Koutouzi Giasemi, Nasihatkton Behrooz, Danielak-Nowak Monika, Leonhardt Henrik, Falkenberg Mårten, Kahl Fredrik
Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg, Sweden.
K. N. Toosi University of Technology, Tehran, Iran.
BMC Med Imaging. 2018 Nov 8;18(1):42. doi: 10.1186/s12880-018-0285-1.
A crucial step in image fusion for intraoperative guidance during endovascular procedures is the registration of preoperative computed tomography angiography (CTA) with intraoperative Cone Beam CT (CBCT). Automatic tools for image registration facilitate the 3D image guidance workflow. However their performance is not always satisfactory. The aim of this study is to assess the accuracy of a new fully automatic, feature-based algorithm for 3D3D registration of CTA to CBCT.
The feature-based algorithm was tested on clinical image datasets from 14 patients undergoing complex endovascular aortic repair. Deviations in Euclidian distances between vascular as well as bony landmarks were measured and compared to an intensity-based, normalized mutual information algorithm.
The results for the feature-based algorithm showed that the median 3D registration error between the anatomical landmarks of CBCT and CT images was less than 3 mm. The feature-based algorithm showed significantly better accuracy compared to the intensity-based algorithm (p < 0.001).
A feature-based algorithm for 3D image registration is presented.
在血管内手术的术中引导图像融合中，一个关键步骤是术前计算机断层血管造影（CTA）与术中锥形束CT（CBCT）的配准。图像配准的自动工具有助于三维图像引导工作流程。然而，它们的性能并不总是令人满意。本研究的目的是评估一种用于CTA与CBCT三维配准的新型全自动、基于特征的算法的准确性。
在14例接受复杂血管内主动脉修复术的患者的临床图像数据集上测试基于特征的算法。测量血管和骨标志之间欧几里得距离的偏差，并与基于强度的归一化互信息算法进行比较。
基于特征的算法结果显示，CBCT和CT图像的解剖标志之间的三维配准误差中位数小于3毫米。与基于强度的算法相比，基于特征的算法显示出显著更高的准确性（p<0.001）。
提出了一种用于三维图像配准的基于特征的算法。
