Four-dimensional computed tomography (4D CT) which clearly includes the temporal changes in anatomy during the diagnosis, planning, and delivery of radiotherapy has great promise. Deformable image registration has the potential to reduce the geometrical uncertainty of the target, and makes it possible to signally improve the treatment accuracy by optimizing treatment in response to anatomical uncertainty. In this paper, we used Scale Invariant Feature Transform (SIFT) algorithm to extract landmark points, and we proposed a registration method based on B-Spline model, then used a limited memory quasi-Newton method to optimize the system, also calls the limited memory BFGS (L-BFGS) method. The deformable registration model B-Spline model can derive the images at all intermediate phases from sets of 3D images acquired at a few known phase points. Because 4D CT can track the location of region of interest (ROI) and tumors over several respiratory cycles, so 4D CT can make the apparent size of the tumor which is caused by breathing motion more accurate. The method is evaluated on 10 4D-CT data sets of patients in a breathing cycle.
LungRegNet: An unsupervised deformable image registration method for 4D‐CT lung