Deux approches de la corrélation 3D d'images volumiques comparées sur des données de tomographie à rayons X

On présente une comparaison de deux approches de la corrélation 3D d'images volumiques, développées indépendamment dans des contextes scientifiques différents. L'une est CorrelManu3D, développée dans le cadre de la mécanique des solides avec les images de tomographie à rayons X. L'autre, dénommée TomoWarp, dérive d'une approche géophysique d'analyses d'images de gisements sous-terrains. L'une et l'autre sont appliquées au suivi de la déformation et de l'endommagement dans divers géomatériaux soumis à des chargements mécaniques sous contrôle tomographique à rayons X. Nous comparons les deux approches sur le cas d'un essai triaxial sur roche argileuse avec confinement. Nous trouvons que, dans cet exemple, les deux approches donnent les quantifications de déformation localisée différente, probablement à cause des différences des raffinements sous-voxel utilisé

A framework for creating population specific multimodal brain atlas using clinical T1 and diffusion tensor images

International audienceSpatial normalization is one of the most important steps in population based statistical analysis of brain images. This involves normalizing all the brain images to a pre-defined template or a population specific template. With multiple emerging imaging modalities, it is quintessential to develop a method for building a joint template that is a statistical representation of the given population across different modalities. It is possible to create different population specific templates in different modalities using existing methods. However, they do not give an opportunity for voxelwise comparison of different modalities. A multimodal brain template with probabilistic region of interest (ROI) definitions will give opportunity for multivariate statistical frameworks for better understanding of brain diseases. In this paper, we propose a methodology for developing such a multimodal brain atlas using the anatomical T1 images and the diffusion tensor images (DTI), along with an automated workflow to probabilistically define the different white matter regions on the population specific multimodal template. The method will be useful to carry out ROI based statistics across different modalities even in the absence of expert segmentation. We show the effectiveness of such a template using voxelwise multivariate statistical analysis on population based group studies on HIV/AIDS patients. 1 The need for a probabilistic multimodal atlas The growth in brain imaging data across different modalities gives an opportunity to understand the disease progression and make correlations across them. Statistical analysis across different modalities and across population require spatial normalization. All the brain images are often normalized to a pre-defined template, for example the ICBM-152 or MNI template. However in [1] and [2], the authors have shown that choosing a generic template biases the statistical presently at Imaging Genetics Center, University of Southern California presently at MORPHENE team, INRIA Sophia-Antipoli

Spatial Normalization of Diffusion Tensor MRI Using Multiple Channels

Diffusion Tensor MRI (DT-MRI) can provide important in vivo information for the detection of brain abnormalities in diseases characterized by compromised neural connectivity. To quantify diffusion tensor abnormalities based on voxel-based statistical analysis, spatial normalization is required to minimize the anatomical variability between studied brain structures. In this article, we used a multiple input channel registration algorithm based on a demons algorithm and evaluated the spatial normalization of diffusion tensor image in terms of the input information used for registration. Registration was performed on 16 DT-MRI data sets using different combinations of the channels, including a channel of T2-weighted intensity, a channel of the fractional anisotropy, a channel of the difference of the first and second eigenvalues, two channels of the fractional anisotropy and the trace of tensor, three channels of the eigenvalues of the tensor, and the six channel tensor components. To evaluate the registration of tensor data, we defined two similarity measures, i.e., the endpoint divergence and the mean square error, which we applied to the fiber bundles of target images and registered images at the same seed points in white matter segmentation. We also evaluated the tensor registration by examining the voxel-by-voxel alignment of tensors in a sample of 15 normalized DT-MRIs. In all evaluations, nonlinear warping using six independent tensor components as input channels showed the best performance in effectively normalizing the tract morphology and tensor orientation. We also present a nonlinear method for creating a group diffusion tensor atlas using the average tensor field and the average deformation field, which we believe is a better approach than a strict linear one for representing both tensor distribution and morphological distribution of the population.ope

Segmentation algorithms for ear image data towards biomechanical studies

In recent years, the segmentation, i.e. the identification, of ear structures in video-otoscopy, computerised tomography (CT) and magnetic resonance (MR) image data, has gained significant importance in the medical imaging area, particularly those in CT and MR imaging. Segmentation is the fundamental step of any automated technique for supporting the medical diagnosis and, in particular, in biomechanics studies, for building realistic geometric models of ear structures. In this paper, a review of the algorithms used in ear segmentation is presented. The review includes an introduction to the usually biomechanical modelling approaches and also to the common imaging modalities. Afterwards, several segmentation algorithms for ear image data are described, and their specificities and difficulties as well as their advantages and disadvantages are identified and analysed using experimental examples. Finally, the conclusions are presented as well as a discussion about possible trends for future research concerning the ear segmentation.info:eu-repo/semantics/publishedVersio

Volumetria de estruturas cerebrais profundas com imagem RM

A Ressonância Magnética é uma técnica de diagnóstico por imagem frequentemente presente na prática clínica e em constante desenvolvimento. É um método moderno e sofisticado de aquisição de imagem e sinal, com elevada qualidade de imagem, relevante para a volumetria cerebral. A volumetria associada a RM facilita a comparação de dados volumétricos por possibilitar a obtenção de dados mais concretos a nível dos volumes das estruturas cerebrais. Atualmente, o interesse no desenvolvimento de metodologias para a análise de estruturas e medição volumétrica tem vindo a aumentar, sendo que, é desejável que seja um método mais automático, rápido e eficaz e que realize a segmentação de imagem com pouca intervenção do operador. Este estudo experimental tem como objetivo a comparação do volume das estruturas subcorticais entre 2 softwares diferentes a fim de testar a robustez de ambos. Os softwares utilizados, o FreeSurfer e o VolBrain, implementam estratégias de segmentação (semi-)automáticas, seguindo paradigmas algorítmicos diferentes. Ambos os softwares são de distribuição livre e utilizados para estudos de anatomia cerebral, incluindo a segmentação de anatomia cortical e subcortical, fornecendo os respetivos volumes. Inicialmente fez-se um estudo sobre os conceitos de aquisição de imagem cerebral por RM e sobre as estratégias de segmentação deformáveis existentes. A segmentação por modelos deformáveis revelou-se produtiva com resultados prometedores, devido ao facto de ser um método flexível e capaz de segmentar casos mais complexos. Antes de realizar a segmentação da nossa base de dados, efetuou-se IV uma análise sobre os softwares utilizados, as estratégias de segmentação e as propriedades de ambos, onde foi possível observar o modus operandi de cada um, assim como as diferenças entre estes. De seguida realizou-se o processamento das imagens da amostra, composta por 35 casos com diferentes estados de saúde (saudável, presença de tumor ou quisto, epilepsia, autismo), de ambos os sexos e com idades entre os 5 e os 50 anos. No fim da segmentação, ambos forneceram dados volumétricos das estruturas subcorticais, que foram devidamente tabelados a fim de serem analisados e comparados. Para uma melhor visualização comparativa da diferença dos volumes obtidos realizou-se uma rede no MeVisLab que permitiu a sobreposição de ambos os resultados. Os resultados demonstram que o FreeSurfer fornece valores, no geral, significativamente superiores aos do VolBrain, em alguns casos mais relevantes que outros. Tais diferenças são possíveis devido a questões algorítmicas e de pipeline. O VolBrain foi considerado mais fiável a nível de resultados que o FreeSurfer, pois este último tem tendência a superestimar as estruturas subcorticais.Magnetic resonance imaging (MRI) is a diagnostic imaging technique frequently present in the clinical practice and in constant development. It is a modern and sophisticated method of image and signal acquisition, with high image quality, relevant to cerebral volumetry. Volumetry associated with MRI facilitates the comparison of volumetric data allowing to obtain more solid data on the volumes of cerebral structures. Currently, the interest in the development of methodologies for the analyses of structures and volumetric measurement has been increasing, so it is desirable that it becomes a more automated, fast and efficient method and able to perform image segmentation with reduced operator intervention. This experimental study aims to compare the volume of subcortical structures between two different softwares to test the robustness of both. The softwares used, FreeSurfer and VolBrain, implements (semi) automatic segmentation strategies, following different algorithmic paradigms. Both softwares are freely available and are used for cerebral anatomy studies, including the segmentation of cortical and subcortical anatomy, providing the respective volumes. Initially, a study was made focusing on the concepts of MR imaging and on the existing deformable segmentation strategies. The segmentation by deformable models proved to be productive with promising results, due to the fact that it is a flexible method capable of segmenting more complex cases. Before segmenting our data, we analyzed the characteristics of the softwares used, the segmentation strategies and the properties of both, being possible to observe the modus operandi of each one, as well as the differences between them. Next, the images of the sample, composed of VI 35 cases with different health states (healthy, presence of tumor or cyst, epilepsy, autism), of both genders and aged between 5 and 50 years, were processed. At the end of segmentation, both provided volumetric data from subcortical structures, which were tabulated for analysis and comparisons. For a better comparative visualization of the difference of the obtained volumes, a network in MeVisLab was performed to inspect the overlap of both results. The results showed that FreeSurfer provides values that are generally significantly higher than those of VolBrain, in some cases more relevant than others. Such differences are possible due to algorithmic and pipeline issues. VolBrain was considered more reliable in terms of results than FreeSurfer, since the latter tends to overestimate the subcortical structures.Mestrado em Tecnologias da Imagem Médic

Visual analytics methods for shape analysis of biomedical images exemplified on rodent skull morphology

In morphometrics and its application fields like medicine and biology experts are interested in causal relations of variation in organismic shape to phylogenetic, ecological, geographical, epidemiological or disease factors - or put more succinctly by Fred L. Bookstein, morphometrics is "the study of covariances of biological form". In order to reveal causes for shape variability, targeted statistical analysis correlating shape features against external and internal factors is necessary but due to the complexity of the problem often not feasible in an automated way. Therefore, a visual analytics approach is proposed in this thesis that couples interactive visualizations with automated statistical analyses in order to stimulate generation and qualitative assessment of hypotheses on relevant shape features and their potentially affecting factors. To this end long established morphometric techniques are combined with recent shape modeling approaches from geometry processing and medical imaging, leading to novel visual analytics methods for shape analysis. When used in concert these methods facilitate targeted analysis of characteristic shape differences between groups, co-variation between different structures on the same anatomy and correlation of shape to extrinsic attributes. Here a special focus is put on accurate modeling and interactive rendering of image deformations at high spatial resolution, because that allows for faithful representation and communication of diminutive shape features, large shape differences and volumetric structures. The utility of the presented methods is demonstrated in case studies conducted together with a collaborating morphometrics expert. As exemplary model structure serves the rodent skull and its mandible that are assessed via computed tomography scans