A Non-rigid Registration Framework That Accommodates Pathology Detection

Image-guided external beam radiation therapy (EBRT) for the treatment of cancer enables accurate placement of radiation dose to the cancerous region. However, the deformation of soft tissue during the course of treatment, such as in cervical cancer, presents significant challenges. Furthermore, the presence of pathologies such as tumors may violate registration constraints and cause registration errors. In this paper, we present a novel MAP framework that performs nonrigid registration and pathology detection simultaneously. The matching problem here is defined as a mixture of two different distributions which describe statistically image gray-level variations for two pixel classes (i.e. tumor class and normal tissue class). The determinant of the transformation's Jacobian is also constrained, which guarantees the transformation to be smooth and simulates the tumor regression process. We perform the experiments on 30 patient MR data to validate our approach. Quantitative analysis of experimental results illustrate the promising performance of this method in comparison to previous techniques.