4D-CT Lung registration using anatomy-based multi-level multi-resolution optical flow analysis and thin-plate splines

The accuracy of 4D-CT registration is limited by inconsistent Hounsfield unit (HU) values in the 4D-CT data from one respiratory phase to another and lower image contrast for lung substructures. This paper presents an optical flow and thin-plate spline (TPS)-based 4D-CT registration method to account for these limitations. The use of unified HU values on multiple anatomy levels (e.g., the lung contour, blood vessels, and parenchyma) accounts for registration errors by inconsistent landmark HU value. While 3D multi-resolution optical flow analysis registers each anatomical level, TPS is employed for propagating the results from one anatomical level to another ultimately leading to the 4D-CT registration. 4D-CT registration was validated using target registration error (TRE), inverse consistency error (ICE) metrics, and a statistical image comparison using Gamma criteria of 1 % intensity difference in window range. Validation results showed that the proposed method was able to register CT lung datasets with TRE and ICE values 3 mm. In addition, the average number of voxel that failed the Gamma criteria was 3 %, which supports the clinical applicability of the propose registration mechanism. The proposed 4D-CT registration computes the volumetric lung deformations within clinically viable accuracy.