Three-dimensional hybrid guidance system for cardiac interventional procedures

心脏介入手术三维混合引导系统

• 批准号: EP/X023826/1
• 负责人: YingLiang Ma
• 金额: $ 35.03万
• 依托单位: University of East Anglia
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX023826%2F1
• 关键词: Three; dimensional; hybrid; guidance; system

Minimally invasive cardiac surgeries are the common treatment for cardiovascular disease, involving the insertion of flexible devices (e.g. catheters or stents) into heart chambers. X-ray fluoroscopy is currently used to guide surgeons as the devices are highly visible under X-rays and modern X-ray systems provide real-time (i.e. with no lag) imaging, a large field-of-view and excellent image resolution. However, X-ray images offer very little anatomical information as surgeons cannot see where the heart chamber is and its surrounding blood vessels, unless contrast agents are injected. Furthermore, X-ray images are 2D images and so objects inside the image could overlap each other making it difficult to determine the accurate position of devices relative to the complex heart anatomy. This results in extended procedure times and thus additional harmful radiation doses. To add this anatomical information, hybrid guidance systems have been developed which combine the X-ray information with other information (e.g. from computerised tomography) to add the shadows or contours on the top of the X-ray images. The drawbacks of these systems are that they still heavily rely on X-ray fluoroscopic images to provide guidance, and all information is still 2D.The aim of this project is to develop a new 3D hybrid guidance system superior to these existing approaches. It will provide 3D information to surgeons, increasing their efficiency and thus reducing X-ray exposure. It will also use additional 3D guidance equipment such as the electroanatomical mapping (EAM) system to reduce the frequency of X-ray images, and so further reduce X-ray exposure. The EAM system uses a weak magnetic field rather than harmful X-ray radiation and so it can be switched on throughout the procedure. The primary use of the EAM system is to map electrophysiological activities within the heart. But it also can track catheters within a heart chamber and create low-resolution 3D models of heart chambers. It is not possible to visualise the 3D blood vessel structures clearly when using the EAM system and also some of devices such as stents and balloons might not be tracked. Hence the need for the proposed hybrid system with X-ray information.To develop this system we will use advanced computer vision techniques to detect devices and extract 3D blood vessel models from X-ray images, and then fuse these with existing 3D models inside the EAM system to provide the completed information to guide the procedure. Due to the high-level of noise present in low-dose X-ray images and the interference from overlapping objects, it is a challenging task to achieve accurate and robust detection in real-time. To meet the challenges, a novel approach is proposed to simultaneously detect the electrode catheters by the electrode pattern and the device on the wire by an image classifier. Since all devices are objects attached to the wires, our learning-base image classifiers will only need to search the areas along the wire-like objects. Furthermore, our approach will also be able to solve the challenge of the accurate alignment between 3D models in two systems measured in different coordinate systems. The alignment is based on tracking the 3D position of the same device in both an EAM system and an X-ray system. As it is possible to use the EAM system as the main guidance tool and use less frequent X-ray images, our proposed system will significantly reduce X-ray radiation exposure. This will benefit patients as X-ray radiation might cause the cancer in their later life. We will partner with Abbott Medical UK Ltd, and aim to develop and adapt our approach using Abbott's EAM system so that a research prototype can be made in the near future. But our theoretical contributions will not limited to the EAM system, and could be used to hybridise X-ray images with other image-guidance systems, such as the 3D echo imaging, as well as future robotic surgery systems.

微创心脏手术是心血管疾病的常见治疗方法，涉及将柔性装置（例如导管或支架）插入心脏腔室中。 X射线透视镜当前用于指导外科医生，因为在X射线下设备非常可见，现代X射线系统可提供实时（即没有滞后）成像，大型视野和出色的图像分辨率。但是，X射线图像几乎没有解剖信息，因为外科医生看不到心脏腔室的位置及其周围的血管，除非注射对比剂。此外，X射线图像是2D图像，因此图像内部的对象可能会彼此重叠，从而使很难确定相对于复杂心脏解剖结构的设备的准确位置。这会导致延长的程序时间，从而导致额外的有害辐射剂量。为了添加这些解剖信息，已经开发了混合引导系统，该系统将X射线信息与其他信息（例如，从计算机层析成像）结合在一起，以在X射线图像顶部添加阴影或轮廓。这些系统的缺点是它们仍然在很大程度上依赖X射线荧光镜图像来提供指导，并且所有信息仍然是2D。该项目的目的是开发一种新的3D混合指导系统，优于这些现有方法。它将向外科医生提供3D信息，从而提高其效率，从而减少X射线暴露。它还将使用其他3D引导设备，例如电解图（EAM）系统来降低X射线图像的频率，从而进一步减少X射线曝光。 EAM系统使用弱磁场而不是有害的X射线辐射，因此可以在整个过程中打开它。 EAM系统的主要用途是绘制心脏内的电生理活性。但是它也可以跟踪心脏室内的导管，并创建低分辨率的3D心脏室模型。当使用EAM系统时，不可能清楚地可视化3D血管结构，并且可能不会跟踪一些设备，例如支架和气球。因此，需要使用X射线信息的拟议混合动力系统。为了开发此系统，我们将使用先进的计算机视觉技术来检测设备并从X射线图像中提取3D血管模型，然后将其与现有的3D模型融合在一起EAM系统提供完整的信息来指导该过程。由于低剂量X射线图像中存在的高级噪声和重叠对象的干扰，因此实时实现准确且可靠的检测是一项艰巨的任务。为了应对挑战，提出了一种新颖的方法来通过电极图案同时检测电极导管，并通过图像分类器在电线上的设备检测到电极导管。由于所有设备都是附在电线上的对象，因此我们的学习基础图像分类器只需要搜索沿着线状对象的区域即可。此外，我们的方法还将能够解决在不同坐标系统中测得的两个系统中3D模型之间准确对齐的挑战。该对齐基于在EAM系统和X射线系统中跟踪同一设备的3D位置。由于可以将EAM系统用作主要的指导工具并使用较少的X射线图像，因此我们提出的系统将大大减少X射线辐射的暴露。这将使患者受益，因为X射线辐射可能会导致晚年癌症。我们将与Abbott Medical Uk Ltd合作，并旨在使用Abbott的EAM系统开发和适应我们的方法，以便可以在不久的将来制作研究原型。但是，我们的理论贡献不仅限于EAM系统，并且可以用来将X射线图像与其他图像指标系统（例如3D Echo成像以及未来的机器人手术系统）合并。