Regional lung ventilation can be measured via Xenon-enhanced computed tomography (Xe-CT) by determining washin (WI) and washout (WO) rates of stable Xe. It has been assumed that WI = WO, ignoring Xe solubility in blood and tissue and then other geometric isssues. We test this by measuring WO-WI in lung by Xe-CT. Also, we investigate the effect of tidal volume (TV) and end inspiratory (EI) vs end expiratory (EE) scan gating on WO and WI measurements. 3 anesthetized, supine sheep were scanned using multidetector-row computed tomography (MDCT). Imaging was gated to both EE and EI during a WI (33 breaths) and WO (20 breaths) maneuver using 55% Xe for WI and room air for WO. Time constants (TCs) of Xe WI and WO were obtained by exponential fitting. WO and WI TCs were compared: 1) apex and base 2) dependent, middle, and nondependent 3) EE and EI 4) three TVs. The vertical gradient of WO-WI showed WO > WI in dependent vs non-dependent regions. WO-WI in both dependent and nondependent region at the lung base and apex was larger when measured at EE compared to EI. As TV increases, the global WO-WI difference decreased. TV showed greater influence on WO than WI. Xe WO was longer than WI possibly reflecting Xe solubility in blood and tissue. Higher TVs and gating to EE provided greater effects on WO than WI TCs which may relate to the number of partial volumed conducting airways contributing to the regional voxel-based measures. We conclude that WO mode is more susceptible to errors caused by either xenon solubility or tidal volume than WI mode and EE scanning may more accurately reflect alveolar ventilation.
This paper describes an algorithm for automated segmentation of pulmonary vessels from thoracic 3D CT images. The lung region is roughly extracted based on thresholding and labeling in order to reduce computational cost in the following filtering step. Vessels are enhanced by application of a line-filter, which is based on a combination of eigen values of a Hessian matrix to provide higher response to vessels compared with the other structures. Initial segmentation is performed by thresholding of the filter output. Since extracted vessels may contain tiny holes and local discontinuities between segments, especially around branchpoints, tracking algorithm is used to fill these gaps. Though the results may still contain not only vessels but also parts of airway walls and noise, such structures can be eliminated by considering the number of branchpoints associated with each structure since vascular trees are characterized as objects with many branchpoints. Therefore, a thinning algorithm is applied to determine the number of branchpoints and the final segmentation is obtained by thresholding with regard to the number of branchpoints. We applied the algorithm to five healthy human scans and obtained visually promising results. In order to evaluate our segmentation results quantitatively, approximately 2,000 manually identified points inside the vascular tree were selected in each case to check how many were correctly included in the segmentation result. On average, 98% of the manually identified vessel points were properly marked as vessels. This result demonstrates the promising performance of our algorithm and its utility for further analyses.