Modelamiento de la arteria carótida

This study presents a modelling for the carotid artery that allows to analyze the theoretical behavior of pressure, flow and arterial volume in it, from the variation of both physical and physiological characteristics, such as: the thickness and length of the vessel blood, the viscosity and density of blood; determinants in the study of the normal functionality of the artery. The model of the arterial vessel –an adaptation of the Windkessel model of three elements, reported by Westerhof and Stergiopulos–, consists of an electrical circuit composed of passive RLC elements. The arterial segment was analyzed by mathematical and computational tools, relating Poiseuille's laws and electric laws. The pressure, flow and volume curves were obtained when changes occurred in the measurable characteristics of the carotid artery, in order to facilitate the medical interpretation of possible pathologies related to these changes.Este estudio presenta un modelado para la arteria carótida que permite analizar el comportamiento teórico de la presión, el flujo y el volumen arterial en ella, a partir de la variación de características tanto físicas como fisiológicas tales como: el espesor y longitud del vaso sanguíneo, la viscosidad y densidad de la sangre; determinantes en el estudio de la funcionalidad normal de tal arteria. El modelo del vaso arterial, –una adaptación del modelo de Windkessel de tres elementos reportado por Westerhof y Stergiopulos–, consiste en un circuito eléctrico compuesto de elementos pasivos RLC. El segmento arterial se analizó mediante herramientas matemáticas y computacionales, relacionando las leyes de Poiseuille y las leyes eléctricas. Se obtuvieron las curvas de presión, flujo y volumen, cuando ocurrían cambios en las características medibles de la arteria carótida, con el fin de facilitar la interpretación médica de posibles patologías relacionadas con estos cambios

Early-stage atherosclerosis detection using deep learning over carotid ultrasound images

This paper proposes a computer-aided diagnosis tool for the early detection of atherosclerosis. This pathology is responsible for major cardiovascular diseases, which are the main cause of death worldwide. Among preventive measures, the intima-media thickness (IMT) of the common carotid artery stands out as early indicator of atherosclerosis and cardiovascular risk. In particular, IMT is evaluated by means of ultrasound scans. Usually, during the radiological examination, the specialist detects the optimal measurement area, identifies the layers of the arterial wall and manually marks pairs of points on the image to estimate the thickness of the artery. Therefore, this manual procedure entails subjectivity and variability in the IMT evaluation. Instead, this article suggests a fully automatic segmentation technique for ultrasound images of the common carotid artery. The proposed methodology is based on machine learning and artificial neural networks for the recognition of IMT intensity patterns in the images. For this purpose, a deep learning strategy has been developed to obtain abstract and efficient data representations by means of auto-encoders with multiple hidden layers. In particular, the considered deep architecture has been designed under the concept of extreme learning machine (ELM). The correct identification of the arterial layers is achieved in a totally user-independent and repeatable manner, which not only improves the IMT measurement in daily clinical practice but also facilitates the clinical research. A database consisting of 67 ultrasound images has been used in the validation of the suggested system, in which the resulting automatic contours for each image have been compared with the average of four manual segmentations performed by two different observers (ground-truth). Specifically, the IMT measured by the proposed algorithm is 0.625±0.167mm (mean±standard deviation), whereas the corresponding ground-truth value is 0.619±0.176mm. Thus, our method shows a difference between automatic and manual measures of only 5.79±34.42μm. Furthermore, different quantitative evaluations reported in this paper indicate that this procedure outperforms other methods presented in the literature