Fast high-quality sparse reconstruction of photoacoustic imaging based on HTP compressed sensing

被引:0
|
作者
Tang, Jiaqi [1 ]
Zhao, Aojie [1 ]
Li, Bo [1 ]
Song, Xianlin [1 ]
机构
[1] Nanchang Univ, Sch Informat Engn, Nanchang 330031, Jiangxi, Peoples R China
来源
NOVEL OPTICAL SYSTEMS, METHODS, AND APPLICATIONS XXIV | 2021年 / 11815卷
关键词
photoacoustic tomography; K-wave simulation toolbox; compressed sensing; reconstruction algorithm; hard thresholding pursuit;
D O I
10.1117/12.2592082
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
As a new non-destructive medical imaging technology, photoacoustic imaging combines the advantages of optical imaging and ultrasonic imaging, which has the characteristics of high contrast and strong penetration ability. It can effectively image biological tissues and functions, and be applied to the early diagnosis and treatment of tumors, cardiovascular and cerebrovascular diseases, which has broad prospects for development in the field of biomedicine. When photoacoustic imaging is used to collect a large number of pathological medical images, it is prone to slow data transmission and poor reconstruction effect. In an effort to speed up data transmission and improve image reconstruction quality, this article is based on photoacoustic imaging technology and compressed sensing reconstruction algorithm, a virtual simulation platform of photoacoustic tomography combined with compressive sensing is built by using K-wave simulation toolbox to simulate the propagation process of photoacoustic signals, and the hard thresholding pursuit algorithm is used to complete the signal reconstruction. In order to verify the performance of the virtual simulation platform, in this paper, the local vascular network map is compressed and reconstructed. The obtained image retains the main information in the original image, and the edge features are similar. The results show that the virtual simulation platform can reconstruct high quality images by a small amount of data, which provides important significance and theoretical research value for compressed sensing reconstruction algorithm applied in photoacoustic imaging.
引用
收藏
页数:5
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