Three-dimensional reconstruction of trachea using computed tomography imaging as therapy for tracheal stenosis in infants. Issue 132 (August 2016)

• Record Type: Journal Article
• Authors: Kuo, Chung-Feng Jeffrey; Leu, Yi-Shing; Kuo, Richard; Su, Chin-Hui; Yuan, Tzu-Chieh; Ke, Bo-Han; Wu, Nain-Ying
• Is Part Of: Computer methods and programs in biomedicine. Issue 132(2016)
• Page Start: 177
• Page End: 187
• Publication Date: 2016-08
• Subjects: Infant tracheal stenosis -- Tracheal three-dimensional reconstruction -- Adjuvant therapy for tracheal stenosis; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.04.027
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: CT image 3D reconstruction for infant tracheal stenosis and adjuvant therapy. The improved median filter retains texture effectively to capture medical images. The trachea position can be tracked and the contour can be extracted in all samples. The minimum cross-section area enables to know the position and size of the endotracheal tube. Proposed new therapeutic indexes of infant tracheal stenosis validated by actual samples. Abstract: Background and objective: In this study, we aim to develop a system that uses computed tomography (CT) imaging for three-dimensional (3D) reconstruction of the trachea as therapy for tracheal stenosis in infants, and further calculate the cross-sectional area and volume, assisting doctors in clinical diagnosis. Methods: We first used image processing, calculating the cross-sectional area and volume. We used the improved median filter for image processing and designed the system for capturing the cross-sectional area of endotracheal tube. We then established 3D reconstruction images with isosurface extraction technology and calculated the cross-sectional area and volume. Medical indicator data analysis was performed. Results: The median filter developed in this study performed better in filtering speckle noise compared to traditional filtering methods. Furthermore, the median filter can keep fine texture feature, so that the subsequent contour selection and 3D reconstructed volume are more accurate. We also proposed new medical grading indexes according to tracheal obstruction volume ratio to assist doctors with the diagnosis and provide recommendations on treatment. Seventeen samples were examined in this study. Four sections of each sample are reviewed. Sixty-eight sections were used for validation, and the overall accuracy is very reliable. Conclusions: Using image processing we obtained tracheal CT images before 3D reconstruction and calculated the cross-sectional area and volume of the trachea. New medical indicators are proposed according to the location and severity of stenosis to assist doctors with diagnosis.