Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi'an 710071, China.
Department of Infectious Diseases, Ankang Central Hospital, Ankang 725000, China.
Comput Math Methods Med. 2020 Jul 2;2020:4942121. doi: 10.1155/2020/4942121. eCollection 2020.
Transesophageal echocardiography (TEE) has become an essential tool in interventional cardiologist's daily toolbox which allows a continuous visualization of the movement of the visceral organ without trauma and the observation of the heartbeat in real time, due to the sensor's location at the esophagus directly behind the heart and it becomes useful for navigation during the surgery. However, TEE images provide very limited data on clear anatomically cardiac structures. Instead, computed tomography (CT) images can provide anatomical information of cardiac structures, which can be used as guidance to interpret TEE images. In this paper, we will focus on how to transfer the anatomical information from CT images to TEE images via registration, which is quite challenging but significant to physicians and clinicians due to the extreme morphological deformation and different appearance between CT and TEE images of the same person. In this paper, we proposed a learning-based method to register cardiac CT images to TEE images. In the proposed method, to reduce the deformation between two images, we introduce the Cycle Generative Adversarial Network (CycleGAN) into our method simulating TEE-like images from CT images to reduce their appearance gap. Then, we perform nongrid registration to align TEE-like images with TEE images. The experimental results on both children' and adults' CT and TEE images show that our proposed method outperforms other compared methods. It is quite noted that reducing the appearance gap between CT and TEE images can benefit physicians and clinicians to get the anatomical information of ROIs in TEE images during the cardiac surgical operation.
经食管超声心动图（TEE）已成为介入心脏病学家日常工具包中的重要工具，它允许在没有创伤的情况下连续观察内脏器官的运动，并实时观察心跳，这是由于传感器位于心脏后面的食管位置，因此在手术过程中对导航很有用。然而，TEE 图像提供的关于心脏结构的清晰解剖学信息非常有限。相反，计算机断层扫描（CT）图像可以提供心脏结构的解剖学信息，这些信息可用于指导 TEE 图像的解释。在本文中，我们将重点介绍如何通过配准将 CT 图像的解剖学信息传输到 TEE 图像，这对医生和临床医生来说是一项极具挑战性但非常重要的任务，因为同一人的 CT 和 TEE 图像之间存在极端的形态变形和外观差异。在本文中，我们提出了一种基于学习的方法，将心脏 CT 图像配准到 TEE 图像。在提出的方法中，为了减少两幅图像之间的变形，我们引入了循环生成对抗网络（CycleGAN），从 CT 图像模拟 TEE 样图像，以缩小它们的外观差距。然后，我们进行非网格配准，将 TEE 样图像与 TEE 图像对齐。在儿童和成人的 CT 和 TEE 图像上的实验结果表明，我们提出的方法优于其他比较方法。值得注意的是，缩小 CT 和 TEE 图像之间的外观差距可以使医生和临床医生在心脏手术过程中受益，以获得 TEE 图像中 ROI 的解剖学信息。
