Vessel-based registration with application to nodule detection in thoracic CT scans

Volume registration is fundamental to multiple medical imaging algorithms. Specifically, non-rigid registration of thoracic CT scans taken at different time instances can be used to detect new nodules more reliably and assess the growth rate of existing nodules. Voxel-based registration techniques are generally sensitive to intensity variation and structural differences, which are common in CT scans due to partial volume effects and naturally occurring motion and deformations. The approach we propose in this paper is based on vessel tree extraction which is then used to infer the complete volume registration. Vessels form unique features with good localization. Using extracted vessel trees, a minimization process is used to estimate the motion vectors at vessels. Accurate motion vectors are obtained at vessel junctions whereas vessel segments support only normal component estimation. The obtained motion vectors are then interpolated to produce a dense motion field using thin plate splines. The proposed approach is evaluated on both real and synthetically deformed volumes. The obtained results are compared to several standard registration techniques. It is shown that by using vessel structure, the proposed approach results in improved performance.