Biomedical subjects

Jean-Louis Coatrieux

Publications and source records attributed to Jean-Louis Coatrieux.

4 recordsLinked to original sources

PubMed2003

Fast algorithm for 3-D vascular tree modeling.

In this short paper, accelerated three-dimensional computer simulations of vascular trees development, preserving physiological and haemodynamic features, are reported. The new computation schemes deal: (i). with the geometrical optimization of each newly created bifurcation; and (ii). with the recalculation of blood pressures and radii of vessels in the whole tree. A significant decrease of the computation time is obtained by replacing the global optimization by the fast updating algorithm allowing more complex structure to be simulated. A comparison between the new algorithms and the previous one is illustrated through the hepatic arterial tree.

Algorithms↗

PubMed2003

Physiologically based modeling of 3-D vascular networks and CT scan angiography.

In this paper, a model-based approach to medical image analysis is presented. It is aimed at understanding the influence of the physiological (related to tissue) and physical (related to image modality) processes underlying the image content. This methodology is exemplified by modeling first, the liver and its vascular network, and second, the standard computed tomography (CT) scan acquisition. After a brief survey on vascular modeling literature, a new method, aimed at the generation of growing three-dimensional vascular structures perfusing the tissue, is described. A solution is proposed in order to avoid intersections among vessels belonging to arterial and/or venous trees, which are physiologically connected. Then it is shown how the propagation of contrast material leads to simulate time-dependent sequences of enhanced liver CT slices.

Algorithms↗

PubMed2002

Signal processing and physiological modeling--part 1: Surface analysis.

Signal processing offers a wide spectrum of theories, methods, and algorithms for addressing a variety of problems ranging from noise reduction, restoration, detection (of events or changes), spatiotemporal dynamics estimation, source localization, and pattern recognition. However, the classical assumptions (stationarity, linearity, etc.) usually do not apply in real situations. Recent advances, such as time-scale and time-frequency transforms, data fusion, long-range dependence, and higher order moments, do not always provide sufficiently robust solutions. In this article, the basic properties and generic features of biomedical signals are examined using a wide range of examples. Algorithmic results are presented to show not only the potential performance but also the limitations of the processing resources at our disposal. The last section describes and discusses signal matching, scenario recognition, and data fusion.

Algorithms↗

PubMed2002

Signal processing and physiological modeling--part II: Depth model-driven analysis.

In this second of two articles on signal processing, we explore the coupling between modeling and signal processing and the critical importance of well-posed clinical questions or hypotheses, as well as a deep knowledge of the underlying mechanisms of such coupling. Our approach consists of building models either as open-loop simulators allowing us to analyze the influence of one or several internal variables on the observations, or as dynamic systems that can enhance our understanding of these underlying mechanisms to be identified in order to lead to an explanative interpretation. These explorations are illustrated by epileptic network and cardiac models, both derived at the macroscopic level.

Algorithms↗