Performance comparison of two commercial BGO-based PET/CT scanners using NEMA NU 2-2001.

Combined positron emission tomography and computed tomography (PET/CT) scanners play a major role in medicine for in vivo imaging in an increasing number of diseases in oncology, cardiology, neurology, and psychiatry. With the advent of short-lived radioisotopes other than 18F and newer scanners, there is a need to optimize radioisotope activity and acquisition protocols, as well as to compare scanner performances on an objective basis. The Discovery-LS (D-LS) was among the first clinical PET/CT scanners to be developed and has been extensively characterized with older National Electrical Manufacturer Association (NEMA) NU 2-1994 standards. At the time of publication of the latest version of the standards (NU 2-2001) that have been adapted for whole-body imaging under clinical conditions, more recent models from the same manufacturer, i.e., Discovery-ST (D-ST) and Discovery-STE (D-STE), were commercially available. We report on the full characterization both in the two- and three-dimensional acquisition mode of the D-LS according to latest NEMA NU 2-2001 standards (spatial resolution, sensitivity, count rate performance, accuracy of count losses, and random coincidence correction and image quality), as well as a detailed comparison with the newer D-ST widely used and whose characteristics are already published.

A surface-based approach to quantify local cortical gyrification.

The high complexity of cortical convolutions in humans is very challenging both for engineers to measure and compare it, and for biologists and physicians to understand it. In this paper, we propose a surface-based method for the quantification of cortical gyrification. Our method uses accurate 3-D cortical reconstruction and computes local measurements of gyrification at thousands of points over the whole cortical surface. The potential of our method to identify and localize precisely gyral abnormalities is illustrated by a clinical study on a group of children affected by 22q11 Deletion Syndrome, compared to control individuals.

Decoding stimulus-related information from single-trial EEG responses based on voltage topographies

Neuroimaging studies typically compare experimental conditions using average brain responses, thereby overlooking the stimulus-related information conveyed by distributed spatio-temporal patterns of single-trial responses. Here, we take advantage of this rich information at a single-trial level to decode stimulus-related signals in two event-related potential (ERP) studies. Our method models the statistical distribution of the voltage topographies with a Gaussian Mixture Model (GMM), which reduces the dataset to a number of representative voltage topographies. The degree of presence of these topographies across trials at specific latencies is then used to classify experimental conditions. We tested the algorithm using a cross-validation procedure in two independent EEG datasets. In the first ERP study, we classified left- versus right-hemifield checkerboard stimuli for upper and lower visual hemifields. In a second ERP study, when functional differences cannot be assumed, we classified initial versus repeated presentations of visual objects. With minimal a priori information, the GMM model provides neurophysiologically interpretable features - vis à vis voltage topographies - as well as dynamic information about brain function. This method can in principle be applied to any ERP dataset testing the functional relevance of specific time periods for stimulus processing, the predictability of subject's behavior and cognitive states, and the discrimination between healthy and clinical populations.

Weight status, body image and bullying among adolescents in the Seychelles.

We investigated the relationship between being bullied and measured body weight and perceived body weight among adolescents of a middle-income sub Saharan African country. Our data originated from the Global School-based Health Survey, which targets adolescents aged 13-15 years. Student weights and heights were measured before administrating the questionnaire which included questions about personal data, health behaviors and being bullied. Standard criteria were used to assess thinness, overweight and obesity. Among 1,006 participants who had complete data, 16.5% (95%CI 13.3-20.2) reported being bullied ≥ 3 days during the past 30 days; 13.4% were thin, 16.8% were overweight and 7.6% were obese. Categories of actual weight and of perceived weight correlated only moderately (Spearman correlation coefficient 0.37 for boys and 0.57 for girls; p < 0.001). In univariate analysis, both actual obesity (OR 1.76; p = 0.051) and perception of high weight (OR 1.63 for "slightly overweight"; OR 2.74 for "very overweight", both p < 0.05) were associated with being bullied. In multivariate analysis, ORs for categories of perceived overweight were virtually unchanged while ORs for actual overweight and obesity were substantially attenuated, suggesting a substantial role of perceived weight in the association with being bullied. Actual underweight and perceived thinness also tended to be associated with being bullied, although not significantly. Our findings suggest that more research attention be given to disentangling the significant association between body image, overweight and bullying among adolescents. Further studies in diverse populations are warranted.

Multisensory Integration and Perceptual Enhancement

We perceive our environment through multiple sensory channels. Nonetheless, research has traditionally focused on the investigation of sensory processing within single modalities. Thus, investigating how our brain integrates multisensory information is of crucial importance for understanding how organisms cope with a constantly changing and dynamic environment. During my thesis I have investigated how multisensory events impact our perception and brain responses, either when auditory-visual stimuli were presented simultaneously or how multisensory events at one point in time impact later unisensory processing. In "Looming signals reveal synergistic principles of multisensory integration" (Cappe, Thelen et al., 2012) we investigated the neuronal substrates involved in motion detection in depth under multisensory vs. unisensory conditions. We have shown that congruent auditory-visual looming (i.e. approaching) signals are preferentially integrated by the brain. Further, we show that early effects under these conditions are relevant for behavior, effectively speeding up responses to these combined stimulus presentations. In "Electrical neuroimaging of memory discrimination based on single-trial multisensory learning" (Thelen et al., 2012), we investigated the behavioral impact of single encounters with meaningless auditory-visual object parings upon subsequent visual object recognition. In addition to showing that these encounters lead to impaired recognition accuracy upon repeated visual presentations, we have shown that the brain discriminates images as soon as ~100ms post-stimulus onset according to the initial encounter context. In "Single-trial multisensory memories affect later visual and auditory object recognition" (Thelen et al., in review) we have addressed whether auditory object recognition is affected by single-trial multisensory memories, and whether recognition accuracy of sounds was similarly affected by the initial encounter context as visual objects. We found that this is in fact the case. We propose that a common underlying brain network is differentially involved during encoding and retrieval of images and sounds based on our behavioral findings. - Nous percevons l'environnement qui nous entoure à l'aide de plusieurs organes sensoriels. Antérieurement, la recherche sur la perception s'est focalisée sur l'étude des systèmes sensoriels indépendamment les uns des autres. Cependant, l'étude des processus cérébraux qui soutiennent l'intégration de l'information multisensorielle est d'une importance cruciale pour comprendre comment notre cerveau travail en réponse à un monde dynamique en perpétuel changement. Pendant ma thèse, j'ai ainsi étudié comment des événements multisensoriels impactent notre perception immédiate et/ou ultérieure et comment ils sont traités par notre cerveau. Dans l'étude " Looming signals reveal synergistic principles of multisensory integration" (Cappe, Thelen et al., 2012), nous nous sommes intéressés aux processus neuronaux impliqués dans la détection de mouvements à l'aide de l'utilisation de stimuli audio-visuels seuls ou combinés. Nos résultats ont montré que notre cerveau intègre de manière préférentielle des stimuli audio-visuels combinés s'approchant de l'observateur. De plus, nous avons montré que des effets précoces, observés au niveau de la réponse cérébrale, influencent notre comportement, en accélérant la détection de ces stimuli. Dans l'étude "Electrical neuroimaging of memory discrimination based on single-trial multisensory learning" (Thelen et al., 2012), nous nous sommes intéressés à l'impact qu'a la présentation d'un stimulus audio-visuel sur l'exactitude de reconnaissance d'une image. Nous avons étudié comment la présentation d'une combinaison audio-visuelle sans signification, impacte, au niveau comportementale et cérébral, sur la reconnaissance ultérieure de l'image. Les résultats ont montré que l'exactitude de la reconnaissance d'images, présentées dans le passé, avec un son sans signification, est inférieure à celle obtenue dans le cas d'images présentées seules. De plus, notre cerveau différencie ces deux types de stimuli très tôt dans le traitement d'images. Dans l'étude "Single-trial multisensory memories affect later visual and auditory object recognition" (Thelen et al., in review), nous nous sommes posés la question si l'exactitude de ia reconnaissance de sons était affectée de manière semblable par la présentation d'événements multisensoriels passés. Ceci a été vérifié par nos résultats. Nous avons proposé que cette similitude puisse être expliquée par le recrutement différentiel d'un réseau neuronal commun.

A wavelet-based method for the determination of the relative resolution between remotely sensed images

Spatial resolution is a key parameter of all remote sensing satellites and platforms. The nominal spatial resolution of satellites is a well-known characteristic because it is directly related to the area in ground that represents a pixel in the detector. Nevertheless, in practice, the actual resolution of a specific image obtained from a satellite is difficult to know precisely because it depends on many other factors such as atmospheric conditions. However, if one has two or more images of the same region, it is possible to compare their relative resolutions. In this paper, a wavelet-decomposition-based method for the determination of the relative resolution between two remotely sensed images of the same area is proposed. The method can be applied to panchromatic, multispectral, and mixed (one panchromatic and one multispectral) images. As an example, the method was applied to compute the relative resolution between SPOT-3, Landsat-5, and Landsat-7 panchromatic and multispectral images taken under similar as well as under very different conditions. On the other hand, if the true absolute resolution of one of the images of the pair is known, the resolution of the other can be computed. Thus, in the last part of this paper, a spatial calibrator that is designed and constructed to help compute the absolute resolution of a single remotely sensed image is described, and an example of its use is presented.

Visual activity predicts auditory recovery from deafness after adult cochlear implantation.

Modern cochlear implantation technologies allow deaf patients to understand auditory speech; however, the implants deliver only a coarse auditory input and patients must use long-term adaptive processes to achieve coherent percepts. In adults with post-lingual deafness, the high progress of speech recovery is observed during the first year after cochlear implantation, but there is a large range of variability in the level of cochlear implant outcomes and the temporal evolution of recovery. It has been proposed that when profoundly deaf subjects receive a cochlear implant, the visual cross-modal reorganization of the brain is deleterious for auditory speech recovery. We tested this hypothesis in post-lingually deaf adults by analysing whether brain activity shortly after implantation correlated with the level of auditory recovery 6 months later. Based on brain activity induced by a speech-processing task, we found strong positive correlations in areas outside the auditory cortex. The highest positive correlations were found in the occipital cortex involved in visual processing, as well as in the posterior-temporal cortex known for audio-visual integration. The other area, which positively correlated with auditory speech recovery, was localized in the left inferior frontal area known for speech processing. Our results demonstrate that the visual modality's functional level is related to the proficiency level of auditory recovery. Based on the positive correlation of visual activity with auditory speech recovery, we suggest that visual modality may facilitate the perception of the word's auditory counterpart in communicative situations. The link demonstrated between visual activity and auditory speech perception indicates that visuoauditory synergy is crucial for cross-modal plasticity and fostering speech-comprehension recovery in adult cochlear-implanted deaf patients.

Low-dose, simple, and fast grating-based X-ray phase-contrast imaging.

Phase sensitive X-ray imaging methods can provide substantially increased contrast over conventional absorption-based imaging and therefore new and otherwise inaccessible information. The use of gratings as optical elements in hard X-ray phase imaging overcomes some of the problems that have impaired the wider use of phase contrast in X-ray radiography and tomography. So far, to separate the phase information from other contributions detected with a grating interferometer, a phase-stepping approach has been considered, which implies the acquisition of multiple radiographic projections. Here we present an innovative, highly sensitive X-ray tomographic phase-contrast imaging approach based on grating interferometry, which extracts the phase-contrast signal without the need of phase stepping. Compared to the existing phase-stepping approach, the main advantages of this new method dubbed "reverse projection" are not only the significantly reduced delivered dose, without the degradation of the image quality, but also the much higher efficiency. The new technique sets the prerequisites for future fast and low-dose phase-contrast imaging methods, fundamental for imaging biological specimens and in vivo studies.

Using numerical classification of profiles based on Vis-NIR spectra to distinguish soils from the Piracicaba Region, Brazil

Considering that information from soil reflectance spectra is underutilized in soil classification, this paper aimed to evaluate the relationship of soil physical, chemical properties and their spectra, to identify spectral patterns for soil classes, evaluate the use of numerical classification of profiles combined with spectral data for soil classification. We studied 20 soil profiles from the municipality of Piracicaba, State of São Paulo, Brazil, which were morphologically described and classified up to the 3rd category level of the Brazilian Soil Classification System (SiBCS). Subsequently, soil samples were collected from pedogenetic horizons and subjected to soil particle size and chemical analyses. Their Vis-NIR spectra were measured, followed by principal component analysis. Pearson's linear correlation coefficients were determined among the four principal components and the following soil properties: pH, organic matter, P, K, Ca, Mg, Al, CEC, base saturation, and Al saturation. We also carried out interpretation of the first three principal components and their relationships with soil classes defined by SiBCS. In addition, numerical classification of the profiles based on the OSACA algorithm was performed using spectral data as a basis. We determined the Normalized Mutual Information (NMI) and Uncertainty Coefficient (U). These coefficients represent the similarity between the numerical classification and the soil classes from SiBCS. Pearson's correlation coefficients were significant for the principal components when compared to sand, clay, Al content and soil color. Visual analysis of the principal component scores showed differences in the spectral behavior of the soil classes, mainly among Argissolos and the others soils. The NMI and U similarity coefficients showed values of 0.74 and 0.64, respectively, suggesting good similarity between the numerical and SiBCS classes. For example, numerical classification correctly distinguished Argissolos from Latossolos and Nitossolos. However, this mathematical technique was not able to distinguish Latossolos from Nitossolos Vermelho férricos, but the Cambissolos were well differentiated from other soil classes. The numerical technique proved to be effective and applicable to the soil classification process.

Multi-Atlas based Segmentation of Head and Neck CT Images using Active Contour

This paper presents the segmentation of bilateral parotid glands in the Head and Neck (H&N) CT images using an active contour based atlas registration. We compare segmentation results from three atlas selection strategies: (i) selection of "single-most-similar" atlas for each image to be segmented, (ii) fusion of segmentation results from multiple atlases using STAPLE, and (iii) fusion of segmentation results using majority voting. Among these three approaches, fusion using majority voting provided the best results. Finally, we present a detailed evaluation on a dataset of eight images (provided as a part of H&N auto segmentation challenge conducted in conjunction with MICCAI-2010 conference) using majority voting strategy.

Advanced Kernel Methods For Remote Sensing Image Classification

Résumé Suite aux recentes avancées technologiques, les archives d'images digitales ont connu une croissance qualitative et quantitative sans précédent. Malgré les énormes possibilités qu'elles offrent, ces avancées posent de nouvelles questions quant au traitement des masses de données saisies. Cette question est à la base de cette Thèse: les problèmes de traitement d'information digitale à très haute résolution spatiale et/ou spectrale y sont considérés en recourant à des approches d'apprentissage statistique, les méthodes à noyau. Cette Thèse étudie des problèmes de classification d'images, c'est à dire de catégorisation de pixels en un nombre réduit de classes refletant les propriétés spectrales et contextuelles des objets qu'elles représentent. L'accent est mis sur l'efficience des algorithmes, ainsi que sur leur simplicité, de manière à augmenter leur potentiel d'implementation pour les utilisateurs. De plus, le défi de cette Thèse est de rester proche des problèmes concrets des utilisateurs d'images satellite sans pour autant perdre de vue l'intéret des méthodes proposées pour le milieu du machine learning dont elles sont issues. En ce sens, ce travail joue la carte de la transdisciplinarité en maintenant un lien fort entre les deux sciences dans tous les développements proposés. Quatre modèles sont proposés: le premier répond au problème de la haute dimensionalité et de la redondance des données par un modèle optimisant les performances en classification en s'adaptant aux particularités de l'image. Ceci est rendu possible par un système de ranking des variables (les bandes) qui est optimisé en même temps que le modèle de base: ce faisant, seules les variables importantes pour résoudre le problème sont utilisées par le classifieur. Le manque d'information étiquétée et l'incertitude quant à sa pertinence pour le problème sont à la source des deux modèles suivants, basés respectivement sur l'apprentissage actif et les méthodes semi-supervisées: le premier permet d'améliorer la qualité d'un ensemble d'entraînement par interaction directe entre l'utilisateur et la machine, alors que le deuxième utilise les pixels non étiquetés pour améliorer la description des données disponibles et la robustesse du modèle. Enfin, le dernier modèle proposé considère la question plus théorique de la structure entre les outputs: l'intègration de cette source d'information, jusqu'à présent jamais considérée en télédétection, ouvre des nouveaux défis de recherche. Advanced kernel methods for remote sensing image classification Devis Tuia Institut de Géomatique et d'Analyse du Risque September 2009 Abstract The technical developments in recent years have brought the quantity and quality of digital information to an unprecedented level, as enormous archives of satellite images are available to the users. However, even if these advances open more and more possibilities in the use of digital imagery, they also rise several problems of storage and treatment. The latter is considered in this Thesis: the processing of very high spatial and spectral resolution images is treated with approaches based on data-driven algorithms relying on kernel methods. In particular, the problem of image classification, i.e. the categorization of the image's pixels into a reduced number of classes reflecting spectral and contextual properties, is studied through the different models presented. The accent is put on algorithmic efficiency and the simplicity of the approaches proposed, to avoid too complex models that would not be used by users. The major challenge of the Thesis is to remain close to concrete remote sensing problems, without losing the methodological interest from the machine learning viewpoint: in this sense, this work aims at building a bridge between the machine learning and remote sensing communities and all the models proposed have been developed keeping in mind the need for such a synergy. Four models are proposed: first, an adaptive model learning the relevant image features has been proposed to solve the problem of high dimensionality and collinearity of the image features. This model provides automatically an accurate classifier and a ranking of the relevance of the single features. The scarcity and unreliability of labeled. information were the common root of the second and third models proposed: when confronted to such problems, the user can either construct the labeled set iteratively by direct interaction with the machine or use the unlabeled data to increase robustness and quality of the description of data. Both solutions have been explored resulting into two methodological contributions, based respectively on active learning and semisupervised learning. Finally, the more theoretical issue of structured outputs has been considered in the last model, which, by integrating outputs similarity into a model, opens new challenges and opportunities for remote sensing image processing.

Artificial-Intelligence-Based Optimization of the Management of Snow Removal Assets and Resources Final Report, October 2002

Geographic information systems (GIS) and artificial intelligence (AI) techniques were used to develop an intelligent snow removal asset management system (SRAMS). The system has been evaluated through a case study examining snow removal from the roads in Black Hawk County, Iowa, for which the Iowa Department of Transportation (Iowa DOT) is responsible. The SRAMS is comprised of an expert system that contains the logical rules and expertise of the Iowa DOT’s snow removal experts in Black Hawk County, and a geographic information system to access and manage road data. The system is implemented on a mid-range PC by integrating MapObjects 2.1 (a GIS package), Visual Rule Studio 2.2 (an AI shell), and Visual Basic 6.0 (a programming tool). The system could efficiently be used to generate prioritized snowplowing routes in visual format, to optimize the allocation of assets for plowing, and to track materials (e.g., salt and sand). A test of the system reveals an improvement in snowplowing time by 1.9 percent for moderate snowfall and 9.7 percent for snowstorm conditions over the current manual system.

The switch from conventional to atypical antipsychotic treatment should not be based exclusively on the presence of cognitive deficits. A pilot study in individuals with schizophrenia.

Background: Atypical antipsychotics provide better control of the negative and affective symptoms of schizophrenia when compared with conventional neuroleptics; nevertheless, their heightened ability to improve cognitive dysfunction remains a matter of debate. This study aimed to examine the changes in cognition associated with long-term antipsychotic treatment and to evaluate the effect of the type of antipsychotic (conventional versus novel antipsychotic drugs) on cognitive performance over time. Methods: In this naturalistic study, we used a comprehensive neuropsychological battery of tests to assess a sample of schizophrenia patients taking either conventional (n = 13) or novel antipsychotics (n = 26) at baseline and at two years after. Results: Continuous antipsychotic treatment regardless of class was associated with improvement on verbal fluency, executive functions, and visual and verbal memory. Patients taking atypical antipsychotics did not show greater cognitive enhancement over two years than patients taking conventional antipsychotics. Conclusions Although long-term antipsychotic treatment slightly improved cognitive function, the switch from conventional to atypical antipsychotic treatment should not be based exclusively on the presence of these cognitive deficits.

Bilateral Sequential NAION: A Comparison of Visual Outcomes in Fellow Eyes Using Quantitative Analysis of Goldmann Visual Fields

Purpose: Previous studies of the visual outcome in bilateral non-arteritic anterior ischemic optic neuropathy (NAION) have yielded conflicting results, specifically regarding congruity between fellow eyes. Prior studies have used measures of acuity and computerized perimetry but none has compared Goldmann visual field outcomes between fellow eyes. In order to better define the concordance of visual loss in this condition, we reviewed our cases of bilateral sequential NAION, including measures of visual acuity, pupillary function and both pattern and severity of visual field loss.Methods: We performed a retrospective chart review of 102 patients with a diagnosis of bilateral sequential NAION. Of the 102 patients, 86 were included in the study for analysis of final visual outcome between the affected eyes. Visual function was assessed using visual acuity, Goldmann visual fields, color vision and RAPD. A quantitative total visual field score and score per quadrant was analyzed for each eye using the numerical Goldmann visual field scoring method previously described by Esterman and colleagues. Based upon these scores, we calculated the total deviation and pattern deviation between fellow eyes and between eyes of different patients. Statistical significance was determined using nonparametric tests.Results: A statistically significant correlation was found between fellow eyes for multiple parameters, including logMAR visual acuity (P = 0.0101), global visual field (P = 0.0001), superior visual field (P = 0.0001), and inferior visual field (P = 0.0001). In addition, the mean deviation of both total (P = 0.0000000007) and pattern (P = 0.000000004) deviation analyses was significantly less between fellow eyes ("intra"-eyes) than between eyes of different patients ("inter"-eyes).Conclusions: Visual function between fellow eyes showed a fair to moderate correlation that was statistically significant. The pattern of vision loss was also more similar in fellow eyes than between eyes of different patients. These results may help allow better prediction of visual outcome for the second eye in patients with NAION. These findings may also be useful for evaluating efficacy of therapeutic interventions.

Performance of a new gadolinium-based intravascular contrast agent in free-breathing inversion-recovery 3D coronary MRA.

In three-dimensional (3D) coronary magnetic resonance angiography (MRA), the in-flow contrast between the coronary blood and the surrounding myocardium is attenuated as compared to thin-slab two-dimensional (2D) techniques. The application of a gadolinium (Gd)-based intravascular contrast agent may provide an additional source of signal and contrast by reducing T(1blood) and supporting the visualization of more distal or branching segments of the coronary arterial tree. In six healthy adults, the left coronary artery (LCA) system was imaged pre- and postcontrast with a 0.075-mmol/kg bodyweight dose of the intravascular contrast agent B-22956. For imaging, an optimized free-breathing, navigator-gated and -corrected 3D inversion recovery (IR) sequence was used. For comparison, state-of-the-art baseline 3D coronary MRA with T(2) preparation for non-exogenous contrast enhancement was acquired. The combination of IR 3D coronary MRA, sophisticated navigator technology, and B-22956 allowed for an extensive visualization of the LCA system. Postcontrast, a significant increase in both the signal-to-noise ratio (SNR; 46%, P < 0.05) and contrast-to-noise ratio (CNR; 160%, P < 0.01) was observed, while vessel sharpness of the left anterior descending (LAD) artery and the left coronary circumflex (LCX) were improved by 20% (P < 0.05) and 18% (P < 0.05), respectively.