Compressed sensing photoacoustic tomography using Stagewise Weak OMP algorithm based on k-wave: a simulation study

Abstract

Photoacoustic tomography technology is a new non-invasive, non-ionizing biomedical imaging method. This technology combines the high contrast of optical imaging and the high-resolution characteristics of ultrasound imaging, which can obtain high-resolution images in deeper tissues. In recent years, it has developed rapidly and won widespread attention. Traditional sampling method must follow the Nyquist sampling theorem, which wastes a lot of sensing time and storage space. In order to improve the sampling efficiency, compressed sensing (CS) theory is used to collect and process photoacoustic data. The advantage of CS theory is that it can combine data acquisition and data compression. So that only the sparse features of the original signal need to be collected, and a high-quality original target image can be successfully reconstructed with very few samples, which greatly reduces data redundancy. More than that, the requirements for equipment are reduced. This paper uses MATLAB's k-wave simulation toolbox to establish a virtual photoacoustic field, collect the photoacoustic signals of biological tissues, and reconstruct the image through the segmented weak orthogonal matching pursuit (StOMP) algorithm. The results show that the MATLAB virtual compressed sensing photoacoustic tomography simulation platform based on k-wave can realize high-quality photoacoustic tomography with less data. The superiority of the compressed sensing theory and the efficiency of the k-wave virtual platform are verified.