Use of a dynamic foot pressure index to monitor the effects of treatment for equinus gait in children with cerebral palsy.

The purpose of this study is to introduce and describe a newly developed index using foot pressure analysis to quantify the degree of equinus gait in children with cerebral palsy before and after injection with botulinum toxin. Data were captured preinjection and 12 weeks postinjection. Ten children aged 2(1/2) to 6(1/2) years took part (5 boys and 5 girls). Three of them had a diagnosis of spastic diplegia and 7 of congenital hemiplegia. In total, 13 limbs were analyzed. After orientation and segmentation of raw pedobarographic data, we determined a dynamic foot pressure index graded 0 to 100 that quantified the relative degree of heel and forefoot contact during stance. These data were correlated (Pearson correlation) with clinical measurements of dorsiflexion at the ankle (on a slow and fast stretch) and video observation (using the Observational Gait Scale). Pedobarograph data were strongly correlated with both the Observational Gait Scale scores (R = 0.79, P < 0.005) and clinical measurements of dorsiflexion on a fast stretch, which is reflective of spasticity (R = 0.70, P < 0.005). We demonstrated the index's sensitivity in detecting changes in spasticity and good correlation with video observations seems to indicate this technique's potential validity. When manipulated and segmented appropriately, and with the development of a simple ordinal index, we found that foot pressure data provided a useful tool in tracking changes in patients with spastic equinus.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Auditory spatio-temporal brain dynamics and their consequences for multisensory interactions in humans.

Recent multisensory research has emphasized the occurrence of early, low-level interactions in humans. As such, it is proving increasingly necessary to also consider the kinds of information likely extracted from the unisensory signals that are available at the time and location of these interaction effects. This review addresses current evidence regarding how the spatio-temporal brain dynamics of auditory information processing likely curtails the information content of multisensory interactions observable in humans at a given latency and within a given brain region. First, we consider the time course of signal propagation as a limitation on when auditory information (of any kind) can impact the responsiveness of a given brain region. Next, we overview the dual pathway model for the treatment of auditory spatial and object information ranging from rudimentary to complex environmental stimuli. These dual pathways are considered an intrinsic feature of auditory information processing, which are not only partially distinct in their associated brain networks, but also (and perhaps more importantly) manifest only after several tens of milliseconds of cortical signal processing. This architecture of auditory functioning would thus pose a constraint on when and in which brain regions specific spatial and object information are available for multisensory interactions. We then separately consider evidence regarding mechanisms and dynamics of spatial and object processing with a particular emphasis on when discriminations along either dimension are likely performed by specific brain regions. We conclude by discussing open issues and directions for future research.

Flow-targeted inversion-prepared b-TFE coronary MR angiography: initial results in patients.

PURPOSE: Visualization of coronary blood flow in the right and left coronary system in volunteers and patients by means of a modified inversion-prepared bright-blood coronary magnetic resonance angiography (cMRA) sequence. MATERIALS AND METHODS: cMRA was performed in 14 healthy volunteers and 19 patients on a 1.5 Tesla MR system using a free-breathing 3D balanced turbo field echo (b-TFE) sequence with radial k-space sampling. For magnetization preparation a slab selective and a 2D selective inversion pulse were used for the right and left coronary system, respectively. cMRA images were evaluated in terms of clinically relevant stenoses (< 50 %) and compared to conventional catheter angiography. Signal was measured in the coronary arteries (coro), the aorta (ao) and in the epicardial fat (fat) to determine SNR and CNR. In addition, maximal visible vessel length, and vessel border definition were analyzed. RESULTS: The use of a selective inversion pre-pulse allowed direct visualization of the coronary blood flow in the right and left coronary system. The measured SNR and CNR, vessel length, and vessel sharpness in volunteers (SNR coro: 28.3 +/- 5.0; SNR ao: 37.6 +/- 8.4; CNR coro-fat: 25.3 +/- 4.5; LAD: 128.0 cm +/- 8.8; RCA: 74.6 cm +/- 12.4; Sharpness: 66.6 % +/- 4.8) were slightly increased compared to those in patients (SNR coro: 24.1 +/- 3.8; SNR ao: 33.8 +/- 11.4; CNR coro-fat: 19.9 +/- 3.3; LAD: 112.5 cm +/- 13.8; RCA: 69.6 cm +/- 16.6; Sharpness: 58.9 % +/- 7.9; n.s.). In the patient study the assessment of 42 coronary segments lead to correct identification of 10 clinically relevant stenoses. CONCLUSION: The modification of a previously published inversion-prepared cMRA sequence allowed direct visualization of the coronary blood flow in the right as well as in the left coronary system. In addition, this sequence proved to be highly sensitive regarding the assessment of clinically relevant stenotic lesions.

The timing of exploratory decision-making revealed by single-trial topographic EEGanalyses.

Decision-making in an uncertain environment is driven by two major needs: exploring the environment to gather information or exploiting acquired knowledge to maximize reward. The neural processes underlying exploratory decision-making have been mainly studied by means of functional magnetic resonance imaging, overlooking any information about the time when decisions are made. Here, we carried out an electroencephalography (EEG) experiment, in order to detect the time when the brain generators responsible for these decisions have been sufficiently activated to lead to the next decision. Our analyses, based on a classification scheme, extract time-unlocked voltage topographies during reward presentation and use them to predict the type of decisions made on the subsequent trial. Classification accuracy, measured as the area under the Receiver Operator's Characteristic curve was on average 0.65 across 7 subjects. Classification accuracy was above chance levels already after 516 ms on average, across subjects. We speculate that decisions were already made before this critical period, as confirmed by a positive correlation with reaction times across subjects. On an individual subject basis, distributed source estimations were performed on the extracted topographies to statistically evaluate the neural correlates of decision-making. For trials leading to exploration, there was significantly higher activity in dorsolateral prefrontal cortex and the right supramarginal gyrus; areas responsible for modulating behavior under risk and deduction. No area was more active during exploitation. We show for the first time the temporal evolution of differential patterns of brain activation in an exploratory decision-making task on a single-trial basis.

Mixed spectral-structural classification of very high resolution images with summation kernels

In this paper, mixed spectral-structural kernel machines are proposed for the classification of very-high resolution images. The simultaneous use of multispectral and structural features (computed using morphological filters) allows a significant increase in classification accuracy of remote sensing images. Subsequently, weighted summation kernel support vector machines are proposed and applied in order to take into account the multiscale nature of the scene considered. Such classifiers use the Mercer property of kernel matrices to compute a new kernel matrix accounting simultaneously for two scale parameters. Tests on a Zurich QuickBird image show the relevance of the proposed method : using the mixed spectral-structural features, the classification accuracy increases of about 5%, achieving a Kappa index of 0.97. The multikernel approach proposed provide an overall accuracy of 98.90% with related Kappa index of 0.985.

Fast Bias Field Correction for 9.4 Tesla Magnetic Resonance Imaging

In this paper the problem of intensity inhomogeneity athigh magnetic field on magnetic resonance images isaddressed. Specifically, rat brain images at 9.4Tacquired with a surface coil are bias corrected. Wepropose a low- pass frequency model that takes intoaccount not only background-object contours but alsoother important contours inside the image. Twopre-processing filters are proposed: first, to create avolume of interest without contours, and second, toextrapolate the image values of such masked area to thewhole image. Results are assessed quantitatively andvisually in comparison to standard low pass filterapproach, and they show as expected better accuracy inenhancing image intensity.

Étude des mécanismes de transfert des fibres en sciences forensiques

RESUME Les fibres textiles sont des produits de masse utilisés dans la fabrication de nombreux objets de notre quotidien. Le transfert de fibres lors d'une action délictueuse est dès lors extrêmement courant. Du fait de leur omniprésence dans notre environnement, il est capital que l'expert forensique évalue la valeur de l'indice fibres. L'interprétation de l'indice fibres passe par la connaissance d'un certain nombre de paramètres, comme la rareté des fibres, la probabilité de leur présence par hasard sur un certain support, ainsi que les mécanismes de transfert et de persistance des fibres. Les lacunes les plus importantes concernent les mécanismes de transfert des fibres. A ce jour, les nombreux auteurs qui se sont penchés sur le transfert de fibres ne sont pas parvenus à créer un modèle permettant de prédire le nombre de fibres que l'on s'attend à retrouver dans des circonstances de contact données, en fonction des différents paramètres caractérisant ce contact et les textiles mis en jeu. Le but principal de cette recherche est de démontrer que la création d'un modèle prédictif du nombre de fibres transférées lors d'un contact donné est possible. Dans le cadre de ce travail, le cas particulier du transfert de fibres d'un tricot en laine ou en acrylique d'un conducteur vers le dossier du siège de son véhicule a été étudié. Plusieurs caractéristiques des textiles mis en jeu lors de ces expériences ont été mesurées. Des outils statistiques (régression linéaire multiple) ont ensuite été utilisés sur ces données afin d'évaluer l'influence des caractéristiques des textiles donneurs sur le nombre de fibres transférées et d'élaborer un modèle permettant de prédire le nombre de fibres qui vont être transférées à l'aide des caractéristiques influençant significativement le transfert. Afin de faciliter la recherche et le comptage des fibres transférées lors des expériences de transfert, un appareil de recherche automatique des fibres (liber finder) a été utilisé dans le cadre de cette recherche. Les tests d'évaluation de l'efficacité de cet appareil pour la recherche de fibres montrent que la recherche automatique est globalement aussi efficace qu'une recherche visuelle pour les fibres fortement colorées. Par contre la recherche automatique perd de son efficacité pour les fibres très pâles ou très foncées. Une des caractéristiques des textiles donneurs à étudier est la longueur des fibres. Afin de pouvoir évaluer ce paramètre, une séquence d'algorithmes de traitement d'image a été implémentée. Cet outil permet la mesure de la longueur d'une fibre à partir de son image numérique à haute résolution (2'540 dpi). Les tests effectués montrent que les mesures ainsi obtenues présentent une erreur de l'ordre du dixième de millimètre, ce qui est largement suffisant pour son utilisation dans le cadre de cette recherche. Les résultats obtenus suite au traitement statistique des résultats des expériences de transfert ont permis d'aboutir à une modélisation du phénomène du transfert. Deux paramètres sont retenus dans le modèle: l'état de la surface du tissu donneur et la longueur des fibres composant le tissu donneur. L'état de la surface du tissu est un paramètre tenant compte de la quantité de fibres qui se sont détachées de la structure du tissu ou qui sont encore faiblement rattachées à celle-ci. En effet, ces fibres sont les premières à se transférer lors d'un contact, et plus la quantité de ces fibres par unité de surface est importante, plus le nombre de fibres transférées sera élevé. La longueur des fibres du tissu donneur est également un paramètre important : plus les fibres sont longues, mieux elles sont retenues dans la structure du tissu et moins elles se transféreront. SUMMARY Fibres are mass products used to produce numerous objects encountered everyday. The transfer of fibres during a criminal action is then very common. Because fibres are omnipresent in our environment, the forensic expert has to evaluate the value of the fibre evidence. To interpret fibre evidence, the expert has to know some parameters as frequency of fibres,' probability of finding extraneous fibres by chance on a given support, and transfer and persistence mechanisms. Fibre transfer is one of the most complex parameter. Many authors studied fibre transfer mechanisms but no model has been created to predict the number of fibres transferred expected in a given type of contact according to parameters as characteristics of the contact and characteristics of textiles. The main purpose of this research is to demonstrate that it is possible to create a model to predict the number of fibres transferred during a contact. In this work, the particular case of the transfer of fibres from a knitted textile in wool or in acrylic of a driver to the back of a carseat has been studied. Several characteristics of the textiles used for the experiments were measured. The data obtained were then treated with statistical tools (multiple linear regression) to evaluate the influence of the donor textile characteristics on the number of úbers transferred, and to create a model to predict this number of fibres transferred by an equation containing the characteristics having a significant influence on the transfer. To make easier the searching and the counting of fibres, an apparatus of automatic search. of fibers (fiber finder) was used. The tests realised to evaluate the efficiency of the fiber finder shows that the results obtained are generally as efficient as for visual search for well-coloured fibres. However, the efficiency of automatic search decreases for pales and dark fibres. One characteristic of the donor textile studied was the length of the fibres. To measure this parameter, a sequence of image processing algorithms was implemented. This tool allows to measure the length of a fibre from it high-resolution (2'540 dpi) numerical image. The tests done shows that the error of the measures obtained are about some tenths of millimetres. This precision is sufficient for this research. The statistical methods applied on the transfer experiment data allow to create a model of the transfer phenomenon. Two parameters are included in the model: the shedding capacity of the donor textile surface and the length of donor textile fibres. The shedding capacity of the donor textile surface is a parameter estimating the quantity of fibres that are not or slightly attached to the structure of the textile. These fibres are easily transferred during a contact, and the more this quantity of fibres is high, the more the number of fibres transferred during the contact is important. The length of fibres is also an important parameter: the more the fibres are long, the more they are attached in the structure of the textile and the less they are transferred during the contact.

Structured sparsity through reweighting and application to diffusion MRI

We consider the problem of multiple correlated sparse signals reconstruction and propose a new implementation of structured sparsity through a reweighting scheme. We present a particular application for diffusion Magnetic Resonance Imaging data and show how this procedure can be used for fibre orientation reconstruction in the white matter of the brain. In that framework, our structured sparsity prior can be used to exploit the fundamental coherence between fibre directions in neighbour voxels. Our method approaches the ℓ0 minimisation through a reweighted ℓ1-minimisation scheme. The weights are here defined in such a way to promote correlated sparsity between neighbour signals.