Estimating large lung motion in COPD patients by symmetric regularised correspondence fields

Abstract

This paper presents a new and highly efficient approach for finding correspondences across volumes with large motion. Most existing registration approaches are set in the continuous optimisation domain, which has severe limitations for estimating larger deformations. Featurebased approaches that rely on finding corresponding keypoints have been proposed, but they are prone to erroneous matching due to repetitive features and low contrast areas. This can be overcome by using a discrete optimisation approach. However, finding a constrained search space and regularisation strategy is still an open problem. Our method calculates a dissimilarity distribution over a densely sampled space of displacements for a small number of distinctive keypoints (found in only one volume). A parts-based model is used to infer smooth motion of connected keypoints and regularise the correspondence field. This effective and highly accurate approach is further improved by enforcing the symmetry of uncertainty estimates of displacements. Our method ranks first on one of the most challenging medical registration benchmarks for breath-hold CT scan-pairs of COPD patients, where accurate motion estimation is important for diagnosis.