Real-Time 2D/3D Deformable Registration Using Metric Learning

Abstract

We present a novel 2D/3D deformable registration method, called Registration Efficiency and Accuracy through Learning Metric on Shape (REALMS), that can support real-time Image-Guided Radiation Therapy (IGRT). The method consists of two stages: planning-time learning and registration. In the planning-time learning, it firstly models the patient’s 3D deformation space from the patient’s time-varying 3D planning images using a low-dimensional parametrization. Secondly, it samples deformation parameters within the deformation space and generates corresponding simulated projection images from the deformed 3D image. Finally, it learns a Riemannian metric in the projection space for each deformation parameter. The learned distance metric forms a Gaussian kernel of a kernel regression that minimizes the leave-one-out regression residual of the corresponding deformation parameter. In the registration, REALMS interpolates the patient’s 3D deformation parameters using the kernel regression with the learned distance metrics. Our test results showed that REALMS can localize the tumor in 10.89 ms (91.82 fps) with 2.56±1.11 mm errors using a single projection image. These promising results show REALMS’s high potential to support real-time, accurate, and low-dose IGRT.