光学学报, 2018, 38 (11): 1117002, 网络出版: 2019-05-09   

基于字典学习的稀疏角度采样光声信号重建 下载: 873次

Reconstruction for Sparse-View Sampling Photoacoustic Signals Based on Dictionary Learning
作者单位
南开大学电子信息与光学工程学院现代光学研究所, 天津 300350
摘要
光声成像兼具光学成像的高对比度和超声成像对深层组织的高分辨率等优点,在生物医学成像领域具有巨大的潜力,而且发展十分迅速;光声成像通过在多个角度进行光声信号的采集,可以获得生物组织的二维或三维光学吸收分布图像;但实际的光声成像往往因硬件条件和成像时间的制约而难以采集角度足够多的光声信号;在信号采样不足的情况下,光声图像的重建质量会严重下降,出现大量伪迹。针对该问题,提出了一种基于字典学习与稀疏表示的恢复重建算法,采用该算法对光声信号进行预处理,并进行仿真实验。结果表明:与不经过光声信号超分辨率重建的时间反演法图像重建结果相比,经所提算法处理后的光声重建图像的伪迹显著减少,细节更加清晰,峰值信噪比提高了8 dB左右;不同信噪比下的仿真实验验证了所提出算法具有良好的稳健性。
Abstract
Photoacoustic imaging has great potential in the field of biomedical imaging because it is a beneficial combination of the high contrast of pure optical imaging and the high resolution of ultrasound imaging for deep-tissue. By acquiring the photoacoustic signals at multiple locations, we can obtain a two-dimensional or three-dimensional optical absorption distribution image of the biological tissue. However, it is difficult for actual photoacoustic imaging to acquire the photoacoustic signals with enough detector locations due to the constraints of hardware conditions and imaging time. In the case of insufficient signal sampling, the reconstruction quality of the photoacoustic image is seriously degraded, and a large number of artifacts appear consequently. To overcome this problem, we propose a reconstruction strategy which uses photoacoustic signals preprocessed by a recovered algorithm based on dictionary learning and sparse representation, and simulation experiments are carried out. The results show that by applying the proposed algorithm, a photoacoustic image can be reconstructed with less artifacts, clearer details and 8 dB peak signal-to-noise ratio improvement compared with images reconstructed without super-resolution reconstruction of photoacoustic signals. The simulation experiments with different signal-to-noise ratios verify that the proposed algorithm has good robustness.

黄凯, 陈平, 刘伟伟, 林列. 基于字典学习的稀疏角度采样光声信号重建[J]. 光学学报, 2018, 38(11): 1117002. Kai Huang, Ping Chen, Weiwei Liu, Lie Lin. Reconstruction for Sparse-View Sampling Photoacoustic Signals Based on Dictionary Learning[J]. Acta Optica Sinica, 2018, 38(11): 1117002.

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