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.

Relaxivity of Gd-based contrast agents on X nuclei with long intrinsic relaxation times in aqueous solutions.

The relaxivity of commercially available gadolinium (Gd)-based contrast agents was studied for X-nuclei resonances with long intrinsic relaxation times ranging from 6 s to several hundred seconds. Omniscan in pure 13C formic acid had a relaxivity of 2.9 mM(-1) s(-1), whereas its relaxivity on glutamate C1 and C5 in aqueous solution was approximately 0.5 mM(-1) s(-1). Both relaxivities allow the preparation of solutions with a predetermined short T1 and suggest that in vitro substantial sensitivity gains in their measurement can be achieved. 6Li has a long intrinsic relaxation time, on the order of several minutes, which was strongly affected by the contrast agents. Relaxivity ranged from approximately 0.1 mM(-1) s(-1) for Omniscan to 0.3 for Magnevist, whereas the relaxivity of Gd-DOTP was at 11 mM(-1) s(-1), which is two orders of magnitude higher. Overall, these experiments suggest that the presence of 0.1- to 10-microM contrast agents should be detectable, provided sufficient sensitivity is available, such as that afforded by hyperpolarization, recently introduced to in vivo imaging.

MRI-based prostate brachytherapy seed localization.

A magnetic resonance imaging (MRI) pulse sequence and a corresponding image processing algorithm to localize prostate brachytherapy seeds during or after therapy are presented. Inversion-Recovery with ON-resonant water suppression (IRON) is an MRI methodology that generates positive contrast in regions of magnetic field susceptibility, as created by prostate brachytherapy seeds. Phantoms comprising of several materials found in brachytherapy seeds were created to assess the usability of the IRON pulse sequence for imaging seeds. Resulting images show that seed materials are clearly visible with high contrast using IRON, agreeing with theoretical predictions. A seed localization algorithm to process IRON images demonstrates the potential of this imaging technique for seed localization and dosimetry.

Changes in volume, surface estimate, three-dimensional shape and total number of neurons of the human primary visual cortex from midgestation until old age.

Macroscopic features such as volume, surface estimate, thickness and caudorostral length of the human primary visual cortex (Brodman's area 17) of 46 human brains between midgestation and 93 years were studied by means of camera lucida drawings from serial frontal sections. Individual values were best fitted by a logistic function from midgestation to adulthood and by a regression line between adulthood and old age. Allometric functions were calculated to study developmental relationships between all the features. The three-dimensional shape of area 17 was also reconstructed from the serial sections in 15 cases and correlated with the sequence of morphological events. The sulcal pattern of area 17 begins to develop around 21 weeks of gestation but remains rather simple until birth, while it becomes more convoluted, particularly in the caudal part, during the postnatal period. Until birth, a large increase in cortical thickness (about 83% of its mean adult value) and caudorostral length (69%) produces a moderate increase in cortical volume (31%) and surface estimate (40%) of area 17. After birth, the cortical volume and surface undergo their maximum growth rate, in spite of a rather small increase in cortical thickness and caudorostral length. This is due to the development of the pattern of gyrification within and around the calcarine fissure. All macroscopic features have reached the mean adult value by the end of the first postnatal year. With aging, the only features to undergo significant regression are the cortical surface estimate and the caudorostral length. The total number of neurons in area 17 shows great interindividual variability at all ages. No decrease in the postnatal period or in aging could be demonstrated.

MBIS: multivariate Bayesian image segmentation tool.

We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multichannel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge.

An active contour-based atlas registration model applied to automatic subthalamic nucleus targeting on MRI: method and validation.

This paper presents a new non parametric atlas registration framework, derived from the optical flow model and the active contour theory, applied to automatic subthalamic nucleus (STN) targeting in deep brain stimulation (DBS) surgery. In a previous work, we demonstrated that the STN position can be predicted based on the position of surrounding visible structures, namely the lateral and third ventricles. A STN targeting process can thus be obtained by registering these structures of interest between a brain atlas and the patient image. Here we aim to improve the results of the state of the art targeting methods and at the same time to reduce the computational time. Our simultaneous segmentation and registration model shows mean STN localization errors statistically similar to the most performing registration algorithms tested so far and to the targeting expert's variability. Moreover, the computational time of our registration method is much lower, which is a worthwhile improvement from a clinical point of view.

Active Contour-Based Segmentation of Head and Neck with Adaptive Atlas Selection

This paper presents automated segmentation of structuresin the Head and Neck (H\&N) region, using an activecontour-based joint registration and segmentation model.A new atlas selection strategy is also used. Segmentationis performed based on the dense deformation fieldcomputed from the registration of selected structures inthe atlas image that have distinct boundaries, onto thepatient's image. This approach results in robustsegmentation of the structures of interest, even in thepresence of tumors, or anatomical differences between theatlas and the patient image. For each patient, an atlasimage is selected from the available atlas-database,based on the similarity metric value, computed afterperforming an affine registration between each image inthe atlas-database and the patient's image. Unlike manyof the previous approaches in the literature, thesimilarity metric is not computed over the entire imageregion; rather, it is computed only in the regions ofsoft tissue structures to be segmented. Qualitative andquantitative evaluation of the results is presented.