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Evaluation of most normal and patho-pulmonary physiology has relied upon indirect measures of pulmonary function which yield global estimates of underlying structural and functional deficits, which are usually very heterogeneous in nature. Early signs of disease are not recognizable by these techniques, nor are they usually recognizable by the manifestation of physical symptoms. As X-ray CT technology has improved, imaging has held a promise to provide the detailed information here-to-fore missing in standard pulmonary function evaluations. A full solution to the imaging and analysis problem requires true dynamic volumetric approaches to facilitate tracking the lung through space during respiratory maneuvers, and following the radiopacified blood and airflow tracers as they pass through the pulmonary vascular bed or wash in or out of the alveolar air spaces. However, high- resolution, high-speed, stacked single-slice approaches for lung imaging have brought the state-of-the-art to a point where quantitative airway evaluation can play an important role in the study of lung disease and normal lung physiology, if one limits the evaluation to those airway segmented sliced in true cross-sections, or to the evaluation of those airway segments for which a true cross-sectional image can be reformatted from the original stacked sections. This paper presents a software system, called ASAP (for Airway Segmentation and Analysis Program), which provides a rapid, minimally-interactive method for objective identification of airway borders and the reporting of associated geometric measures of diameters and wall thicknesses. We demonstrate that this system yields highly reproducible results both within and between observers, and quantitative measures are accurate to within the resolution of the scanner when phantoms of known geometry are evaluated. Results included here demonstrate that the well-accepted half-max criteria for border definition is a rough approximation, which when applied to structures such as intrathoracic airways yields incorrect results. Our analysis shows that the inner and outer wall detection thresholds must be customized based upon the size of the structure of interest.
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