Abstract
Transesophageal echocardiography (TEE) plays a pivotal role in cardiology for diagnostic and interventional procedures. However, using it effectively requires extensive training due to the intricate nature of image acquisition and interpretation. To enhance the efficiency of novice sonographers and reduce variability in scan acquisitions, we propose a novel ultrasound (US) navigation assistance method based on contrastive learning as goal-conditioned reinforcement learning (GCRL). We augment the previous framework using a novel contrastive patient batching method (CPB) and a data-augmented contrastive loss, both of which we demonstrate are essential to ensure generalization to anatomical variations across patients. The proposed framework enables navigation to both standard diagnostic as well as intricate interventional views with a single model. Our method was developed with a large dataset of 789 patients and obtained an average error of 6.56 mm in position and 9.36 degrees in angle on a testing dataset of 140 patients, which is competitive or superior to models trained on individual views. Furthermore, we quantitatively validate our method's ability to navigate to interventional views such as the Left Atrial Appendage (LAA) view used in LAA closure. Our approach holds promise in providing valuable guidance during transesophageal ultrasound examinations, contributing to the advancement of skill acquisition for cardiac ultrasound practitioners.
Abstract (translated)
经食道超声检查（TEE）在心血管病学中具有重要的诊断和干预作用。然而，要有效地使用它，需要进行广泛的培训，因为图像获取和解释的复杂性。为了提高新手超声技术员的效率，减少扫描获取的变异性，我们提出了一种基于对比学习的目标条件强化学习（GCRL）超声导航辅助方法。我们通过一种新颖的对比患者批量方法（CPB）和数据增强对比损失来增强先前的框架。我们证明了CPB和数据增强对比损失对确保患者间解剖变异的泛化至关重要。所提出的框架能够通过单个模型实现对标准诊断和复杂干预视图的导航。我们的方法基于一个大型数据集（789名患者）开发，在测试数据集（140名患者）上的平均误差为6.56毫米的位置和9.36度的角度，与单个视图训练的模型相当或更好。此外，我们通过定量验证了我们的方法在到达干预视图（如左心房附壁）方面的能力，这些视图在LAA关闭中使用。我们的方法在提供心血管超声检查中的有价值的指导方面具有潜力，有助于提高心脏超声技术员的技能。
URL
https://arxiv.org/abs/2405.01409
