Automatic error correction using adaptive weighting for vessel-based deformable image registration.

In this paper, we extend our previous work on deformable image registration to inhomogenous tissues. Inhomogenous tissues include the tissues with embedded tumors, which is common in clinical applications. It is a very challenging task since the registration method that works for homogenous tissues may not work well with inhomogenous tissues. The maximum error normally occurs in the regions with tumors and often exceeds the acceptable error threshold. In this paper, we propose a new error correction method with adaptive weighting to reduce the maximum registration error. Our previous fast deformable registration method is used in the inner loop. We have also proposed a new evaluation metric average error of deformation field (AEDF) to evaluate the registration accuracy in regions between vessels and bifurcation points. We have validated the proposed method using liver MR images from human subjects. AEDF results show that the proposed method can greatly reduce the maximum registration errors when compared with the previous method with no adaptive weighting. The proposed method has the potential to be used in clinical applications to reduce registration errors in regions with tumors.