Transthoracic Echocardiography (TTE) is a fundamental, non-invasive diagnostic tool in cardiovascular medicine, enabling detailed visualization of cardiac structures crucial for diagnosing various heart conditions. Despite its widespread use, TTE ultrasound imaging faces inherent limitations, notably the trade-off between field of view (FoV) and resolution. This paper introduces a novel application of conditional Generative Adversarial Networks (cGANs), specifically designed to extend the FoV in TTE ultrasound imaging while maintaining high resolution. Our proposed cGAN architecture, termed echoGAN, demonstrates the capability to generate realistic anatomical structures through outpainting, effectively broadening the viewable area in medical imaging. This advancement has the potential to enhance both automatic and manual ultrasound navigation, offering a more comprehensive view that could significantly reduce the learning curve associated with ultrasound imaging and aid in more accurate diagnoses. The results confirm that echoGAN reliably reproduce detailed cardiac features, thereby promising a significant step forward in the field of non-invasive cardiac naviagation and diagnostics.

翻译：经胸超声心动图（TTE）是心血管医学中一种基础性、非侵入性的诊断工具，能够对心脏结构进行详细可视化，这对于诊断各种心脏疾病至关重要。尽管其应用广泛，TTE超声成像仍面临固有的局限性，尤其是视野（FoV）与分辨率之间的权衡。本文介绍了一种条件生成对抗网络（cGAN）的新颖应用，该网络专门设计用于在保持高分辨率的同时扩展TTE超声成像的视野。我们提出的cGAN架构，命名为echoGAN，展示了通过外绘生成逼真解剖结构的能力，有效拓宽了医学成像的可视区域。这一进展有望同时增强自动和手动超声导航，提供更全面的视图，从而显著降低超声成像相关的学习曲线，并有助于实现更精准的诊断。结果证实，echoGAN能够可靠地复现详细的心脏特征，因此有望在非侵入性心脏导航与诊断领域迈出重要一步。