OpenWaves: A Large-Scale Anatomically Realistic Ultrasound-CT Dataset for Benchmarking Neural Wave Equation Solvers

Zhijun Zeng, Youjia Zheng, Hao Hu, Zeyuan Dong, Yihang Zheng, Xinliang Liu, Jinzhuo Wang, Zuoqiang Shi, Linfeng Zhang, Yubing Li, He Sun

Keywords: Computational imaging, Inverse problem, Neural operators, Ultrasound Computed Tomography, Full Waveform Inversion

Abstract: Accurate and efficient simulation of wave equations is crucial in computational physics, especially for wave imaging applications like ultrasound computed tomography (USCT), which reconstructs tissue properties from scattered waves. Traditional numerical solvers for wave equations are computationally intensive and often unstable, limiting their practical applications for quasi-real-time imaging. Neural operators offer an innovative approach by accelerating PDE solving using neural networks; however, their effectiveness in realistic imaging is constrained by existing datasets that oversimplify real-world complexity. In this paper, we present OpenWaves, a large-scale wave equation dataset designed to bridge the gap between theoretical equations and practical imaging applications. OpenWaves provides over 16 million frequency-domain wave simulations using real USCT configurations, featuring anatomically realistic human breast phantoms across four categories. It enables comprehensive benchmarking of popular neural operators for both forward simulation and inverse imaging tasks, allowing analysis of their performance, scalability, and generalization capabilities. By offering a realistic and extensive dataset, OpenWaves not only serves as a platform for developing innovative neural PDE solvers but also facilitates their deployment in real-world medical imaging problems.

Primary Area: datasets and benchmarks

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Submission Number: 6229