Ultrasound Lung Aeration Map via Physics-Aware Neural Operators

作     者：Wang, Jiayun Ostras, Oleksii Sode, Masashi Tolooshams, Bahareh Li, Zongyi Azizzadenesheli, Kamyar Pinton, Gianmarco Pinton, Gianmarco F. Anandkumar, Anima 

作者机构：Department of Computing and Mathematical Sciences California Institute of Technology 1200 E California Blvd PasadenaCA91125 United States Department of Biomedical Engineering University of North Carolina at Chapel Hill 103 South Building Chapel HillNC27514 United States Department of Biomedical Engineering North Carolina State University Campus Box 7625 RaleighNC27695 United States NVIDIA 2788 San Tomas Express Way Santa ClaraCA95051 United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2025年

核心收录：

主　　题：Beamforming 

摘      要：Lung ultrasound is a growing modality in clinics for diagnosing and monitoring acute and chronic lung diseases due to its low cost and accessibility. Lung ultrasound works by emitting diagnostic pulses, receiving pressure waves and converting them into radio frequency (RF) data, which are then processed into B-mode images with beamformers for radiologists to interpret. However, unlike conventional ultrasound for soft tissue anatomical imaging, lung ultrasound interpretation is complicated by complex reverberations from the pleural interface caused by the inability of ultrasound to penetrate air. The indirect B-mode images make interpretation highly dependent on reader expertise, requiring years of training, which limits its widespread use despite its potential for high accuracy in skilled hands. To address these challenges and democratize ultrasound lung imaging as a reliable diagnostic tool, we propose Luna (the Lung Ultrasound Neural operator for Aeration), an AI model that directly reconstructs lung aeration maps from RF data, bypassing the need for traditional beamformers and indirect interpretation of B-mode images. Luna uses a Fourier neural operator, which processes RF data efficiently in Fourier space, enabling accurate reconstruction of lung aeration maps. From reconstructed aeration maps, we calculate lung percent aeration, a key clinical metric, offering a quantitative, reader-independent alternative to traditional semi-quantitative lung ultrasound scoring methods. The development of Luna involves synthetic and real data: We simulate synthetic data with an experimentally validated approach and scan ex vivo swine lungs as real data. Trained on abundant simulated data and fine-tuned with a small amount of real-world data, Luna achieves robust performance, demonstrated by an aeration estimation error of 9% in ex-vivo swine lung scans. We demonstrate the potential of directly reconstructing lung aeration maps from RF data, providing a foundation for improving