SADiff: Structure-aware diffusion model for enhancing prostate multiphoton microscopy imaging

Multiphoton Microscopy (MPM) has become an essential technique in bioimaging, particularly for studying thick tissues and live animals, offering deep tissue penetration, high-resolution imaging, and minimizing photodamage and photobleaching. Nevertheless, MPM comes with performance trade-offs related to imaging quality, acquisition speed, and sample health in practice. Achieving high-resolution imaging with optimal signal-to-noise ratio (SNR) in a timely manner while minimizing potential sample damage remains challenging, especially when the goal is to provide pathologically relevant diagnostic information in clinical settings. In this paper, we propose a structure-aware diffusion model (SADiff), specifically designed to enhance denoising and super-resolution in prostate MPM imaging. SADiff innovatively incorporates high-frequency residuals into the diffusion process, a strategy that significantly improves the model’s ability to capture and preserve fine structural details, such as glandular morphology, that are crucial for accurate prostate cancer diagnosis, without introducing additional trainable parameters. Moreover, a novel symmetric tanh-based schedule is designed to effectively control the integration of high-frequency residuals, ensuring optimal image quality. Extensive experiments conducted on clinical prostate cancer MPM image datasets demonstrate that SADiff significantly outperforms state-of-the-art methods in both qualitative and quantitative evaluations. By transforming low-resolution, low-SNR images into high-resolution, high-SNR images, SADiff provides a robust solution for MPM imaging, enhancing diagnostic accuracy and potentially leading to improved patient outcomes.