3D-3D Tubular Organ Registration and Bifurcation Detection from CT Images

Abstract

The registration of tubular organs (pulmonary tracheobronchial tree or vasculature) of 3D medical images is critical in various clinical applications such as surgical planning and radiotherapy. For example, the pulmonary tracheobronchial tree or vascular structures can be used as the landmarks in lung tumor resection planning; the quantifying treatment effectiveness of the radiotherapy on lung nodules is based on the registration of the pulmonary tracheobronchial tree or vessels; the planning inter-patients partial liver transplants use registered contrast injection angiography (CTA) to create digital-subtraction contrast injection angiography (CTA) of liver vessels. The bifurcation of the tubular organs plays a critical role in clinical practices as well. Inflammation caused by bronchitis alters the airway branching configuration which causes various breathing problems (Luo et al., 2007). Atherosclerotic disease at the bifurcation has been widely known as a risk factor for cerebral ischemic episodes and infarction (Binaghi et al., 2001). The bifurcation points (or the branching points) have been chosen to build the validation protocol of the registration methods (Gee et al., 2002). Many researchers have developed various methods for registration of tubular organs from medical images. Baert et al. (2004) used an intensity based 2D-3D registration algorithm to register the pre-operative 3D Magnetic Resonance Angiogram (MRA) data to the interventional digital subtraction angiography (DSA) images. Chan et al. (2004) proposed a 2D-3D vascular registration algorithm based on minimizing the sum of squared differences between the projected image and the reference DSA image. However, these registration methods are all developed for applications with 2D-3D registration. Chan & Chung (2003) solves a 3D3D registration problem by transform the problem into 2D-3D registration problem. Aylward