Perfusion computed tomography (CT) has been widely used to assess the response of lung cancer treatment. However,
the respiratory motion has become the major obstacle to the pixel-based time-series analyses. To minimize the effect of
respiratory motion and investigate the feasibility of perfusion CT for prediction of tumor response and prognosis of non-small cell lung cancer, an image registration framework is proposed by unifying a virtual 3D local rigid alignment and 3D global non-rigid alignment. The basic idea is to use the perfusion CT data and routine whole-lung CT data,
respectively. To realize this idea, maximum intensity projection (MIP) of the time series perfusion CT images is first
generated, followed by decomposing the MIP image into region of interest (ROI), which is located on a lung nodule. For the ROI, affine transformation model based on mutual information is performed to estimate the virtual three dimensional linear deformations. Following that, the 3D thin plate spline (TPS) is carried out to establish the pixel correspondence between the paired volumetric CT data. The control points for the TPS are global feature points chosen from the boundary of whole lung, which are automatically derived by using the iterative closest point (ICP) matching Algorithm. The proposed algorithm has been evaluated both qualitatively and quantitatively on real lung perfusion CT datasets. From the time-intensity curves and perfusion parameters, the experiment results suggest that the findings on perfusion CT images obtained after treatment may be considered as a significant predictor of lung cancer.