MP2RAGE, a self bias-field corrected sequence for improved segmentation and T-1-mapping at high field

The large spatial inhomogeneity in transmit B, field (B-1(+)) observable in human MR images at hi h static magnetic fields (B-0) severely impairs image quality. To overcome this effect in brain T-1-weighted images the, MPRAGE sequence was modified to generate two different images at different inversion times MP2RAGE By combining the two images in a novel fashion, it was possible to create T-1-weigthed images where the result image was free of proton density contrast, T-2* contrast, reception bias field, and, to first order transmit field inhomogeneity. MP2RAGE sequence parameters were optimized using Bloch equations to maximize contrast-to-noise ratio per unit of time between brain tissues and minimize the effect of B-1(+) variations through space. Images of high anatomical quality and excellent brain tissue differentiation suitable for applications such as segmentation and voxel-based morphometry were obtained at 3 and 7 T. From such T-1-weighted images, acquired within 12 min, high-resolution 3D T-1 maps were routinely calculated at 7 T with sub-millimeter voxel resolution (0.65-0.85 mm isotropic). T-1 maps were validated in phantom experiments. In humans, the T, values obtained at 7 T were 1.15 +/- 0.06 s for white matter (WM) and 1.92 +/- 0.16 s for grey matter (GM), in good agreement with literature values obtained at lower spatial resolution. At 3 T, where whole-brain acquisitions with 1 mm isotropic voxels were acquired in 8 min the T-1 values obtained (0.81 +/- 0.03 S for WM and 1.35 +/- 0.05 for GM) were once again found to be in very good agreement with values in the literature. (C) 2009 Elsevier Inc. All rights reserved.

Avaluació de l'estàndard SQL : 1999 respecte les característiques orientades a l'objecte que suporta i la seva implementació als SGBD object-relacional

En aquest treball s'intenta fer una síntesi de les especificacions aportades per l'estàndard definit com a SQL: 1999, tot analitzant les ampliacions que fan referència a la nova orientació a l'objecte i a la incorporació de l'herència com a principal element diferenciador.

Avaluació de l'estàndard SQL : 1999 respecte les característiques orientades a l'objecte que suporta i la seva implementació als SGBD object-relational

L'objectiu és estudiar les característiques orientades a l'objecte de l'estàndard SQL: 1999 i posar-les a prova amb un producte comercial que les suporti.

Representing diffusion MRI in 5D for segmentation of white matter tracts with a level set method.

We present a method for segmenting white matter tracts from high angular resolution diffusion MR. images by representing the data in a 5 dimensional space of position and orientation. Whereas crossing fiber tracts cannot be separated in 3D position space, they clearly disentangle in 5D position-orientation space. The segmentation is done using a 5D level set method applied to hyper-surfaces evolving in 5D position-orientation space. In this paper we present a methodology for constructing the position-orientation space. We then show how to implement the standard level set method in such a non-Euclidean high dimensional space. The level set theory is basically defined for N-dimensions but there are several practical implementation details to consider, such as mean curvature. Finally, we will show results from a synthetic model and a few preliminary results on real data of a human brain acquired by high angular resolution diffusion MRI.

Improving Clustering Algorithms for Image Segmentation using Contour and Region Information

In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach

An information theoretic framework for image segmentation

In this paper, an information theoretic framework for image segmentation is presented. This approach is based on the information channel that goes from the image intensity histogram to the regions of the partitioned image. It allows us to define a new family of segmentation methods which maximize the mutual information of the channel. Firstly, a greedy top-down algorithm which partitions an image into homogeneous regions is introduced. Secondly, a histogram quantization algorithm which clusters color bins in a greedy bottom-up way is defined. Finally, the resulting regions in the partitioning algorithm can optionally be merged using the quantized histogram

Segmentation automatique de la matière grise et de la matière blanche par un algorithme génétique : une étude de validation

Résumé Objectif: l'observation des variations de volume de la matière grise (MG), de la matière blanche (MB), et du liquide céphalo-rachidien (LCR) est particulièrement utile dans l'étude de nombreux processus physiopathologiques, la mesure quantitative 'in vivo' de ces volumes présente un intérêt considérable tant en recherche qu'en pratique clinique. Cette étude présente et valide une méthode de segmentation automatique du cerveau avec mesure des volumes de MG et MB sur des images de résonance magnétique. Matériel et Méthode: nous utilisons un algorithme génétique automatique pour segmenter le cerveau en MG, MB et LCR à partir d'images tri-dimensionnelles de résonance magnétique en pondération Ti. Une étude morphométrique a été conduite sur 136 sujets hommes et femmes de 15 à 74 ans. L'algorithme a ensuite été validé par 5 approches différentes: I. Comparaison de mesures de volume sur un cerveau de cadavre par méthode automatique et par mesure de déplacement d'eau selon la méthode d'Archimède. 2. Comparaison de mesures surfaces sur des images bidimensionnelles segmentées soit par un traçage manuel soit par la méthode automatique. 3. Evaluation de la fiabilité de la segmentation par acquisitions et segmentations itératives du même cerveau. 4. Les volumes de MG, MB et LCR ont été utilisés pour une étude du vieillissement normal de la population. 5. Comparaison avec les données existantes de la littérature. Résultats: nous avons pu observer une variation de la mesure de 4.17% supplémentaire entre le volume d'un cerveau de cadavre mesuré par la méthode d'Archimède, en majeure partie due à la persistance de tissus après dissection_ La comparaison des méthodes de comptage manuel de surface avec la méthode automatique n'a pas montré de variation significative. L'épreuve du repositionnement du même sujet à diverses reprises montre une très bonne fiabilité avec une déviation standard de 0.46% pour la MG, 1.02% pour la MB et 3.59% pour le LCR, soit 0.19% pour le volume intracrânien total (VICT). L'étude morphométrique corrobore les résultats des études anatomiques et radiologiques existantes. Conclusion: la segmentation du cerveau par un algorithme génétique permet une mesure 100% automatique, fiable et rapide des volumes cérébraux in vivo chez l'individu normal.

Multimodal evaluation for medical image segmentation

This paper is a joint effort between five institutionsthat introduces several novel similarity measures andcombines them to carry out a multimodal segmentationevaluation. The new similarity measures proposed arebased on the location and the intensity values of themisclassified voxels as well as on the connectivity andthe boundaries of the segmented data. We showexperimentally that the combination of these measuresimprove the quality of the evaluation. The study that weshow here has been carried out using four differentsegmentation methods from four different labs applied toa MRI simulated dataset of the brain. We claim that ournew measures improve the robustness of the evaluation andprovides better understanding about the differencebetween segmentation methods.

Object-oriented Bayesian networks for evaluating DIP-STR profiling results from unbalanced DNA mixtures.

The genetic characterization of unbalanced mixed stains remains an important area where improvement is imperative. In fact, with current methods for DNA analysis (Polymerase Chain Reaction with the SGM Plus™ multiplex kit), it is generally not possible to obtain a conventional autosomal DNA profile of the minor contributor if the ratio between the two contributors in a mixture is smaller than 1:10. This is a consequence of the fact that the major contributor's profile 'masks' that of the minor contributor. Besides known remedies to this problem, such as Y-STR analysis, a new compound genetic marker that consists of a Deletion/Insertion Polymorphism (DIP), linked to a Short Tandem Repeat (STR) polymorphism, has recently been developed and proposed elsewhere in literature [1]. The present paper reports on the derivation of an approach for the probabilistic evaluation of DIP-STR profiling results obtained from unbalanced DNA mixtures. The procedure is based on object-oriented Bayesian networks (OOBNs) and uses the likelihood ratio as an expression of the probative value. OOBNs are retained in this paper because they allow one to provide a clear description of the genotypic configuration observed for the mixed stain as well as for the various potential contributors (e.g., victim and suspect). These models also allow one to depict the assumed relevance relationships and perform the necessary probabilistic computations.

3D Automated Lung Nodule Segmentation in HRCT

A fully-automated 3D image analysis method is proposed to segment lung nodules in HRCT. A specific gray-level mathematical morphology operator, the SMDC-connection cost, acting in the 3D space of the thorax volume is defined in order to discriminate lung nodules from other dense (vascular) structures. Applied to clinical data concerning patients with pulmonary carcinoma, the proposed method detects isolated, juxtavascular and peripheral nodules with sizes ranging from 2 to 20 mm diameter. The segmentation accuracy was objectively evaluated on real and simulated nodules. The method showed a sensitivity and a specificity ranging from 85% to 97% and from 90% to 98%, respectively.

Rank estimation of trajectory matrix in motion segmentation

A novel technique for estimating the rank of the trajectory matrix in the local subspace affinity (LSA) motion segmentation framework is presented. This new rank estimation is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built with LSA. The result is an enhanced model selection technique for trajectory matrix rank estimation by which it is possible to automate LSA, without requiring any a priori knowledge, and to improve the final segmentation