Caractérisation de pathologies cérébrales par l’analyse de modèles multi-compartiment en IRM de diffusion

Diffusion weighted imaging (DWI) is a specific type of MRI acquisition based on the direction of diffusion of the brain water molecule. Its allow, through several acquisitions, to model brain microstructure, as white matter, which are significantly smaller than the voxel-resolution. To acquire a large number of images in a clinical use, very-fast acquisition technique are required as single-shot imaging, however these acquisitions suffer local large distortions. We propose a Block-Matching registration method based on a the acquisition of images with opposite phase-encoding directions (PED). This technique specially designs for Echo-Planar Images (EPI), but which could be generic, robustly correct images and provide a deformation field. This field is applicable to an entire DWI series from only one reversed b 0 allowing distortion correction with a minimal time acquisition cost. This registration algorithm has been validated both on a phantom data set and on in-vivo data and is available in our source medical image processing toolbox Anima. From these diffusion images, we are able to construct multi-compartments models (MCM) which could represented complex brain microstructure. We need to do registration, average, create atlas on these MCM to be able to make studies and produce statistic analysis. We propose a general method to interpolate MCM as a simplification problem based on spectral clustering. This technique, which is adaptable for any MCM, has been validated for both synthetic and real data. Then, from a registered dataset, we made analysis at a voxel-level doing statistic on MCM parameters. Specifically design tractography can also be perform to make analysis, following tracks, based on individual compartment. All these tools are designed and used on real data and contribute to the search of biomakers for brain diseases.L'imagerie pondérée en diffusion est un type d'acquisition IRM spécifique basé sur la direction de diffusion des molécules d'eau dans le cerveau. Cela permet, au moyen de plusieurs acquisitions, de modéliser la microstructure du cerveau, comme la matière blanche qui à une taille très inférieur à la résolution du voxel. L'obtention d'un grand nombre d'images nécessite, pour un usage clinique, des techniques d'acquisition ultra rapide tel que l'imagerie parallèle. Malheureusement, ces images sont entachées de large distorsions. Nous proposons une méthode de recalage par blocs basée sur l'acquisition d'images avec des directions de phase d'encodage opposées. Cette technique spécialement conçue pour des images écho planaires, mais qui peut être générique, corrige les images de façon robuste tout en fournissant un champs de déformation. Cette transformation est applicable à une série entière d'image de diffusion à partir d'une seule image b 0 renversée, ce qui permet de faire de la correction de distorsion avec un temps d'acquisition supplémentaire minimal. Cet algorithme de recalage, qui a été validé à la fois sur des données synthétiques et cliniques, est disponible avec notre programme de traitement d'images Anima. A partir de ces images de diffusion, nous sommes capable de construire des modèles de diffusion multi-compartiment qui représentent la microstructure complexe du cerveau. Pour pouvoir produire des analyses statistiques sur ces modèles, nous devons être capable de faire du recalage, du moyennage, ou encore de créer un atlas d'images. Nous proposons une méthode générale pour interpoler des modèles multi-compartiment comme un problème de simplification basé sur le partitionnement spectral. Cette technique qui est adaptable pour n'importe quel modèle, a été validé à la fois sur des données synthétiques et réelles. Ensuite à partir d'une base de données recalée, nous faisons des analyses statistiques en extrayant des paramètres au niveau du voxel. Une tractographie, spécifiquement conçue pour les modèles multi-compartiment, est aussi utilisée pour faire des analyses en suivant les fibres de matière blanche. Ces outils sont conçus et appliqués à des données réelles pour contribuer à la recherche de biomarqueurs pour les pathologies cérébrales

Interpolation and averaging of diffusion MRI multi-compartment models

International audienceMulti-compartment models (MCM) are increasingly used to characterize the brain white matter microstructure from diffusion-weighted imaging (DWI). Their use in clinical studies is however limited by the inability to resample an MCM image towards a common reference frame, or to construct atlases from such brain microstructure models. We propose to solve this problem by first identifying that these two tasks amount to the same problem. We propose to tackle it by viewing it as a simplification problem, solved thanks to spectral clustering and the definition of semi-metrics between several usual compartments encountered in the MCM literature. This generic framework is evaluated for two models: the multi-tensor model where individual fibers are modeled as individual tensors and the diffusion direction imaging (DDI) model that differentiates intra-and extra-axonal components of each fiber. Results on simulated data, simulated transformations and real data show the ability of our method to well interpolate MCM images of these types. We finally present as an application an MCM template of normal controls constructed using our approach

The tropical brown alga Lobophora variegata as a bioindicator of mining contamination in the New Caledonia lagoon : a field transplantation study

Previous field and laboratory studies have identified the alga Lobophora variegata as a good candidate for biomonitoring metal contamination in the New Caledonia lagoon which is subjected to intensive and extensive metal inputs from land-based mining activities. The aim of this work was to further assess the bioindicative potential of this species by investigating, in the field, its bioaccumulation capacity for local key contaminants, i.e. Ag, As, Cd, Co, Cr, Cu, Mn, Ni and Zn. Algae from clean and contaminated sites were cross-transplanted for a period of three months in order to determine the in situ uptake and depuration kinetics of the nine elements. Results indicate that algae transplanted to the contaminated site displayed a significant linear increase in concentration with time for Ag, As, Cd, Co, Cr, Cu, Mn and Ni. In contrast, algae transplanted to the clean site did not show major deputation of these elements, except for Co. Overall, L variegata showed a rapid temporal response in metal uptake, especially for the elements intensively released into the coastal environment of New Caledonia (viz,, Co, Cr, Mn and Ni). This species appears therefore as an excellent bioindicator species of metal contamination in this area. Our results also provide background information necessary for using L. variegata under in situ experimental conditions so as to provide better quantitative information on ambient metal contamination levels. The wide distribution of L. variegata in tropical areas further enhances its potential as a bioinclicator species of metal contamination in other tropical coastal environments

Patient specific tracts-based analysis of diffusion compartment models: application to multiple sclerosis patients with acute optic neuritis

International audienceMultiple sclerosis is a complex disease where voxel-based, group-based statistics of the brain microstructure have shown their limits in explaining patient evolution. This is first due to too simple diffusion models, mixing information. Voxel-based studies also lack knowledge on brain structural connectivity. Finally, group-based analysis does not describe well the specific patient status (a crucial point for clinicians). We propose an atlas-based framework, combined with advanced diffusion compartment models, for patient specific analysis of microstructural disease burden on major fiber bundles. We apply our framework to the analysis of optic radiations of MS patients with acute optic neuritis

Symmetric Block-Matching Registration for the Distortion Correction of Echo-Planar Images

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Estimation of microstructure parameter from ex-vivo data using realistic WM models

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Estimation of microstructure parameter from ex-vivo data using realistic WM models

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Validation of two tropical marine bivalves as bioindicators of mining contamination in the New Caledonia lagoon : field transplantation experiments

The bioaccumulation and retention capacities of some key local contaminants of the New Caledonia lagoon (Ag, As, Cd, Co, Cr, Cu, Mn, Ni and Zn) have been determined in the oyster Isognomon isognomon and the edible clam Gafrarium tumidum during transplantation experiments. In a first set of experiments, oysters and clams from a clean site were transplanted into contaminated sites. Uptake kinetics determined in the field indicated that for Cr and Cu in oysters and Co, Ni, and Zn in clams, concentrations in transplanted bivalves reached those of resident organisms after 100d, whereas for the other elements, it would require a longer time for transplanted bivalves to reach the same levels as in the resident populations (e.g., up to 3 years for Cd). However, the slow uptake rate for metals observed in the latter transplantation is rather related to low bioavailability of metals at the contaminated sites than to low bioaccumulation efficiency of the organisms. Indeed, results of a second transplantation experiment into two highly contaminated stations indicated a faster bioaccumulation of metals in both bivalves. Results of both transplantations point out that the clam G. tumidum is a more effective bioindicator of mining contamination than I. isognomon, since it is able to bioaccumulate the contaminants to a greater extent. However the very efficient metal retention capacity noted for most elements indicates that organisms originating from contaminated sites would not be suitable for monitoring areas of lower contamination. Hence, geographical origin of animals to be transplanted in a monitoring perspective should be carefully selected