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双压缩光声单像素成像。

Dual-compressed photoacoustic single-pixel imaging.

作者信息

Guo Yuning, Li Baowen, Yin Xiaobo

机构信息

Department of Mechanical Engineering, University of Colorado, Boulder, CO80309, USA.

Department of Material Science and Engineering, Department of Physics, Shenzhen Institute for Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen518055, China.

出版信息

Natl Sci Rev. 2022 Mar 25;10(1):nwac058. doi: 10.1093/nsr/nwac058. eCollection 2023 Jan.

Abstract

Photoacoustic imaging, an acoustic imaging modality with potentially optical resolution in an optical turbid medium, has attracted great attention. However, the convergence of wavefront optimization and raster scanning in computational photoacoustic imaging leads to the challenge of fast mapping, especially for a spatial resolution approaching the acoustic deep-subwavelength regime. As a sparse sampling paradigm, compressive sensing has been applied in numerous fields to accelerate data acquisition without significant quality losses. In this work, we propose a dual-compressed approach for photoacoustic surface tomography that enables high-efficiency imaging with 3D spatial resolution unlimited by the acoustics in a turbid environment. The dual-compressed photoacoustic imaging with single-pixel detection, enabled by spatially optical modulation with synchronized temporally photoacoustic coding, allows decoding of the fine optical information from the modulated acoustic signal even when the variance of original photoacoustic signals is weak. We perform a proof-of-principle numerical demonstration of dual-compressed photoacoustic imaging, that resolves acoustic sub-acoustic-wavelength details with a significantly reduced number of measurements, revealing the potential for dynamic imaging. The dual-compressed concept, which transforms unobtrusive spatial difference into spatio-temporal detectable information, can be generalized to other imaging modalities to realize efficient, high-spatial-resolution imaging.

摘要

光声成像作为一种在光学混浊介质中具有潜在光学分辨率的声学成像模态,已引起了广泛关注。然而,计算光声成像中波前优化和光栅扫描的结合导致了快速映射的挑战,特别是对于接近声学深亚波长范围的空间分辨率而言。作为一种稀疏采样范式,压缩感知已被应用于众多领域,以在不显著损失质量的情况下加速数据采集。在这项工作中,我们提出了一种用于光声表面层析成像的双压缩方法,该方法能够在混浊环境中实现不受声学限制的具有三维空间分辨率的高效成像。通过空间光调制与同步时间光声编码实现的单像素检测双压缩光声成像,即使在原始光声信号的方差较弱时,也能从调制后的声学信号中解码出精细的光学信息。我们对双压缩光声成像进行了原理验证的数值演示,该演示以显著减少的测量次数解析了声学亚波长细节,揭示了动态成像的潜力。双压缩概念将不显眼的空间差异转化为时空可检测信息,可推广到其他成像模态,以实现高效、高空间分辨率成像。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45f/9923385/a63571cdffd0/nwac058fig1.jpg

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