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基于级联分类器和图像配准的 3D 超声实时配准 X 射线透视。

Real-time registration of 3D echo to x-ray fluoroscopy based on cascading classifiers and image registration.

机构信息

School of Computing, Electronics and Mathematics, Coventry University, CV1 5FB, United Kingdom.

School of Biomedical Engineering and Imaging Sciences, King's College London, SE1 7EH, United Kingdom.

出版信息

Phys Med Biol. 2021 Feb 25;66(5):055019. doi: 10.1088/1361-6560/abe420.

DOI:10.1088/1361-6560/abe420
PMID:33556925
Abstract

Three-dimensional (3D) transesophageal echocardiography (TEE) is one of the most significant advances in cardiac imaging. Although TEE provides real-time 3D visualization of heart tissues and blood vessels and has no ionizing radiation, x-ray fluoroscopy still dominates in guidance of cardiac interventions due to TEE having a limited field of view and poor visualization of surgical instruments. Therefore, fusing 3D echo with live x-ray images can provide a better guidance solution. This paper proposes a novel framework for image fusion by detecting the pose of the TEE probe in x-ray images in real-time. The framework does not require any manual initialization. Instead it uses a cascade classifier to compute the position and in-plane rotation angle of the TEE probe. The remaining degrees of freedom are determined by fast marching against a template library. The proposed framework is validated on phantoms and patient data. The target registration error for the phantom was 2.1 mm. In addition, 10 patient datasets, seven of which were acquired from cardiac electrophysiology procedures and three from trans-catheter aortic valve implantation procedures, were used to test the clinical feasibility as well as accuracy. A mean registration error of 2.6 mm was achieved, which is well within typical clinical requirements.

摘要

三维(3D)经食管超声心动图(TEE)是心脏成像领域的重大进展之一。尽管 TEE 提供了心脏组织和血管的实时 3D 可视化,且没有电离辐射,但由于 TEE 的视野有限,手术器械的可视化效果不佳,X 射线透视仍在心脏介入指导中占据主导地位。因此,将 3D 超声与实时 X 射线图像融合可以提供更好的指导解决方案。本文提出了一种通过实时检测 X 射线图像中 TEE 探头的姿态来进行图像融合的新框架。该框架不需要任何手动初始化,而是使用级联分类器来计算 TEE 探头的位置和平面内旋转角度。其余自由度通过快速行进模板库来确定。该框架在体模和患者数据上进行了验证。体模的目标配准误差为 2.1 毫米。此外,还使用了 10 个患者数据集进行测试,其中 7 个来自心脏电生理程序,3 个来自经导管主动脉瓣植入程序,以测试临床可行性和准确性。实现了 2.6 毫米的平均配准误差,完全符合典型的临床要求。

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