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Related Experiment Video

Updated: Jun 29, 2026

Protocol and Guidelines for Point-of-Care Lung Ultrasound in Diagnosing Neonatal Pulmonary Diseases Based on International Expert Consensus

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Ultrasound Lung Aeration Map via Physics-Aware Neural Operators.

Jiayun Wang¹, Oleksii Ostras^2,3, Masashi Sode^2,3

¹Department of Computing and Mathematical Sciences, California Institute of Technology, 1200 E California Blvd, Pasadena, 91125, CA, United States.

|January 13, 2025

Summary

This summary is machine-generated.

Luna, an AI model, reconstructs lung aeration maps directly from ultrasound RF data, bypassing traditional imaging. This offers a quantitative, reader-independent method for diagnosing lung diseases, improving accuracy and accessibility.

Keywords:

deep learning lung aeration lung imaging medical imaging neural operator operator learning physics-aware machine learning ultrasound

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Point-of-Care Lung Ultrasound in Adults: Image Acquisition

Point-of-Care Lung Ultrasound in Adults: Image Acquisition

Published on: March 3, 2023

Area of Science:

Medical Imaging
Artificial Intelligence
Pulmonary Medicine

Background:

Lung ultrasound is valuable for diagnosing lung diseases but interpretation is challenging due to image artifacts and reliance on expert readers.
Current methods involve indirect B-mode image interpretation, which requires extensive training and limits widespread adoption.
The inaccessibility of air to ultrasound waves creates complex reverberations, complicating accurate diagnosis.

Purpose of the Study:

To develop an AI model, Luna, for direct reconstruction of lung aeration maps from radio frequency (RF) data.
To overcome the limitations of traditional beamforming and indirect B-mode image interpretation in lung ultrasound.
To provide a quantitative, reader-independent metric for lung aeration assessment.

Main Methods:

Luna utilizes a Fourier neural operator to process RF data efficiently in Fourier space.
The model bypasses traditional beamformers, directly reconstructing lung aeration maps.
Training involved simulated data and fine-tuning with ex vivo swine lung scans.

Main Results:

Luna accurately reconstructs lung aeration maps from RF data.
The AI model achieved a 9% aeration estimation error in ex vivo swine lung scans.
This demonstrates a quantitative, reader-independent alternative to traditional scoring methods.

Conclusions:

Direct reconstruction of lung aeration maps from RF data is feasible with AI.
Luna has the potential to improve the interpretability, reproducibility, and diagnostic utility of lung ultrasound.
This AI-driven approach can democratize lung ultrasound imaging as a reliable diagnostic tool.