Generative Adversarial Networks in Ultrasound Imaging: Extending Field of View Beyond Conventional Limits

2024-05-31

arXiv.org

AI Summary

To address the clinical challenge in transthoracic echocardiography (TTE) of simultaneously achieving wide field-of-view (FoV) and high spatial resolution, this paper proposes echoGAN—the first conditional generative adversarial network (cGAN)-based extrapolation method specifically designed for TTE. echoGAN introduces two key innovations: (1) an anatomy-guided prior constraint loss to enforce anatomical plausibility, and (2) a multi-scale discriminator to preserve fine-grained structural details. The method achieves high-fidelity, anatomically consistent FoV expansion while retaining sub-millimeter resolution of critical cardiac structures—including myocardium and valves. Evaluated on real-world TTE data, extrapolated regions attain SSIM > 0.92 and PSNR > 28 dB; crucial anatomical structures are reconstructed with diagnostic-level fidelity, satisfying clinical interpretability requirements. Consequently, echoGAN significantly enhances procedural navigation robustness and diagnostic coverage during TTE examinations.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

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.

Problem

• Trans-thoracic Echocardiography
• Image Resolution
• Diagnostic Accuracy

Innovation

• echoGAN
• cGANs
• TTE Image Enhancement