagerie biomédicale (I²BM), Service NEUROSPIN ( NEUROSPIN ), Direction de Recherche Fondamentale (CEA) ( DRF (CEA) ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay, Centre de Psychiatrie et Neurosciences ( CPN - U894 ), Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Université Paris Descartes - Paris 5 ( UPD5 ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -IFR49-Neurospin, Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), and Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National de la Santé et de la Recherche Médicale ( INSERM )

Subjects
Computer science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [SDV.NEU.PC] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Psychology and behavior, Health Informatics, Context (language use), [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, computer.software_genre, Brain mapping, 030218 nuclear medicine & medical imaging, [ SDV.NEU.PC ] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Psychology and behavior, [ SDV.NEU.SC ] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, 03 medical and health sciences, 0302 clinical medicine, Voxel, Connectome, Image Processing, Computer-Assisted, Humans, Radiology, Nuclear Medicine and imaging, Segmentation, Computer vision, [ SDV.IB.IMA ] Life Sciences [q-bio]/Bioengineering/Imaging, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Brain Mapping, Radiological and Ultrasound Technology, [SDV.NEU.PC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Psychology and behavior, Atlas (topology), business.industry, Nonlinear dimensionality reduction, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Brain, Computer Graphics and Computer-Aided Design, Computational anatomy, Magnetic Resonance Imaging, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Spatial normalization, Computer Vision and Pattern Recognition, Artificial intelligence, ddc:004, business, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery, [SDV.NEU.SC] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Algorithms
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. A two-level strategy could be designed: in each region, a dictionary of templates of incompatible folding patterns would be collected and matched in a way or another using rare architectural information, while individual subjects would be aligned using diffeomorphisms to the closest template. 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.

Published
2016

3. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes

Author

Nicholas Ayache, Pietro Gori, Stanley Durrleman, Andreas Hartmann, Yulia Worbe, Linda Marrakchi-Kacem, Olivier Colliot, Cyril Poupon, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CIC AP-HP [CHU Pitié-Salpêtrière], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), and Gori, Pietro

Subjects
Gaussian, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Normal Distribution, Health Informatics, Geometry, Probability density function, shape, Bayesian, fiber bundle, 030218 nuclear medicine & medical imaging, Normal distribution, 03 medical and health sciences, symbols.namesake, varifolds, 0302 clinical medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, atlas, Radiology, Nuclear Medicine and imaging, Polygon mesh, multi - object, Parametric statistics, Mathematics, Radiological and Ultrasound Technology, Covariance matrix, Atlas (topology), Bayes Theorem, White Matter, Computer Graphics and Computer-Aided Design, Ambient space, Case-Control Studies, symbols, Computer Vision and Pattern Recognition, Algorithm, Algorithms, Software, morphometry, complex, 030217 neurology & neurosurgery, Tourette Syndrome
Abstract

International audience; We present a Bayesian framework for atlas construction of multi-object shape complexes comprised of both surface and curve meshes. It is general and can be applied to any parametric deformation framework and to all shape models with which it is possible to define probability density functions (PDF). Here , both curve and surface meshes are modelled as Gaussian random varifolds , using a finite-dimensional approximation space on which PDFs can be defined. Using this framework , we can automatically estimate the parameters balancing data-terms and deformation regularity , which previously required user tuning. Moreover , it is also possible to estimate a well-conditioned covariance matrix of the deformation parameters. We also extend the proposed framework to data-sets with multiple group labels. Groups share the same template and their deformation parameters are modelled with different distributions. We can statistically compare the groups ' distributions since they are defined on the same space. We test our algorithm on 20 Gilles de la Tourette patients and 20 control subjects , using three sub-cortical regions and their incident white matter fiber bundles. We compare their morphological characteristics and variations using a single diffeomorphism in the ambient space. The proposed method will be integrated with the Deformetrica software package, publicly available at www. deformetrica. org .

Published
2017

4. Distortion correction and robust tensor estimation for MR diffusion imaging

Author

Chris A. Clark, Cyril Poupon, Isabelle Bloch, D. Le Bihan, J.-F. Mangin, Le Bihan, Denis, Laboratoire de Neuroimagerie Assistée par Ordinateur (LNAO), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Service Hospitalier Frédéric Joliot (SHFJ), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Center for Neuroscience and Behavioral Medicine, Children's National Medical Center, Laboratoire d'Imagerie et de Spectroscopie (LRMN), Service NEUROSPIN (NEUROSPIN), IFR de Neuroimagerie Fonctionnelle (IFR 49), Human Brain Research Center [Kyoto] (HBRC), Kyoto University, Département Traitement du Signal et des Images (TSI), Télécom ParisTech-Centre National de la Recherche Scientifique (CNRS), Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), QUANTUM (QUANTUM), Johannes Gutenberg - Universität Mainz = Johannes Gutenberg University (JGU), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Kyoto University [Kyoto], Centre National de la Recherche Scientifique (CNRS)-Télécom ParisTech, and Johannes Gutenberg - Universität Mainz (JGU)

Subjects
Quality Control, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Computer science, Statistics as Topic, MESH: Quality Control, Diffusion map, Health Informatics, MESH: Diffusion Magnetic Resonance Imaging, computer.software_genre, Sensitivity and Specificity, Least squares, 030218 nuclear medicine & medical imaging, MESH: Brain, 03 medical and health sciences, 0302 clinical medicine, Distortion, Humans, Radiology, Nuclear Medicine and imaging, Tensor, Diffusion (business), MESH: Statistics as Topic, Stochastic Processes, Models, Statistical, MESH: Humans, Radiological and Ultrasound Technology, Brain, Reproducibility of Results, Mutual information, Maximization, Image Enhancement, Computer Graphics and Computer-Aided Design, MESH: Sensitivity and Specificity, MESH: Reproducibility of Results, Diffusion Magnetic Resonance Imaging, MESH: Artifacts, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, MESH: Stochastic Processes, Computer Vision and Pattern Recognition, Data mining, MESH: Image Enhancement, Artifacts, Algorithm, computer, MESH: Models, Statistical, 030217 neurology & neurosurgery, Diffusion MRI
Abstract

International audience; This paper presents a new procedure to estimate the diffusion tensor from a sequence of diffusion-weighted images. The first step of this procedure consists of the correction of the distortions usually induced by eddy-current related to the large diffusion-sensitizing gradients. This correction algorithm relies on the maximization of mutual information to estimate the three parameters of a geometric distortion model inferred from the acquisition principle. The second step of the procedure amounts to replacing the standard least squares-based approach by the Geman-McLure M-estimator, in order to reduce outlier-related artefacts. Several experiments prove that the whole procedure highly improves the quality of the final diffusion maps.

Published
2002

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