标题： 基于物理感知神经算子的超声肺部通气图 显示英文标题

标题： Ultrasound Lung Aeration Map via Physics-Aware Neural Operators

摘要： 肺部超声在临床上作为一种诊断和监测急性和慢性肺部疾病的方法正在迅速发展，这是由于其低成本和易于获取。 肺部超声通过发射诊断脉冲，接收压力波并将其转换为射频（RF）数据，然后通过波束成形器将这些数据处理成B模式图像，供放射科医生进行解读。 然而，与用于软组织解剖成像的传统超声不同，肺部超声的解读因无法穿透空气而在胸膜界面产生复杂的混响而变得复杂。 间接的B模式图像使得解读高度依赖于读者的专业知识，需要多年的培训，这限制了其广泛应用，尽管在熟练人员手中具有高准确性潜力。 为了解决这些挑战，并将肺部超声成像民主化为一种可靠的诊断工具，我们提出了LUNA，这是一种直接从RF数据重建肺部通气图的AI模型，绕过了传统波束成形器和B模式图像的间接解读需求。 LUNA使用了一种傅里叶神经算子，该算子在傅里叶空间中高效处理RF数据，从而实现肺部通气图的准确重建。 LUNA为传统的半定量肺部超声评分方法提供了一种定量、与读者无关的替代方案。 LUNA的发展涉及合成数据和真实数据：我们使用经过实验验证的方法模拟合成数据，并扫描离体猪肺作为真实数据。 LUNA在大量模拟数据上进行训练，并用少量真实世界数据进行微调，实现了稳健的性能，在离体肺部扫描中的通气估计误差为9%。 我们展示了从RF数据重建肺部通气图的潜力，为提高肺部超声的可重复性和诊断效用奠定了基础。 显示英文摘要

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, 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.