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

Blood flow is sensitive to disease and provides insight into cardiac function, making flow field analysis valuable for diagnosis. However, the attenuation of ultrasound signals makes obtaining accurate flow fields challenging, often resulting in noisy or incomplete data. We present a physics-informed neural field model with multi-scale Fourier Feature encoding for estimating blood flow from 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, with instance-based learning used for adaptation to individual patient data.