|本期目录/Table of Contents|

[1]崔建良,陈春晓△,陈志颖,等.基于T2DR-Net和互信息的光学-CT图像配准方法研究[J].生物医学工程研究,2022,02:143-150.
 CUI Jianliang,CHEN Chunxiao,CHEN Zhiying,et al.Optical-CTimage registration method based on T2DR-Net and mutual information[J].Journal of Biomedical Engineering Research,2022,02:143-150.
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基于T2DR-Net和互信息的光学-CT图像配准方法研究(PDF)

《生物医学工程研究》[ISSN:1006-6977/CN:61-1281/TN]

期数:
2022年02期
页码:
143-150
栏目:
出版日期:
2022-06-25

文章信息/Info

Title:
Optical-CTimage registration method based on T2DR-Net and mutual information
文章编号:
1672-6278 (2022)02-0143-08
作者:
崔建良陈春晓△陈志颖姜睿林
南京航空航天大学生物医学工程系,南京 211106
Author(s):
CUI Jianliang CHEN Chunxiao CHEN Zhiying JIANG Ruilin
Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
关键词:
图像配准荧光成像2D/3D配准纹理迁移互信息光学/CT图像配准
Keywords:
Image registrationFluorescence imaging2D/3D registrationTexture transferMutual informationOptical/CT image registration
分类号:
R318.51;TP391.9;TP183
DOI:
10.19529/j.cnki.1672-6278.2022.02.07
文献标识码:
A
摘要:
荧光分子断层成像技术(fluorescence molecular tomography, FMT)系统中为获得体内光源的结构信息,需要利用CT体数据。FMT系统在进行光学图像与CT图像的配准时,由于两种模态图像的成像原理、图像风格和图像维度等方面的差异,导致传统配准方法耗时长、效果差。本研究提出了一种基于T2DR-Net(texture transfer and dense registration net)与互信息的光学-CT图像配准方法,实现FMT系统中白光图像与CT图像的配准。该方法将光学-CT图像配准分为粗配准和精配准两个部分。在粗配准阶段,利用CycleGAN实现了FMT白光图像和CT投影像的纹理迁移,以降低两种图像纹理差异对图像配准的影响,并提出了DenseReg-Net模型获取白光图像和CT投影像粗配准参数;在精配准阶段,通过互信息方法进一步对两种模态图像配准,并得到最终的配准结果。利用1 330张光学图像和39 711张CT投影像作为样本集来验证配准方法的有效性,实验结果表明,本研究提出的光学-CT图像配准方法,相关系数为0.8797±0.0175,结构相似性为0.8683±0.0051,模型配准时间为(2.88±1.39) s。模型的配准效果及其稳定性优于传统方法。此外,与传统方法相比,速度提升了约60倍。
Abstract:
In order to obtain the structural information of the in vivo light source, the fluorescence molecular tomography (FMT) system needs to use computed tomography (CT) volume data. When the FMT system performs the registration of the optical image and the CT image, due to the differences in the imaging principles, image styles, and image dimensions of the two modal images, the traditional registration method takes a long time and has poor results. We proposed an optical-CT image registration model based on texture transfer and dense registration net (T2DR-Net) and mutual information to realize the registration of white light images and CT images in the FMT system. The method included two parts: rough registration and fine registration. In the rough registration stage, in order to reduce the impact of the difference of the two image textures on the image registration, CycleGAN was used to realize the texture transfer of FMT white light image and CT projection image, and the DenseReg-Net model was proposed to obtain the white light image and CT projection image rough registration parameters. In the fine registration stage, the mutual information method was used for registration correction, and the final registration result was obtained. We used 1 330 optical images and 39 711 CT projection images as sample sets to verify the validity of the registration method. The results of the registration showed that this optical-CT image registration model had a correlation coefficient of 0.8797±0.0175 and a structural similarity of 0.8683±0.0051,the registration time of this model was(2.88±1.39) s. The registration effect and stability of the model are better than that of traditional methods, and it is about 60 times faster than the traditional method.

参考文献/References

备注/Memo

备注/Memo:
(收稿日期:2021-11-06)△通信作者Email: ccxbme@nuaa.edu.cn
更新日期/Last Update: 2022-07-21