|Table of Contents|

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.

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Last Update: 2020-04-02