Fuzzy modeling of electrical impedance tomography images of the lungs

• Autor(a) principal: Tanaka, Harki
• Data de Publicação: 2008
• Outros Autores: Ortega, Neli Regina Siqueira, Galizia, Mauricio Stanzione, Borges, João Batista, Amato, Marcelo Britto Passos
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Clinics
• Texto Completo: https://www.revistas.usp.br/clinics/article/view/17807
• topic: Ventilation; Perfusion; Hypertonic saline; Segmentation; Fuzzy logic

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.