El-Bey Bourennane

Influence of age and sex on aortic distensibility assessed by MRI in healthy subjects

Magnetic resonance imaging (MRI) is particularly well adapted to the evaluation of aortic distensibility. The calculation of this parameter, based on the change in vessel cross-sectional area per unit change in blood pressure, requires precise delineation of the aortic wall on a series of cine-MR images. Firstly, the study consisted in validating a new automatic method to assess aortic elasticity. Secondly, aortic distensibility was studied for the ascending and descending thoracic aortas in 26 healthy subjects. Two homogeneous groups were available to evaluate the influence of sex and age (with an age limit value of 35 years). The automatic postprocessing method proved to be robust and reliable enough to automatically determine aortic distensibility, even on artefacted images. In the 26 healthy volunteers, a marked decrease in distensibility appears with age, although this decrease is only significant for the ascending aorta (8.97+/-2.69 10(-3) mmHg(-1) vs. 5.97+/-2.02 10(-3) mmHg(-1)). Women have a higher aortic distensibility than men but only significantly at the level of the descending aorta (7.20+/-1.61 10(-3) mmHg(-1) vs. 5.05+/-2.40 10(-3) mmHg(-1)). Through our automatic contouring method, the aortic distensibility from routine cine-MRI has been studied on a healthy subject population providing reference values of aortic stiffness. The aortic distensibility calculation shows that age and sex are causes of aortic stiffness variations in healthy subjects.

Automatic detection of vessel wall contours from cine-MRI for aortic compliance determination

The aortic compliance is defined as the relative change of aortic cross-sectional area divided by the change in arterial pressure. Magnetic resonance imaging (MRI) can be used to evaluate aortic compliance. A knowledge of the aortic contour is essential to determine the aortic area. To prevent important intra-and inter-observer variability, the aortic contours are detected automatically. This work consists in extracting automatically the aortic contour at different phases of the cardiac cycle. An automatic edge detection making use of a Haralick filter and graph searching allows the estimation of aortic cross-sectional area on MR images, from which the aortic compliance is derived

Classification boundary approximation by using combination of training steps for real-time image segmentation

We propose a method of real-time implementation of an approximation of the support vector machine decision rule. The method uses an improvement of a supervised classification method based on hyperrectangles, which is useful for real-time image segmentation. We increase the classification and speed performances using a combination of classification methods: a support vector machine is used during a pre-processing step. We recall the principles of the classification methods and we evaluate the hardware implementation cost of each method. We present our learning step combination algorithm and results obtained using Gaussian distributions and an example of image segmentation coming from a part of an industrial inspection problem The results are evaluated regarding hardware cost as well as classification performances.