Spectroscopic photoacoustic (sPA) imaging uses multiple wavelengths to differentiate chromophores based on their unique optical absorption spectra. This technique has been widely applied in areas such as vascular mapping, tumor detection, and therapeutic monitoring. However, sPA imaging is highly susceptible to noise, leading to poor signal-to-noise ratio (SNR) and compromised image quality. Traditional denoising techniques like frame averaging, though effective in improving SNR, can be impractical for dynamic imaging scenarios due to reduced frame rates. Advanced methods, including learning-based approaches and analytical algorithms, have demonstrated promise but often require extensive training data and parameter tuning, limiting their adaptability for real-time clinical use. In this work, we propose a sPA denoising using a tuning-free analytical and data-free enhancement (SPADE) framework for denoising sPA images. This framework integrates a data-free learning-based method with an efficient BM3D-based analytical approach while preserves spectral linearity, providing noise reduction and ensuring that functional information is maintained. The SPADE framework was validated through simulation, phantom, ex vivo, and in vivo experiments. Results demonstrated that SPADE improved SNR and preserved spectral information, outperforming conventional methods, especially in challenging imaging conditions. SPADE presents a promising solution for enhancing sPA imaging quality in clinical applications where noise reduction and spectral preservation are critical.

翻译：光谱光声（sPA）成像利用多波长技术，依据发色团独特的光学吸收光谱对其进行区分。该技术已广泛应用于血管成像、肿瘤检测与治疗监测等领域。然而，sPA成像极易受噪声干扰，导致信噪比（SNR）降低及图像质量下降。传统去噪技术（如帧平均法）虽能有效提升信噪比，但因会降低帧率，在动态成像场景中往往难以实际应用。基于学习的方法与解析算法等先进技术虽展现出潜力，但通常需要大量训练数据与参数调优，限制了其在实时临床场景中的适应性。本研究提出一种基于免调优解析与无数据增强（SPADE）框架的sPA图像去噪方法。该框架将无数据学习方法与高效的BM3D解析算法相结合，在保持光谱线性的同时实现噪声抑制，确保功能信息得以保留。通过仿真、仿体、离体及在体实验对SPADE框架进行了验证。结果表明，SPADE在提升信噪比与保持光谱信息方面均优于传统方法，尤其在具有挑战性的成像条件下表现突出。SPADE为临床应用中需兼顾噪声抑制与光谱保真的sPA成像质量提升提供了前景广阔的解决方案。