Cancer Science & Therapy

3D Volumetric Visualization with Automatic Rigid and Deformable Hybrid Image Registration for Adaptive Radiotherapy

Abstract

Chang-Yu Wang, Tsair-Fwu Lee and Chun-Hsiung Fang

To provide more clinically convenient image fusions for adaptive radiotherapy (ART), an automatic rigid and deformable image registration framework (AIRF) is developed for multimodal visualization of multiple chronological images and multiple radiotherapy (RT) plans. Our hybrid image registration framework, AIRF, uses a faster but less accurate rigid registration method to provide an initial registration, followed by a slower but more accurate deformable registration method to fine tune the final registration. A multi-resolution approach is also employed in the image registration process to further improve the registration accuracy, robustness and efficiency. Volume visualization is provided to guide the automatic image registration process because it can reduce the global positioning error that results from a partial 3D visual presentation in the three conventional orthogonal planar views (axial, sagittal, and coronal). The AIRF can automatically align multiple volumetric images of patients taken over an extended period of time and can merge multiple radiotherapy plans based on different planning computed tomography (CT) images. It offers illustrative 3D volumetric visualization, hybrid rigid and deformable image registration, and automatic transfer of RT dose distribution and RT structure models such as treatment targets and organs at risk (OARs) onto chronological images. The AIRF can automatically register multiple volumetric image datasets of patients taken over an extended period of time and can merge multiple RT plans based on different planning CT images for 4D or adaptive radiotherapy.