Mountney Peter, Ionasec Razvan, Kaizer Markus, Mamaghani Sina, Wu Wen, Chen Terrence, John Matthias, Boese Jan, Comaniciu Dorin
Siemens Corporate Research & Technology, Princeton, USA.
Med Image Comput Comput Assist Interv. 2012;15(Pt 2):544-51. doi: 10.1007/978-3-642-33418-4_67.
New minimal-invasive interventions such as transcatheter valve procedures exploit multiple imaging modalities to guide tools (fluoroscopy) and visualize soft tissue (transesophageal echocardiography (TEE)). Currently, these complementary modalities are visualized in separate coordinate systems and on separate monitors creating a challenging clinical workflow. This paper proposes a novel framework for fusing TEE and fluoroscopy by detecting the pose of the TEE probe in the fluoroscopic image. Probe pose detection is challenging in fluoroscopy and conventional computer vision techniques are not well suited. Current research requires manual initialization or the addition of fiducials. The main contribution of this paper is autonomous six DoF pose detection by combining discriminative learning techniques with a fast binary template library. The pose estimation problem is reformulated to incrementally detect pose parameters by exploiting natural invariances in the image. The theoretical contribution of this paper is validated on synthetic, phantom and in vivo data. The practical application of this technique is supported by accurate results (< 5 mm in-plane error) and computation time of 0.5s.
诸如经导管瓣膜手术等新型微创干预措施利用多种成像方式来引导工具（荧光透视）并可视化软组织（经食管超声心动图（TEE））。目前，这些互补的成像方式在不同的坐标系中以及不同的监视器上显示，这给临床工作流程带来了挑战。本文提出了一种通过在荧光透视图像中检测TEE探头的位姿来融合TEE和荧光透视的新颖框架。在荧光透视中，探头位姿检测具有挑战性，传统的计算机视觉技术并不适用。当前的研究需要手动初始化或添加基准标记。本文的主要贡献是通过将判别式学习技术与快速二进制模板库相结合实现自主六自由度位姿检测。通过利用图像中的自然不变性，将位姿估计问题重新表述为逐步检测位姿参数。本文的理论贡献在合成数据、体模数据和体内数据上得到了验证。该技术的实际应用得到了准确结果（平面内误差<5毫米）和0.5秒计算时间的支持。
