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.

中文翻译：

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基于周期对抗网络的先天性心脏病手术导航的CT-TEE图像配准。

经食道超声心动图（TEE）已成为介入心脏病专家的日常工具箱中的必不可少的工具，由于传感器位于食管正后方的位置，食管超声心动图能够连续可视化内脏器官的运动而无损伤，并实时观察心跳。心脏，在手术过程中对导航很有用。但是，TEE图像提供的关于清晰的心脏解剖结构的数据非常有限。取而代之的是，计算机断层扫描（CT）图像可以提供心脏结构的解剖信息，可以用作解释TEE图像的指南。在本文中，我们将重点介绍如何通过配准将解剖信息从CT图像传输到TEE图像，这是非常具有挑战性的，但由于同一个人的CT和TEE图像之间的极端形态变形和外观不同，对医生和临床医生而言意义重大。在本文中，我们提出了一种基于学习的方法将心脏CT图像注册为TEE图像。在所提出的方法中，为了减少两个图像之间的变形，我们将循环生成对抗网络（CycleGAN）引入到从CT图像模拟TEE图像的方法中，以减少它们的出现间隙。然后，我们执行非网格配准以将类似TEE的图像与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。