SU‐FF‐J‐171: Quantification of Ventilation Imaging From Clinical 4DCT Datasets for Selective Avoidance IMRT in Non‐Small Cell Lung Cancer

Abstract

Purpose: To determine a clinically appropriate ventilation level for selective avoidance IMRT using clinical 4D CT data sets and a small‐deformation inverse‐consistent linear‐elastic (SICLE) registration algorithm Materials/Methods: Using SICLE, the maximum inhalation and expiration phases of 4D CT datasets from 6 NSCLC patients were registered. After registration, smoothing was applied to the input images to account for registration error. A correction was applied to the voxels in the inhalation image to account for lung tissue mass discrepancy due to lung inflation. The ventilation was calculated voxel by voxel as a local volume change, DV/Vex = 1000 (HUin − HUex)/(HUex(1000 + HUin)), where HUex and HUin are the Hounsfield units associated with the expiration and inhalation images, respectively. After this, an additional smoothing filter and a cumulative distribution function were applied to aid segmentation and normalize the data between 0% and 100%. Ventilation volumes were quantified as a function of normalized ventilation level, which can be used as a threshold for delineation of ventilation subregions. Results: Using the inhalation image registered to the expiration image, consistent ventilation images were calculated. The ventilation levels of 90%, 80%, 70%, 60%, and 50% were studied as it was determined that ventilation levels below 50% would not be beneficial as this region would include almost the entirety of the lungs. An exponential relationship between ventilation level and ventilationcompetent volume at a given percentage ventilation was observed. Quantified ventilation data and charts demonstrating these relationships were generated. Conclusions: In selective avoidance IMRT for NSCLC, it is critical to determine the optimal ventilation level to segment within a normal lung region, which would then be used as an avoidance structure in functional image guided IMRT. Our study shows that a 70% ventilation level is promising to be implemented into selective avoidance IMRT techniques for NSCLC.