Fuzzy Modeling of Electrical Impedance Tomography Images of the Lungs

Abstract

OBJECTIVES:Aiming to improve the anatomical resolution of electrical impedance tomography images, we developed a fuzzy model based on electrical impedance tomography's high temporal resolution and on the functional pulmonary signals of perfusion and ventilation. INTRODUCTION: Electrical impedance tomography images carry information about both ventilation and perfusion. However, these images are difficult to interpret because of insufficient anatomical resolution, such that it becomes almost impossible to distinguish the heart from the lungs. METHODS: Electrical impedance tomography data from an experimental animal model were collected during normal ventilation and apnea while an injection of hypertonic saline was administered. The fuzzy model was elaborated in three parts: a modeling of the heart, the pulmonary ventilation map and the pulmonary perfusion map. Image segmentation was performed using a threshold method, and a ventilation/perfusion map was generated. RESULTS: Electrical impedance tomography images treated by the fuzzy model were compared with the hypertonic saline injection method and computed tomography scan images, presenting good results. The average accuracy index was 0.80 when comparing the fuzzy modeled lung maps and the computed tomography scan lung mask. The average ROC curve area comparing a saline injection image and a fuzzy modeled pulmonary perfusion image was 0.77. DISCUSSION: The innovative aspects of our work are the use of temporal information for the delineation of the heart structure and the use of two pulmonary functions for lung structure delineation. However, robustness of the method should be tested for the imaging of abnormal lung conditions. CONCLUSIONS: These results showed the adequacy of the fuzzy approach in treating the anatomical resolution uncertainties in electrical impedance tomography images.

Real-Time Imaging and Detection of Intracranial Haemorrhage by Electrical Impedance Tomography in a Piglet Model

“…9 The combination of data pre-processing methods and new algorithms could improve the capabilities of EIT detection. In addition, anatomical images such as CT and MRI can be used to provide accurate location information to facilitate the use of this technique: 22 the anatomical image and EIT image could be merged as a fusion image to provide the precise location of the bleeding.…”

As Medidas de Risco Clássicas e as de Risco Fuzzi

“…A teoria de conjuntos fuzzy pode oferecer o suporte metodológico adequado à incorporação das incertezas no cálculo do risco. Diversos trabalhos na área de epidemiologia que utilizaram a teoria de conjuntos fuzzy de forma satisfatória: desenvolvimento de um modelo fuzzy para estimar a possibilidade de morte neonatal com boa acurácia que pode ser utilizado em hospitais gerais (NASCIMENTO, RIZOL, ABIUZI, 2009); avaliação de um grupo de fatores de risco associados à candidíase oral por meio da lógica fuzzy (CAMPISI, 2008); criação de um modelo fuzzy para melhorar a capacidade diagnóstica de imagens de tomografia de impedância elétrica, para identificar e separar o coração das regiões do pulmão (TANAKA et al, 2008). Há diversas vantagens no uso da teoria fuzzy na classificação dos sujeitos considerados em risco, como por exemplo, na classificação de crianças de baixo peso quando se pretende avaliar risco de morte neonatal.…”

Endoscopic Lung Volume Reduction for Emphysema

“…It has been proven useful for PEEP titration, to optimize ventilation strategies by detecting the imbalances in regional lung ventilation, as well as to detect pneumothorax and small pleural effusions (Victorino, Borges et al 2004;Costa, Lima et al 2009). More recently there have been studies showing that EIT can assess lung perfusion through intravenous injection of hypertonic saline, which is a contrast agent for EIT images because of its extremely low impeditivity (Tanaka, Ortega et al 2008). The characteristics of EIT makes it a potentially useful tool for the assessment of BLVR.…”