Edge-Aware Short-Chain Diffusion Enables High-Fidelity Sparse-Sampling Optoacoustic Tomography

Objective: To improve image quality in multispectral optoacoustic tomography (MSOT) under conditions of strong noise and extreme sparse sampling. Impact Statement: This work provides a practical solution to enhance MSOT image quality while reducing hardware requirements, which may help expand its use in preclinical and clinical imaging. Introduction: MSOT provides useful biochemical and molecular contrast in tissues. However, image quality is often limited by system noise and sparse sampling. Methods: We propose the optoacoustic universal denoising network (OA-UDNet), a hybrid diffusion-based framework for sparse MSOT data. The model is trained on more than 250,000 in vivo images. It performs joint denoising and high-fidelity image restoration by combining an edge-aware module with diffusion-based generation to preserve structural boundaries. Results: With data from only 32 detectors, the method improves image quality and increases peak signal-to-noise ratio (PSNR) by nearly 14 dB. Evaluated against the standard 256-detector reference, the framework consistently preserves structural fidelity under extreme undersampling. Validation on whole-body mouse imaging, tumor models, and human samples shows reduced artifacts and improved recovery of anatomical and functional features. Conclusion: OA-UDNet improves MSOT image quality under sparse conditions. It offers a simple and effective way to accelerate imaging while reducing hardware complexity.