Deep Tissue Coherent Imaging Using Speckle Intensity Correlations Over Object Position

Luo, Qiaoen; Webb, Kevin J.

doi:10.1364/cleo_si.2017.sm3c.5

Cited by 1 publication

(2 citation statements)

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“…This is exactly the goal of the classical phase retrieval problem [14]. While SCI traditionally employs a Fienup-type hybrid input-output (HIO) algorithm to solve the phase retrieval problem [8,9,13,14], we propose the use of the ADMM, as it has been found to outperform HIO and other standard algorithms (16) and can easily incorporate priors. The goal is to recover x 2 ℝ N , a discretized and vectorized estimate of the target object O, from m 2 ℝ N , the discretized and vectorized estimate of jÔ ω ð Þj.…”

Section: Image Reconstructionmentioning

confidence: 99%

“…Furthermore, scattering decorrelation over time can benefit this technique by allowing for averaging over multiple independent speckle patterns. Numerous follow-up SCI studies are motivated by the potential of fluorescence imaging within thick tissue but, to our knowledge, have only been demonstrated with scattered laser illumination and in geometries where the scatterer is in the far field relative to the target object [9,10]. Spurred by new insights into speckle correlations [11,12], we set out to adapt SCI to fluorescence microscopy within scattering tissue.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Single‐shot speckle correlation fluorescence microscopy in thick scattering tissue with image reconstruction priors

Chang

Wetzstein

2018

Journal of Biophotonics

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Deep tissue imaging in the multiple scattering regime remains at the frontier of fluorescence microscopy. Speckle correlation imaging (SCI) can computationally uncover objects hidden behind a scattering layer, but has only been demonstrated with scattered laser illumination and in geometries where the scatterer is in the far field of the target object. Here, SCI is extended to imaging a planar fluorescent signal at the back surface of a 500-μm-thick slice of mouse brain. The object is reconstructed from a single snapshot through phase retrieval using a proximal algorithm that easily incorporates image priors. Simulations and experiments demonstrate improved image recovery with this approach compared to the conventional SCI algorithm.

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Section: Image Reconstructionmentioning

confidence: 99%