Image analysis for cystic fibrosis: Automatic lung airway wall and vessel measurement on CT images

Abstract

Cystic Fibrosis (CF) is the most common lethal genetic disorder in the Caucasian population, affecting about 30,000 people in the United States. It results in inflammation, hence thickening of airway (AW) walls. It has been demonstrated that AW inflammation begins early in life producing structural AW damage. Because this damage can be present in patients who are relatively asymptomatic, lung disease can progress insidiously. High-resolution computed tomographic imaging has also shown that the AWs of infants and young children with CF have thicker walls and are more dilated than those of normal children. The purpose of this study was to develop computerized methods which allow rapid, efficient and accurate assessment of computed tomographic AW and vessel (V) dimensions from axial CT lung images. For this purpose, a full-width-half-max based automatic AW and V size measurement method was developed. The only user input required is approximate center marking of AW and V by an expert. The method was evaluated on a patient population of 4 infants and 4 children with different stages of mild CF related lung disease. This new automated method for assessing early AW disease in infants and children with CF represents a potentially useful outcome measure for future intervention trials.