Virtual compressed sensing photoacoustic imaging using CoSaMP algorithm based on k-wave

Meijun Sun, Aojie Zhao, Bo Li, Jinhong Zhang, Qiming He, Xianlin Song
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引用次数: 1

Abstract

In recent years, photoacoustic imaging technology has developed rapidly and has become one of the most important technologies in the field of biomedical imaging. Photoacoustic imaging combines the characteristics of high contrast of optical imaging and strong penetrating power of acoustic imaging. It can obtain tissue imaging with high resolution and can also meet the requirements of quantitative analysis of changes in tissue function and physiological parameters at the same time. So, photoacoustic imaging plays an important role in disease prevention and cancer diagnosis. The traditional information acquisition of photoacoustic imaging is based on Nyquist sampling law (the sampling frequency must be greater than twice the highest signal frequency). This method will waste a lot of sampling resources in photoacoustic imaging with a large amount of data and put forward higher requirements for equipment. In order to break through the limitation of Nyquist sampling law, compressed sensing theory is used to compress and sample the signal. Then the original photoacoustic image is reconstructed by sparse key data. In this paper, Compressive Sampling Matching Pursuit (CoSaMP) is used as the reconstruction algorithm. And the compressed sensing photoacoustic imaging platform is built by K-Wave toolbox (photoacoustic imaging platform tool) of MATLAB simulation software together with the reconstruction algorithm to reconstruct the sparse photoacoustic signals observed. The qualitative and quantitative analysis is carried out on the reconstructed images. Results shows that the reconstruction effect meets the requirements, which verifies the superiority of compressed sensing theory and the reliability and advancement of compressed sensing photoacoustic imaging platform.
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基于k波的CoSaMP算法的虚拟压缩传感光声成像
近年来,光声成像技术发展迅速,已成为生物医学成像领域的重要技术之一。光声成像结合了光学成像的高对比度和声成像的强穿透能力的特点。它可以获得高分辨率的组织成像,同时也可以满足定量分析组织功能和生理参数变化的要求。因此,光声成像在疾病预防和癌症诊断中具有重要作用。传统的光声成像信息采集基于奈奎斯特采样定律(采样频率必须大于信号最高频率的两倍)。这种方法在数据量大的光声成像中会浪费大量的采样资源,并且对设备提出了更高的要求。为了突破奈奎斯特采样定律的局限性,采用压缩感知理论对信号进行压缩采样。然后利用稀疏关键数据重构原始光声图像。本文采用压缩采样匹配追踪(CoSaMP)作为重构算法。利用MATLAB仿真软件中的K-Wave工具箱(光声成像平台工具)构建压缩感知光声成像平台,结合重构算法对观测到的稀疏光声信号进行重构。对重建图像进行定性和定量分析。结果表明,重建效果满足要求,验证了压缩感知理论的优越性和压缩感知光声成像平台的可靠性和先进性。
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