2D-3D double X-ray image registration method based on neural network(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2020年第3期
- Page:
- 293-298
- Research Field:
- 医学影像物理
- Publishing date:
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
- PACS:
- R318
- DOI:
- DOI:10.3969/j.issn.1005-202X.2020.03.007
- 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.
Last Update: 2020-04-02