Recovery of Blood Flow From Undersampled Photoacoustic Microscopy Data Using Sparse Modeling.

Abstract

Photoacoustic microscopy (PAM) leverages the optical absorption contrast of blood hemoglobin for high-resolution, multi-parametric imaging of the microvasculature in vivo. However, to quantify the blood flow speed, dense spatial sampling is required to assess blood flow-induced loss of correlation of sequentially acquired A-line signals, resulting in increased laser pulse repetition rate and consequently optical fluence. To address this issue, we have developed a sparse modeling approach for blood flow quantification based on downsampled PAM data. Evaluation of its performance both in vitro and in vivo shows that this sparse modeling method can accurately recover the substantially downsampled data (up to 8 times) for correlation-based blood flow analysis, with a relative error of 12.7 ± 6.1 % across 10 datasets in vitro and 12.7 ± 12.1 % in vivo for data downsampled 8 times. Reconstruction with the proposed method is on par with recovery using compressive sensing, which exhibits an error of 12.0 ± 7.9 % in vitro and 33.86 ± 26.18 % in vivo for data downsampled 8 times. Both methods outperform bicubic interpolation, which shows an error of 15.95 ± 9.85 % in vitro and 110.7 ± 87.1 % in vivo for data downsampled 8 times.