Conference 11772 > Paper 11772-72
Paper 11772-72

High-speed sampling strategy for photoacoustic tomography using ROMP compressed sensing algorithm

Abstract

Photoacoustic tomography technology is a new imaging technology based on photoacoustic effect. It has become one of the most important techniques in biomedical imaging field because of its non-invasive, non-ionized and high resolution. The imaging data of photoacoustic imaging is complex. The traditional Nyquist sampling consumes much time and resources. And it requires high equipment. In order to improve the sampling efficiency and reduce the equipment requirements, Compression Sensing (CS) theory has been used to collect photoacoustic data. Compressed Sensing theory can break through the limitation of Nyquist sampling law and reduce the data redundancy greatly so that the desired imaging results can be reconstructed with less time and resources. In this paper, the K-wave simulation toolbox of MATLAB is used to set up the virtual photoacoustic field and collect the photoacoustic signal of blood vessel. The results show that the MATLAB virtual Compressed Sensing photoacoustic tomography simulation platform based on k-wave can achieve high quality photoacoustic tomography with less data. The superiority of Compressed Sensing theory and the high efficiency and stability of k-wave virtual platform are verified. Also, the Compressed Sensing reconstruction algorithm OMP and ROMP are compared in this paper and the result shows that the ROMP algorithm works better.

Presenter

Nanchang Univ. (China)
I 'm from school of Information Engineering, Nanchang University, China. My major is Information and Communication Engineering. My research interests is compressed sensing photoacoustic tomography.
Presenter/Author
Nanchang Univ. (China)
Author
Nanchang Univ. (China)
Author
Nanchang Univ. (China)
Author
Meijun Sun
Nanchang Univ. (China)
Author
Nanchang Univ. (China)
Author
Nanchang Univ. (China)