Abstract
Photoacoustic microscopy (PAM) enables label-free, quantitative imaging of blood flow and oxygenation in vivo, offering critical insights into microvascular function and tissue metabolism. However, current flow quantification methods suffer from poor accuracy at extreme flow speeds and high computational costs. We present Hybrid Fourier-Derivative Analysis (HFDA), a new method based on frequency analysis of flow-induced modulations in photoacoustic amplitude. Compatible with standard raster scanning, HFDA adaptively integrates Fourier analysis for high-speed flow and derivative analysis for low-speed flow, achieving high accuracy and computational efficiency. Phantom studies validate the accuracy of HFDA across 0.2-20 mm/s, with errors typically less than 7 %. Compared to correlation-based methods, HFDA reduces computational time by 35-fold. In vivo demonstrations in mouse models of hypoxia and hypercapnia further underscore the potential of HFDA as a rapid and precise tool for blood flow quantification in functional and metabolic PAM studies.