La lithiase rénale: un marqueur du risque cardiovasculaire [Kidney stone as a cardiovascular risk marker].

Si le passage d'un calcul rénal est souvent considéré comme un événement médical mineur, quoique très douloureux, de plus en plus d'études indiquent qu'il doit être pris au sérieux puisqu'il peut indiquer un risque cardiovasculaire augmenté. Nous revoyons ici les études qui associent risque cardiovasculaire et calcul rénal et les liens physiopathologiques qui les unissent. Nous montrons que la lithiase est un événement intervenant tôt dans la vie d'un individu à risque de développer des complications cardiovasculaires. Ainsi, la lithiase ne doit pas être banalisée, mais doit être considérée comme une première alerte devant inciter le médecin traitant à recenser précocement les facteurs de risque cardiovasculaires et à mettre en place une stratégie de prévention. Cette approche pourrait permettre de diminuer l'incidence d'événements cardiovasculaires chez les patients formeurs de lithiases. Most of the time, kidney stones are considered as minor, but painful events. However, several studies have recently shown an association between kidney stone and an increased cardio-vascular risk. We review here these studies and explore the underlying pathophysiological hypotheses. At the end, we propose that lithiasis should be considered as a red flag intervening early during life-time and allowing a check of cardiovascular risk factors and early preventive intervention. Such approach may be successful in reducing the incidence of cardio-vascular events in stone formers.

Fast Geodesic Active Fields for Image Registration Based on Splitting and Augmented Lagrangian Approaches.

In this paper, we present an efficient numerical scheme for the recently introduced geodesic active fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, the data-term and the regularization-term are combined through multiplication in a single, parametrization invariant and geometric cost functional. The multiplicative coupling provides an intrinsic, spatially varying and data-dependent tuning of the regularization strength, and the parametrization invariance allows working with images of nonflat geometry, generally defined on any smoothly parametrizable manifold. The resulting energy-minimizing flow, however, has poor numerical properties. Here, we provide an efficient numerical scheme that uses a splitting approach; data and regularity terms are optimized over two distinct deformation fields that are constrained to be equal via an augmented Lagrangian approach. Our approach is more flexible than standard Gaussian regularization, since one can interpolate freely between isotropic Gaussian and anisotropic TV-like smoothing. In this paper, we compare the geodesic active fields method with the popular Demons method and three more recent state-of-the-art algorithms: NL-optical flow, MRF image registration, and landmark-enhanced large displacement optical flow. Thus, we can show the advantages of the proposed FastGAF method. It compares favorably against Demons, both in terms of registration speed and quality. Over the range of example applications, it also consistently produces results not far from more dedicated state-of-the-art methods, illustrating the flexibility of the proposed framework.