In this paper, we investigated a parameter optimization strategy for Optical-flow based DIR of the 4DCT lung anatomy. A novel fast simulated annealing with adaptive Monte Carlo sampling algorithm (FSA-AMC) was investigated for solving the complex non-convex parameter optimization problem. The metric for registration error for a given parameter set was computed using landmark-based mean target registration error (mTRE) between a given volumetric image pair. To reduce the computational time in the parameter optimization process, a GPU based 3D dense optical-flow algorithm was employed for registering the lung volumes.
Numerical analyses on the parameter optimization for the DIR were performed using 4DCT datasets generated with breathing motion models and open-source 4DCT datasets.
Results showed that the proposed method efficiently estimated the optimum parameters for optical-flow and closely matched the best registration parameters obtained using an exhaustive parameter search method.