Lung registration using airway tree morphometry

This paper describes a non-linear medical image registration algorithm that aligns lung CT images scanned at different respiratory phases. The method uses landmarks obtained from the airway tree to find the airway branch extension lines and where the lines intersect the lung surface. The branch extension and lung intersection voxels on the surface were the crucial landmarks that initialize the non-rigid registration process. The advantage of these landmarks is that they have high correspondence between the matching patterns in the template images and deformed images. This method was developed and tested on CT examinations from participants in an asthma study. The registration accuracy was evaluated by the average distance between the corresponding airway tree branch points in the pair of images. The mean value of the distance between landmarks in template images and deformed matching images for subjects 1 and 2 were 8.44 mm (±4.46 mm) and 4.33 mm (± 3.78 mm), respectively. The results show that the lung image registration technique developed in this study may prove useful in quantifying longitudinal changes, performing regional analysis, tracking lung tumors, and compensating for subject motion across CT images.