4DCT-Derived ventilation distribution reproducibility over time

Abstract

Deriving lung ventilation distribution from 4-dimensional CT (4DCT) using deformable image registration (DIR) is a recent technical development. In this study, we evaluated the serial reproducibility of ventilation data derived from two separate 4DCT data sets, collected at different time points. A total of 33 lung cancer patients were retrospectively analysed. All patients had two stereotactic body radiotherapy treatment courses for lung cancer. Seven patients were excluded due to artifacts in the 4DCT data sets. The ventilation distributions for each patient were calculated using the two sets of planning 4DCT data. The deformation matrices between the expiration and inspiration phases generated by DIR were used to produce ventilation distributions using the ΔV method. Ventilation was analysed in the lung regions that received less than 1 Gy and in the contralateral lung respectively. For the 26 cases, the median Spearman correlation coefficient value was 0.31 (range 0.18 to 0.52, p value < 0.01 for all cases) in the regions of <1 Gy and 0.32 (range 0.19 to 0.51) in the contralateral lungs. The median Dice similarity coefficient value between the upper 30% ventilation regions of the two sets was 0.53 (range 0.40 to 0.64) in the regions of <1 Gy and 0.53 (range 0.44 to 0.70) in the contralateral lungs. We conclude that the two ventilation data sets in each case correlated and the reproducibility over time was fair.