Parallel Computing for Quantitative Blood Flow Imaging in Photoacoustic Microscopy

Abstract

Photoacoustic microscopy (PAM) is an emerging biomedical imaging technology capable of quantitative measurement of the microvascular blood flow by correlation analysis. However, the computational cost is high, limiting its applications. Here, we report a parallel computation design based on graphics processing unit (GPU) for high-speed quantification of blood flow in PAM. Two strategies were utilized to improve the computational efficiency. First, the correlation method in the algorithm was optimized to avoid redundant computation and a parallel computing structure was designed. Second, the parallel design was realized on GPU and optimized by maximizing the utilization of computing resource in GPU. The detailed timings and speedup for each calculation step were given and the MATLAB and C/C++ code versions based on CPU were presented as a comparison. Full performance test shows that a stable speedup of ~80-fold could be achieved with the same calculation accuracy and the computation time could be reduced from minutes to just several seconds with the imaging size ranging from 1 × 1 mm2 to 2 × 2 mm2. Our design accelerates PAM-based blood flow measurement and paves the way for real-time PAM imaging and processing by significantly improving the computational efficiency.