Fast-detection photoacoustic tomography platform based on GPSR compressed sensing

Abstract

Photoacoustic imaging is a new biomedical imaging technology developed in recent years. It has the advantages of high resolution and sensitivity to the functional characteristics of biological tissues. However, whether for photoacoustic tomography or photoacoustic microscopy imaging, the imaging resolution depends on the frequency and bandwidth of the received ultrasonic signal. This leads to a large amount of data being collected under the Nyquist’s law. So storage medium and DSP processor are under unprecedented pressure. The problem of large amount of data is usually solved by using compression. Therefore, the combination of compressed sensing theory and photoacoustic imaging can not only restore images with high quality, but also reduce the amount of data as much as possible. It saves storage space and the time of further data processing. This paper introduces signal sparsity and measurement matrix of compressed sensing theory briefly. The virtual photoacoustic imaging of blood vessels is carried out in the simulation environment constructed by the k-wave toolbox of MATLAB. The collected data are restored by using the gradient projection for sparse reconstruction. The results show that high quality photoacoustic imaging images can be reconstructed while a small amount of data is stored. The performance of the simulation platform is verified. And it is of great significance to solve the problem of high data volume of photoacoustic imaging.