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) 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.
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