GPU-accelerated volumetric-mosaic optical-resolution photoacoustic microscopy and quantifying tumor vasculature growth

Tumor growth is closely linked to vascular remodeling, yet comprehensive volumetric imaging of tumor vasculature using photoacoustic microscopy (PAM) remains challenging due to limitations in the field of view, depth penetration, and processing speed. Herein, we present hybrid scanning-based optical-resolution PAM integrated with a GPU-accelerated 3D mosaic and quantification framework for label-free high-resolution monitoring of tumor angiogenesis. Our system employs an optimized mosaic-matching method to achieve large volumetric FOVs (up to 10 × 10 × 2.5 mm³) and supports full 3D reconstruction. In addition, GPU-based parallel processing was applied to enable rapid 3D quantification of vasculature in terms of vessel diameter, density, and branching complexity. The enhanced GPU-based computational framework accelerated the 3D mosaicking and quantification analysis by approximately twofold relative to CPU-based processing. Longitudinal monitoring in a nude-mouse 4T1 breast tumor model over 11 days revealed progressive vascular remodeling and angiogenesis during tumor progression. Our approach overcomes the existing constraints on using PAM by combining hardware-efficient hybrid scanning with GPU-accelerated 3D mosaicking and vasculature quantification. This provides a powerful tool for <i>in vivo</i> tumor vasculature imaging and quantitative analysis, thereby advancing cancer diagnosis and clinical treatment process in future.