Local tissue-weight-based nonrigid registration of lung images with application to regional ventilation

摘要

In this paper, a new nonrigid image registration method is presented to align two volumetric lung CT datasets with an application to estimate regional ventilation. Instead of the sum of squared intensity difference (SSD), we introduce the sum of squared tissue volume difference (SSTVD) as the similarity criterion to take into account the variation of intensity due to respiration. This new criterion aims to minimize the local difference of tissue volume inside the lungs between two images scanned in the same session or over short periods of time, thus preserving the tissue weight of the lungs. Our approach is tested using a pair of volumetric lung datasets acquired at 15% and 85% of vital capacity (VC, VC=5.05 liters for this subject) in a single scanning session. The results show that the new SSTVD predicts a smaller registration error and also yields a better alignment of structures within the lungs than the normal SSD similarity measure. In addition, the regional ventilation derived from the new method exhibits a much more improved physiological pattern than that of SSD.