Imaging decorrelation via deep learning and SPAD array detection

Shiqi Xu; Xi Yang; Wenhui Liu; Ruobing Qian; Pavan Chandra Konda; Roarke W. Horstmeyer

doi:10.1117/12.2578644

5 March 2021 Imaging decorrelation via deep learning and SPAD array detection

Shiqi Xu, Xi Yang, Wenhui Liu, Ruobing Qian, Pavan Chandra Konda, Roarke W. Horstmeyer

Proceedings Volume 11629, Optical Techniques in Neurosurgery, Neurophotonics, and Optogenetics; 116292A (2021) https://doi.org/10.1117/12.2578644
Event: SPIE BiOS, 2021, Online Only

Abstract

We propose a new sensitive diffuse correlation spectroscopy(DCS) method that can probe and identify different decorrelation events happens in sub-second, by acquiring parallelized measurements from 12 fiber detectors placed at different positions on the tissue-phantom surface with a 32 ×32 SPAD array, and process the data with deep learning methods. Both experimental and simulation phantom studies are conducted to evaluate the performance of our system in classifying and imaging decorrelation patterns presented under a 5mm thick tissue phantom made with rapidly decorrelating scattering media.

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Citation Download Citation

Shiqi Xu, Xi Yang, Wenhui Liu, Ruobing Qian, Pavan Chandra Konda, and Roarke W. Horstmeyer "Imaging decorrelation via deep learning and SPAD array detection", Proc. SPIE 11629, Optical Techniques in Neurosurgery, Neurophotonics, and Optogenetics, 116292A (5 March 2021); https://doi.org/10.1117/12.2578644

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KEYWORDS

Detector arrays

Digital micromirror devices

Tissues

Monte Carlo methods

Natural surfaces

Scattering media

Sensors

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