Image registration for PET/CT and CT images with particle swarm optimization

The objective of this research is to develop 2D/3D registration algorithm for PET/CT and CT or MR images acquired by different systems at different times. We matched two anatomical images first (one from PET/CT and the other from standalone CT or MR) that contains affluent anatomical information. Then we geometrically transformed PET image according to the result of transformation parameters calculated by previous step. We developed two stages of registration algorithm. The first stage is global registration. It is consists of 4 independent steps. After selection of reference and target images different data types and ROI of images have been normalized in the preprocessing step. As a next step, target image is geometrically transformed then the similarity between two images has been measured quantitatively. The optimization step updates transformation parameters to find the best matched parameter set efficiently. In second stage that is called fine adjustment, we introduce feature based registration algorithm. The features of each image slices are extracted by independent component analysis(ICA) and the extracted feature plane is used to measure the similarity. B-spline based freeform deformation is done to form a registered image as a final step. The result of proposed algorithm shows good agreement of images that between PET/CT to CT and PET/CT to MR. We will expand the application of the algorithm to different imaging modalities.