Suppr超能文献

基于模型的术前三维磁共振图像到术中低分辨率三维及二维序列磁共振图像的可变形配准

Model-Based Deformable Registration of Preoperative 3D to Intraoperative Low-Resolution 3D and 2D Sequences of MR Images.

作者信息

Marami Bahram, Sirouspour Shahin, Capson David W

机构信息

Department of Electrical and Computer Engineering, McMaster University, 1280 Main Street West, Hamilton, ON, Canada, L8S-4K1.

出版信息

Med Image Comput Comput Assist Interv. 2011;14(Pt 1):460-7. doi: 10.1007/978-3-642-23623-5_58.

Abstract

We have developed an automatic model-based deformable registration method applicable to MR soft-tissue imaging. The registration algorithm uses a dynamic finite element (FE) continuum mechanics model of the tissue deformation to register its 3D preoperative images with intraoperative 1) 3D low-resolution or 2) 2D MR images. The registration is achieved through a filtering process that combines information from the deformation model and observation errors based on correlation ratio, mutual information or sum of square differences between images. Experimental results with a breast phantom show that the proposed method converges in few iterations in the presence of very large deformations, similar to those typically observed in breast biopsy applications.

摘要

我们开发了一种基于模型的自动可变形配准方法,适用于磁共振软组织成像。该配准算法使用组织变形的动态有限元(FE)连续介质力学模型,将术前3D图像与术中1)3D低分辨率图像或2)2D磁共振图像进行配准。配准通过一个滤波过程实现,该过程结合了来自变形模型的信息和基于图像间相关比、互信息或平方差之和的观测误差。使用乳腺模型的实验结果表明,在存在非常大变形的情况下,该方法在几次迭代中即可收敛,这种大变形类似于在乳腺活检应用中通常观察到的情况。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验