Dual-compressed photoacoustic single-pixel imaging

Author:

Guo Yuning1ORCID,Li Baowen12,Yin Xiaobo13

Affiliation:

1. Department of Mechanical Engineering, University of Colorado , Boulder , CO 80309 , USA

2. Department of Material Science and Engineering, Department of Physics, Shenzhen Institute for Quantum Science and Engineering, Southern University of Science and Technology , Shenzhen 518055 , China

3. Materials Science and Engineering Program, University of Colorado , Boulder , CO 80309 , USA

Abstract

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.

Publisher

Oxford University Press (OUP)

Subject

Multidisciplinary

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