Abstract

Info

Title:: 2D-3D double X-ray image registration method based on neural network

Author(s):: SHEN Yanyan1; FENG Hansheng2; 1. School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China; 2. Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China

Keywords:: Keywords: double X-ray image; 2D-3D registration; convolutional neural network; geometric decomposition

Abstract:: Abstract: Based on the situation that the traditional 2D-3D medical image registration algorithm based on iterative optimization can not realize real-time registration due to slow running speed, a real-time 2D-3D registration method is proposed. By decomposing spatial rigid transformation parameters into two planes, 2D-3D registration is simplified into two steps, including 2D-2D approximate rigid registration and single-parameter 2D-3D grid registration. Meanwhile, deep convolutional neural network is used to fit the nonlinear mapping between X-ray images residual and its corresponding postural difference, and the space rigid transformation parameters are regressed from the residual of the X-DRR image pair. The network trained by head CT data can complete the high-precision double X-ray registration within 0.04 s. The proposed registration method can satisfy the requirements of real-time 2D-3D registration during radiotherapy.