3D intersubject warping and registration of pulmonary CT images for a human lung model

Abstract

CT can be used to study pulmonary structure-function relationships. There is a growing clinical need to match pulmonary structures across individuals to detect abnormal structure due to disease and to compare regional pulmonary function. In this paper, we propose a novel scheme for registering and warping 3-D pulmonary CT images of different subjects in two main steps: 1) identify a set of reproducible feature points for each CT image to establish correspondences across subjects; 2) use a landmark and intensity-based consistent image registration algorithm to warp a template image volume to the rest of the lung volumes. Effectiveness of the proposed scheme is evaluated and visualized using both gray-level and segmented CT images. Results show that the proposed scheme is able to reduce landmark registration error and relative volume overlapping error from 10.5 mm and 0.70 before registration to 0.4 mm and 0.11, respectively. The proposed scheme can be used to construct a computerized human lung model (or atlas) to help detect abnormal lung structural changes.