A Multi-component similarity measure for improved robustness of non-rigid registration of combined FDG PET-CT head and neck images

Abstract

Our intention was to register combined FDG PET-CT head and neck images from multiple patients to a template image. We used a free-form deformation (FFD) nonrigid registration (NRR) algorithm. We aimed to overcome instances of mis-registration at the coarse scale that occur when using a similarity measure derived from the CT images alone. We proposed a multi-component (MC) similarity measure to improve the robustness of the registration by utilizing information from both the PET and the CT image. Information from all combinations of the dual modality dataset were incorporated into this measure. We defined the MC similarity measure to be a weighted sum of four components: the normalized sum of squared differences (SSD) for the CT-CT component and normalized mutual information (NMI) for the other components. This function was then used to register a group of combined PET-CT images demonstrating normal anatomy and FDG uptake to the template image. The performance of the registration was assessed by measuring the errors across three bony anatomical landmarks within the image. The results were compared to the registration using SSD of the CT images alone as a similarity measure. Registrations performed using this MC similarity measure demonstrated improved robustness compared to using the SSD of the CT images alone as a similarity measure. © 2009 Springer Berlin Heidelberg.