ABSTRACT
At the beginning of the COVID-19 pandemic in Chile, in March 2020, a projection indicated that a significant group of patients with pneumonia would require admission to an Intensive Care Unit and connection to a mechanical ventilator. Therefore, a paucity of these devices and other supplies was predicted. The initiative "Un respiro para Chile" brought together many people and institutions, public and private. In the course of three months, it allowed the design and building of several ventilatory assistance devices, which could be used in critically ill patients.
Subject(s)
Humans , Pandemics , COVID-19 , Respiration, Artificial , Ventilators, Mechanical , Chile/epidemiology , Intensive Care UnitsABSTRACT
Objective: Hemodynamic parameters are critical to perform a proper diagnosis. However, due to the large number of variables that can be obtained, overall analysis may represent a complex task. To facilitate this, we propose to create a model for classifying different hemodynamic variables between those belonging to a healthy individual and to a pathological patient. For this purpose, we employed data mining techniques to identify relationships among various aortic hemodynamic parameters obtained through multi-dimensional (4D flow) MR imaging. Method: A 4D flow sequence of whole heart and great vessels was acquired using MRI in 19 healthy volunteers and 2 patients (one with aortic coarctation and one with repaired coarctation of the aorta). Retrospectively, data were reformatted along the aorta; three MRI acquisitions were performed for volunteers and 30 sequences for each patient. In each slice the aorta was segmented and various parameters were quantified: area, maximum velocity, minimum velocity, flow and volumen, with following values being calculated for last four parameters: maximum, average, standard deviation, kurtosis, skewness, proportion of time to reach the maximum value, among others. A total of 26 variables for each acquisition were obtained. In order to classify data, the CART Technique (Classification and Regression Trees) was applied. To validate the model, two extra projections were generated per each volunteer and 20 slice per each patient. Results: By using only 7 variables, the CART Technique allows discrimination between images performed either on volunteers or patients with an error rate of 14.1 percent, a sensitivity of 82.5 percent, and a specificity of 89.4 percent. Conclusions: 4D flow MR imaging provides a wealth of hemodynamic data that can be difficult to analyze. In this paper we demonstrate that by using data mining techniques it is possible to classify images from relevant hemodynamic parameters and their relationships in order...
Objetivo: Los parámetros hemodinámicos son de gran utilidad para realizar un adecuado diagnóstico. Sin embargo, debido a la gran cantidad de variables que pueden obtenerse, el análisis global de todas ellas puede ser complejo. Para facilitar esta tarea, nosotros proponemos crear un modelo que permita clasificar distintas variables hemodinámicas entre las pertenecientes a un individuo sano o a uno patológico. Para ello, usaremos técnicas de minería de datos que permitan identificar y encontrar relaciones entre distintos parámetros hemodinámicos de la aorta obtenidos a través de flujo multidimensional (4D flow) por resonancia magnética. Método: Una secuencia 4D flow de todo el corazón y los grandes vasos fue adquirida utilizando resonancia magnética en 19 voluntarios sanos y 2 pacientes (uno con una coartación aórtica y otro con una coartación aórtica reparada). Retrospectivamente, los datos fueron reformateados a lo largo de la aorta, originándose 3 cortes en los voluntarios y 30 cortes en cada paciente. En cada corte la aorta fue segmentada y distintos parámetros fueron cuantificados: área, velocidad máxima, velocidad mínima, flujo y volumen, calculándose en los cuatro últimos su valor máximo, promedio, desviación estándar, curtosis, sesgo, proporción de tiempo en alcanzar el valor máximo, entre otros. Teniendo un total de 26 variables por cada corte. Se aplicó la técnica de árboles de decisión tipo CART (por sus siglas en inglés) para clasificar los datos. Para validar el modelo, 2 cortes extras fueron generados por cada voluntario y 20 cortes por cada paciente. Resultados: La técnica CART, mediante la utilización de sólo 7 variables, puede clasificar las imágenes de los voluntarios y pacientes con una tasa de error del 14,1 por ciento, una sensibilidad de 82,5 por ciento y una especificidad de 89.4 por ciento. Conclusiones: 4D flow provee una gran cantidad de datos hemodinámicos que son difíciles de analizar. En este trabajo demostramos que al utilizar...
Subject(s)
Humans , Aorta/physiopathology , Cardiac Imaging Techniques , Imaging, Three-Dimensional/methods , Magnetic Resonance Imaging/methods , Blood Flow Velocity/physiology , Image Enhancement/methods , Data Mining , Decision Trees , Cardiovascular Diseases/diagnosis , Hemodynamics , Regional Blood Flow , Regression Analysis , Reproducibility of Results , Sensitivity and SpecificityABSTRACT
This study aimed at implementing a stimulation protocol using functional Magnetic Resonance Imaging (fMRI), in a Hospital in Valparaiso (V Region), Chile, to detect cortical areas activated in oral language comprehension. Seven healthy volunteers participated in this study. Average t-score and signal variation were 6.3 +/- 0.3 and 0.5 +/- 0.1 percent, respectively. In spite of these low values, activations were obtained in Wernicke area, middle temporal gyrus, and Heschl s gyrus. The extension of activated areas was small, 5.06 +/- 2.99 cm3, probably due to amplifer low signal-to-noise ratio (SNR), in addition to the cognitive complexity of the task, and to the ambient acoustic noise. Successful implementation of fMRI protocols of language comprehension is possible in a clinical context in Chile without any additional resources.
Nuestro objetivo fue implementar un protocolo de estimulación en resonancia magnética funcional en un Hospital de Valparaíso, V Región de Chile, para detectar las áreas corticales activadas en la comprensión del lenguaje oral. Siete voluntarios sanos participaron de este estudio. El t-score y variación de señal alcanzado fue de 6.3 0.3 y 0.5 +/- 0.1 por ciento respectivamente. A pesar de estos bajos valores, las activaciones se registraron en el área Wernicke, circunvolución temporal media y circunvolución d e Heschl. La extensión de las activaciones fue pequeña, 5.06 +/- 2.99 cm³, probablemente debido a la baja relación señal ruido del resonador (SNR), además de la complejidad cognitiva de la tarea y el ruido acústico ambiente. Nuestros resultados indican que la implementación de la RMf en comprensión del lenguaje es posible de realizar en Chile sin recursos adicionales.
Subject(s)
Humans , Male , Female , Cerebrum/physiology , Comprehension/physiology , Physical Stimulation/methods , Speech/physiology , Magnetic Resonance Imaging/methodsABSTRACT
Diffusion Magnetic Resonance Imaging provides images of unquestionable diagnostic value. It is commonly used in the assessment of stroke and in white matter fiber tracking, among other applications. The diffusion coefficient has been shown to depend on cell concentration, membrane permeability, and cell orientation in the case of white matter or muscle fiber tracking; yet a clear relation between diffusion measurements and known physiological parameters is not established. The aim of this paper is to review hypotheses and actual knowledge on diffusion signal origin to provide assistance in the interpretation of diffusion MR images. Focus will be set on brain images, as most common applications of diffusion MRI are found in neuroradiology. Diffusion signal does not come from two intra- or extracellular compartments, as was first assumed. Restriction of water displacement due to membranes, hindrance in the extracellular space, and tissue heterogeneity are important factors. Unanswered questions remain on how to deal with tissue heterogeneity, and how to retrieve parameters less troublesome to work with from biological and clinical points of view. Diffusion quantification should be done with care, as many variables can lead to variation in measurements.