Ultrasound Lung Aeration Map via Physics-Aware Neural Operators
Journal:
arXiv
Published Date:
Jan 2, 2025
Abstract
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, 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. LUNA offers 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 lung
scans. We demonstrate the potential of reconstructing lung aeration maps from
RF data, providing a foundation for improving lung ultrasound reproducibility
and diagnostic utility.
