Dynamic Reconstruction of Ultrasound-Derived Flow Fields With Physics-Informed Neural Fields

TL;DR

This paper introduces a physics-informed neural field model with multi-scale Fourier features to accurately reconstruct blood flow fields from sparse, noisy ultrasound data, improving diagnostic insights while overcoming ultrasound imaging limitations.

Contribution

The work adapts physics-informed neural fields with Fourier features for ultrasound blood flow reconstruction, achieving low error without ground truth supervision.

Findings

Consistently low mean squared error in denoising and inpainting

Effective on both synthetic and real datasets

Verified against reference flow fields and ground truth measurements

Abstract

Blood flow is sensitive to disease and provides insight into cardiac function, making flow field analysis valuable for diagnosis. However, while safer than radiation-based imaging and more suitable for patients with medical implants, ultrasound suffers from attenuation with depth, limiting the quality of the image. Despite advances in echocardiographic particle image velocimetry (EchoPIV), accurately measuring blood velocity remains challenging due to the technique's limitations and the complexity of blood flow dynamics. Physics-informed machine learning can enhance accuracy and robustness, particularly in scenarios where noisy or incomplete data challenge purely data-driven approaches. We present a physics-informed neural field model with multi-scale Fourier Feature encoding for estimating blood flow from sparse and noisy ultrasound data without requiring ground truth supervision. We demonstrate that this model achieves consistently low mean squared error in denoising and inpainting both synthetic and real datasets, verified against reference flow fields and ground truth flow rate measurements. While physics-informed neural fields have been widely used to reconstruct medical images, applications to medical flow reconstruction are mostly prominent in Flow MRI. In this work, we adapt methods that have proven effective in other imaging modalities to address the specific challenge of ultrasound-based flow reconstruction.