Background: Medical image interpolation is recently introduced as a helpful tool to obtain further information via initial available images taken by tomography systems. This information may be useful for better diagnosis of possible lesions or better tumor delineation at radiation treatment. To do this, deformable image registration algorithms are mainly utilized to perform image interpolation using tomography images. Materials and Methods: In this work, 4DCT thoracic images of five real patients provided by DIR-lab group were utilized. Four implemented registration algorithms as 1) Original Horn-Schunck, 2) Inverse consistent Horn-Schunck, 3) Original Demons and 4) Fast Demons were implemented to represent deformation fields by means of DIRART software packages. Then, the calculated vector fields are processed to reconstruct 4DCT images at any desired time using optical flow based on interpolation method. As a comparative study, the accuracy of interpolated image obtained by each strategy is measured by calculating mean square error between the interpolated image and real middle image as ground truth dataset. Results: Final results represent the ability to accomplish image interpolation among given two-paired images. Among them, Inverse Consistent Horn-Schunck algorithm has the best performance to reconstruct interpolated image with the highest accuracy while Demons method had the worst performance. Conclusion: A comparative study was conducted to quantitatively investigate the role of four available deformable image registrations for finding interpolated virtual image between two consequent tomography images. Since image interpolation is affected by increasing the distance between two given available images, the performance accuracy of four different registration algorithms is investigated concerning this issue. As a result, Inverse Consistent Horn-Schunck does not essentially have the best performance especially in facing large displacements happened due to distance increment.
A Progressive Reduced Basis/Empirical Interpolation Method for Nonlinear Parabolic Problems
