Photoacoustic Microscopy

Photoacoustic microscopy (PAM) combines optical and acoustic imaging to achieve high-resolution images with improved tissue penetration, primarily used for visualizing blood vessels and detecting cancerous cells. Current research emphasizes accelerating PAM imaging speed through computational methods like diffusion models and improving image quality via deep learning-based denoising and super-resolution techniques, often employing neural networks such as U-Nets and Transformers. These advancements are crucial for enhancing the clinical utility of PAM in applications like early cancer detection and improved biomedical imaging, particularly by addressing challenges related to noise reduction and scan time.