101 research outputs found
Compensation du mouvement respiratoire dans les images TEP
National audienceLa qualité des images de tomographie par émission de positon (TEP) est actuellement limitée par les effets du mouvement respiratoire. Ce mouvement introduit des artéfacts qui altÚrent l'interprétation des images et fausse le diagnostic. Les méthodes proposées dans la littérature pour corriger ces artéfacts, sont généralement basées sur l'acquisition avec gating respiratoire. Ces techniques ont un mauvais rapport signal sur bruit réduisant considérablement la qualité des images. Dans ce papier, nous proposons une méthode alternative pour une meilleure reconstruction des images TEP. Elle consiste à acquérir des images CT et TEP synchronisées à la respiration. l'acquisition des images CT est effectuée en mode ciné. Par ailleurs, les données brutes TEP sont acquises, filtrées et classées dans des bins. Le mouvement respiratoire inter-bins est obtenu par recalage non-rigide des images CT correspondantes. Dans ce travail, nous proposons de compenser ce mouvement avant la reconstruction TEP en appliquant directement la déformation estimée à la matrice systÚme. Pour valider notre méthode, nous avons mis en oeuvre deux expérimentations. La premiÚre consiste à valider la méthode de traitement du signal respiratoire à l'aide de données réelles de type RPM VARIAN. La deuxiÚme permet de valider la compensation du mouvement en utilisant NCAT et GATE. Les résultats obtenus confirment le potentiel de la méthode
Correction de mouvement respiratoire en TDM-4D par interpolation bidirectionnelle pondérée
Session "Articles"National audienceCet article traite de la reconstruction 4D d'images de tomodensitomĂ©trie. Les techniques TDM-4D existantes souffrent de l'effet du mouvement respiratoire qui altĂšre la localisation des organes et l'activitĂ© des tumeurs. Ătant donnĂ© une sĂ©quence de coupes acquises Ă diffĂ©rentes positions respiratoires, la mĂ©thode proposĂ©e permet d'interpoler les coupes manquantes par compensation du mouvement non-rigide reprĂ©sentant les dĂ©formations anatomiques. Les champs de vecteurs estimĂ©s par recalage sont inversĂ©s et utilisĂ©s dans notre modĂšle d'interpolation bidirectionnelle. Une mĂ©thode de reconstruction 4D permet de produire une image 3D du corps Ă tout niveau respiratoire. Plusieurs expĂ©rimentations sur des donnĂ©es de fantĂŽme NCAT et des images rĂ©elles du thorax sont prĂ©sentĂ©es. Des indicateurs quantitatifs ont Ă©tĂ© Ă©laborĂ©s. Les rĂ©sultats montrent une amĂ©lioration significative de la prĂ©cision de la reconstructio
Estimating the granularity coefficient of a Potts-Markov random field within an MCMC algorithm
This paper addresses the problem of estimating the Potts parameter B jointly
with the unknown parameters of a Bayesian model within a Markov chain Monte
Carlo (MCMC) algorithm. Standard MCMC methods cannot be applied to this problem
because performing inference on B requires computing the intractable
normalizing constant of the Potts model. In the proposed MCMC method the
estimation of B is conducted using a likelihood-free Metropolis-Hastings
algorithm. Experimental results obtained for synthetic data show that
estimating B jointly with the other unknown parameters leads to estimation
results that are as good as those obtained with the actual value of B. On the
other hand, assuming that the value of B is known can degrade estimation
performance significantly if this value is incorrect. To illustrate the
interest of this method, the proposed algorithm is successfully applied to real
bidimensional SAR and tridimensional ultrasound images
Computing the Cramer-Rao bound of Markov random field parameters: Application to the Ising and the Potts models
This report considers the problem of computing the Cramer-Rao bound for the
parameters of a Markov random field. Computation of the exact bound is not
feasible for most fields of interest because their likelihoods are intractable
and have intractable derivatives. We show here how it is possible to formulate
the computation of the bound as a statistical inference problem that can be
solve approximately, but with arbitrarily high accuracy, by using a Monte Carlo
method. The proposed methodology is successfully applied on the Ising and the
Potts models.% where it is used to assess the performance of three state-of-the
art estimators of the parameter of these Markov random fields
Segmentation of skin lesions in 2D and 3D ultrasound images using a spatially coherent generalized Rayleigh mixture model
This paper addresses the problem of jointly estimating the statistical distribution and segmenting lesions in multiple-tissue high-frequency skin ultrasound images. The distribution of multiple-tissue images is modeled as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is modeled by enforcing local dependence between the mixture components. An original Bayesian algorithm combined with a Markov chain Monte Carlo method is then proposed to jointly estimate the mixture parameters and a label-vector associating each voxel to a tissue. More precisely, a hybrid Metropolis-within-Gibbs sampler is used to draw samples that are asymptotically distributed according to the posterior distribution of the Bayesian model. The Bayesian estimators of the model parameters are then computed from the generated samples. Simulation results are conducted on synthetic data to illustrate the performance of the proposed estimation strategy. The method is then successfully applied to the segmentation of in vivo skin tumors in high-frequency 2-D and 3-D ultrasound images
A Tool Set Combining Learning Styles Prediction, a Blended Learning Methodology and Facilitator Guidebooks â Towards a Best Mix in Blended Learning
One of the challenges in the development in blended learning is to facilitate the individual learning styles of the learners. The alignment of a learning styles assessment with a learning methodology, a mapping between learning styles and social media, recommendations in a guidebook for facilitators and a checklist provide a tool set for a sustainable approach for a responsive learning environment. This paper analyzes how the different approaches, methods and studies interact to form an overall tool set to develop a learnercentered mix in blended learning. It proposes a tool set to adapt blended learning to the learning styles of the learners
Un modÚle Bayésien de mélange de lois Poisson-Gamma pour segmenter des images TEP
Session "Posters"National audienceCet article présente un algorithme Bayésien pour la segmentation d'images de Tomographie par Emission de Positons (TEP). Tenant compte des phénomÚnes physiques sous-jacents à la formation de l'image TEP, nous modélisons l'activité des tissus comme un mélange de distributions Poisson-Gamma. Un algorithme Bayésien hiérarchique de type Monte Carlo par chaßne de Markov (MCMC) permet d'estimer conjointement les paramÚtres du modÚle et de classifier les voxels selon la nature des tissus. De plus, un champ de Potts-Markov permet de représenter la cohérence spatiale des classes dans le modÚle Bayésien. L'algorithme a été validé sur des données synthétiques et testé sur des données provenant de patients réels. Les résultats de la segmentation d'images TEP de l'abdomen suggÚrent que la méthode proposée peut correctement mettre en évidence autant les grosses que les petites tumeur
Sparse EEG Source Localization Using Bernoulli Laplacian Priors
International audienceSource localization in electroencephalography has received an increasing amount of interest in the last decade. Solving the underlying ill-posed inverse problem usually requires choosing an appropriate regularization. The usual l2 norm has been considered and provides solutions with low computational complexity. However, in several situations, realistic brain activity is believed to be focused in a few focal areas. In these cases, the l2 norm is known to overestimate the activated spatial areas. One solution to this problem is to promote sparse solutions for instance based on the l1 norm that are easy to handle with optimization techniques. In this paper, we consider the use of an l0 + l1 norm to enforce sparse source activity (by ensuring the solution has few nonzero elements) while regularizing the nonzero amplitudes of the solution. More precisely, the l0 pseudonorm handles the position of the non zero elements while the l1 norm constrains the values of their amplitudes. We use a BernoulliâLaplace prior to introduce this combined l0 + l1 norm in a Bayesian framework. The proposed Bayesian model is shown to favor sparsity while jointly estimating the model hyperparameters using a Markov chain Monte Carlo sampling technique. We apply the model to both simulated and real EEG data, showing that the proposed method provides better results than the l2 and l1 norms regularizations in the presence of pointwise sources. A comparison with a recent method based on multiple sparse priors is also conducted
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