Coronary MR imaging using free-breathing 3D steady-state free precession with radial k-space sampling.

PURPOSE: To investigate the potential of free-breathing 3D steady-state free precession (SSFP) imaging with radial k-space sampling for coronary MR-angiography (MRA), coronary projection MR-angiography and coronary vessel wall imaging. MATERIALS AND METHODS: A navigator-gated free-breathing T2-prepared 3D SSFP sequence (TR = 6.1 ms, TE = 3.0 ms, flip angle = 120 degrees, field-of-view = 360 mm(2)) with radial k-space sampling (384 radials) was implemented for coronary MRA. For projection coronary MRA, this sequence was combined with a 2D selective aortic spin tagging pulse. Coronary vessel wall imaging was performed using a high-resolution inversion-recovery black-blood 3D radial SSFP sequence (384 radials, TR = 5.3 ms, TE = 2.7 ms, flip angle = 55 degrees, reconstructed resolution 0.35 x 0.35 x 1.2 mm(3)) and a local re-inversion pulse. Six healthy volunteers (two for each sequence) were investigated. Motion artifact level was assessed by two radiologists. Results: In coronary MRA, the coronary lumen was displayed with a high signal and high contrast to the surrounding lumen. Projection coronary MRA demonstrated selective visualization of the coronary lumen while surrounding tissue was almost completely suppressed. In coronary vessel wall imaging, the vessel wall was displayed with a high signal when compared to the blood pool and the surrounding tissue. No visible motion artifacts were seen. Conclusion: 3D radial SSFP imaging enables coronary MRA, coronary projection MRA and coronary vessel wall imaging with a low motion artifact level.

Enhanced Model Selection for motion segmentation

In this paper a novel rank estimation technique for trajectories motion segmentation within the Local Subspace Affinity (LSA) framework is presented. This technique, called Enhanced Model Selection (EMS), is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built by LSA. The results on synthetic and real data show that without any a priori knowledge, EMS automatically provides an accurate and robust rank estimation, improving the accuracy of the final motion segmentation

Automatic landmarks detection in breast reconstruction aesthetic assessment.

This paper addresses a fully automatic landmarks detection method for breast reconstruction aesthetic assessment. The set of landmarks detected are the supraesternal notch (SSN), armpits, nipples, and inframammary fold (IMF). These landmarks are commonly used in order to perform anthropometric measurements for aesthetic assessment. The methodological approach is based on both illumination and morphological analysis. The proposed method has been tested with 21 images. A good overall performance is observed, although several improvements must be achieved in order to refine the detection of nipples and SSNs.

Modulatory effects of 5Hz rTMS over the primary somatosensory cortex in focal dystonia--an fMRI-TMS study.

Dystonia is associated with impaired somatosensory ability. The electrophysiological method of repetitive transcranial magnetic stimulation (rTMS) can be used for noninvasive stimulation of the human cortex and can alter cortical excitability and associated behavior. Among others, rTMS can alter/improve somatosensory discrimation abilities, as shown in healthy controls. We applied 5Hz-rTMS over the left primary somatosensory cortex (S1) in 5 patients with right-sided writer's dystonia and 5 controls. We studied rTMS effects on tactile discrimination accuracy and concomitant rTMS-induced changes in hemodynamic activity measured by functional magnetic resonance imaging (fMRI). Before rTMS, patients performed worse on the discrimination task than controls even though fMRI showed greater task-related activation bilaterally in the basal ganglia (BG). In controls, rTMS led to improved discrimination; fMRI revealed this was associated with increased activity of the stimulated S1, bilateral premotor cortex and BG. In dystonia patients, rTMS had no effect on discrimination; fMRI showed similar cortical effects to controls except for no effects in BG. Improved discrimination after rTMS in controls is linked to enhanced activation of S1 and BG. Failure of rTMS to increase BG activation in dystonia may be associated with the lack of effect on sensory discrimination in this group and may reflect impaired processing in BG-S1 connections. Alternatively, the increased BG activation seen in the baseline state without rTMS may reflect a compensatory strategy that saturates a BG contribution to this task.

Harmonic active contours.

We propose a segmentation method based on the geometric representation of images as 2-D manifolds embedded in a higher dimensional space. The segmentation is formulated as a minimization problem, where the contours are described by a level set function and the objective functional corresponds to the surface of the image manifold. In this geometric framework, both data-fidelity and regularity terms of the segmentation are represented by a single functional that intrinsically aligns the gradients of the level set function with the gradients of the image and results in a segmentation criterion that exploits the directional information of image gradients to overcome image inhomogeneities and fragmented contours. The proposed formulation combines this robust alignment of gradients with attractive properties of previous methods developed in the same geometric framework: 1) the natural coupling of image channels proposed for anisotropic diffusion and 2) the ability of subjective surfaces to detect weak edges and close fragmented boundaries. The potential of such a geometric approach lies in the general definition of Riemannian manifolds, which naturally generalizes existing segmentation methods (the geodesic active contours, the active contours without edges, and the robust edge integrator) to higher dimensional spaces, non-flat images, and feature spaces. Our experiments show that the proposed technique improves the segmentation of multi-channel images, images subject to inhomogeneities, and images characterized by geometric structures like ridges or valleys.

Sexual dimorphism of the human mandible: demonstration by elliptical Fourier analysis.

A new quantitative approach of the mandibular sexual dimorphism, based on computer-aided image analysis and elliptical Fourier analysis of the mandibular outline in lateral view is presented. This method was applied to a series of 117 dentulous mandibles from 69 male and 48 female individuals native of Rhenish countries. Statistical discriminant analysis of the elliptical Fourier harmonics allowed the demonstration of a significant sexual dimorphism in 97.1% of males and 91.7% of females, i.e. in a higher proportion than in previous studies using classical metrical approaches. This original method opens interesting perspectives for increasing the accuracy of sex identification in current anthropological practice and in forensic procedures.

Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers.

MRI tractography is the mapping of neural fiber pathways based on diffusion MRI of tissue diffusion anisotropy. Tractography based on diffusion tensor imaging (DTI) cannot directly image multiple fiber orientations within a single voxel. To address this limitation, diffusion spectrum MRI (DSI) and related methods were developed to image complex distributions of intravoxel fiber orientation. Here we demonstrate that tractography based on DSI has the capacity to image crossing fibers in neural tissue. DSI was performed in formalin-fixed brains of adult macaque and in the brains of healthy human subjects. Fiber tract solutions were constructed by a streamline procedure, following directions of maximum diffusion at every point, and analyzed in an interactive visualization environment (TrackVis). We report that DSI tractography accurately shows the known anatomic fiber crossings in optic chiasm, centrum semiovale, and brainstem; fiber intersections in gray matter, including cerebellar folia and the caudate nucleus; and radial fiber architecture in cerebral cortex. In contrast, none of these examples of fiber crossing and complex structure was identified by DTI analysis of the same data sets. These findings indicate that DSI tractography is able to image crossing fibers in neural tissue, an essential step toward non-invasive imaging of connectional neuroanatomy.

Phase-sensitive black-blood coronary vessel wall imaging.

Black-blood MR coronary vessel wall imaging may become a powerful tool for the quantitative and noninvasive assessment of atherosclerosis and positive arterial remodeling. Although dual-inversion recovery is currently the gold standard, optimal lumen-to-vessel wall contrast is sometimes difficult to obtain, and the time window available for imaging is limited due to competing requirements between blood signal nulling time and period of minimal myocardial motion. Further, atherosclerosis is a spatially heterogeneous disease, and imaging at multiple anatomic levels of the coronary circulation is mandatory. However, this requirement of enhanced volumetric coverage comes at the expense of scanning time. Phase-sensitive inversion recovery has shown to be very valuable for enhancing tissue-tissue contrast and for making inversion recovery imaging less sensitive to tissue signal nulling time. This work enables multislice black-blood coronary vessel wall imaging in a single breath hold by extending phase-sensitive inversion recovery to phase-sensitive dual-inversion recovery, by combining it with spiral imaging and yet relaxing constraints related to blood signal nulling time and period of minimal myocardial motion.

Coronary magnetic resonance angiography.

Coronary magnetic resonance angiography (MRA) is a powerful noninvasive technique with high soft-tissue contrast for the visualization of the coronary anatomy without X-ray exposure. Due to the small dimensions and tortuous nature of the coronary arteries, a high spatial resolution and sufficient volumetric coverage have to be obtained. However, this necessitates scanning times that are typically much longer than one cardiac cycle. By collecting image data during multiple RR intervals, one can successfully acquire coronary MR angiograms. However, constant cardiac contraction and relaxation, as well as respiratory motion, adversely affect image quality. Therefore, sophisticated motion-compensation strategies are needed. Furthermore, a high contrast between the coronary arteries and the surrounding tissue is mandatory. In the present article, challenges and solutions of coronary imaging are discussed, and results obtained in both healthy and diseased states are reviewed. This includes preliminary data obtained with state-of-the-art techniques such as steady-state free precession (SSFP), whole-heart imaging, intravascular contrast agents, coronary vessel wall imaging, and high-field imaging. Simultaneously, the utility of electron beam computed tomography (EBCT) and multidetector computed tomography (MDCT) for the visualization of the coronary arteries is discussed.

Object and Scene Classification: what does a Supervised Approach Provide us?

Given a set of images of scenes containing different object categories (e.g. grass, roads) our objective is to discover these objects in each image, and to use this object occurrences to perform a scene classification (e.g. beach scene, mountain scene). We achieve this by using a supervised learning algorithm able to learn with few images to facilitate the user task. We use a probabilistic model to recognise the objects and further we classify the scene based on their object occurrences. Experimental results are shown and evaluated to prove the validity of our proposal. Object recognition performance is compared to the approaches of He et al. (2004) and Marti et al. (2001) using their own datasets. Furthermore an unsupervised method is implemented in order to evaluate the advantages and disadvantages of our supervised classification approach versus an unsupervised one

Entorn de suport al diagnòstic d'estenosi de caròtida

L’objectiu d’aquest projecte és ampliar la plataforma Starviewer integrant els mòdulsnecessaris per donar suport al diagnòstic de l’estenosi de caròtida permetentinterpretar de forma més fàcil les imatges Angiografia per Ressonància Magnètica(ARM). La plataforma Starviewer és un entorn informàtic que integra funcionalitatsbàsiques i avançades pel processament i la visualització d’imatges mèdiques. Estàdesenvolupat pel Grup d’Informàtica Gràfica de la Universitat de Girona i l’Institut deDiagnòstic per la Imatge (IDI) de l’hospital Dr. Josep Trueta. Una de les limitacions de la plataforma és el no suportar el tractament de lesions delsistema vascular. Per això ens proposem a corregir-ho i ampliar les seves extensionsper a poder diagnosticar l’estenosi de caròtida

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.

Computational anatomy for studying use-dependant brain plasticity.

In this article we provide a comprehensive literature review on the in vivo assessment of use-dependant brain structure changes in humans using magnetic resonance imaging (MRI) and computational anatomy. We highlight the recent findings in this field that allow the uncovering of the basic principles behind brain plasticity in light of the existing theoretical models at various scales of observation. Given the current lack of in-depth understanding of the neurobiological basis of brain structure changes we emphasize the necessity of a paradigm shift in the investigation and interpretation of use-dependent brain plasticity. Novel quantitative MRI acquisition techniques provide access to brain tissue microstructural properties (e.g., myelin, iron, and water content) in-vivo, thereby allowing unprecedented specific insights into the mechanisms underlying brain plasticity. These quantitative MRI techniques require novel methods for image processing and analysis of longitudinal data allowing for straightforward interpretation and causality inferences.

Atlas-based segmentation of pathological brain MR images

We propose a method for brain atlas deformation inpresence of large space-occupying tumors, based on an apriori model of lesion growth that assumes radialexpansion of the lesion from its starting point. First,an affine registration brings the atlas and the patientinto global correspondence. Then, the seeding of asynthetic tumor into the brain atlas provides a templatefor the lesion. Finally, the seeded atlas is deformed,combining a method derived from optical flow principlesand a model of lesion growth (MLG). Results show that themethod can be applied to the automatic segmentation ofstructures and substructures in brains with grossdeformation, with important medical applications inneurosurgery, radiosurgery and radiotherapy.