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Related Experiment Video

Updated: Sep 3, 2025

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells
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Transient Motion Classification Through Turbid Volumes via Parallelized Single-Photon Detection and Deep Contrastive

Shiqi Xu1, Wenhui Liu1,2, Xi Yang1

  • 1Department of Biomedical Engineering, Duke University, Durham, NC, United States.

Frontiers in Neuroscience
|July 25, 2022
PubMed
Summary
This summary is machine-generated.

We introduce CREPE, a novel diffuse correlation spectroscopy technique using parallelized single-photon detection. This method accurately classifies deep tissue motion, like cerebral blood flow, without data labeling.

Keywords:
SPAD arraycontrastive learningdiffuse correlationmultimode fiberneurobehaviorself-supervised learningzero-shot learning

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Area of Science:

  • Biomedical Optics
  • Photonics
  • Medical Imaging

Background:

  • Noninvasive monitoring of deep tissue dynamics, such as cerebral blood flow, is crucial for clinical diagnostics.
  • Classical diffuse correlation spectroscopy (DCS) has limitations in spatial localization and temporal resolution.
  • Advanced optical techniques are needed to overcome these challenges for sensitive detection of decorrelating events.

Purpose of the Study:

  • To develop and validate a new DCS technique, Classifying Rapid Decorrelation Events via Parallelized Single Photon Detection (CREPE), for high-sensitivity detection and classification of subsurface decorrelating movements.
  • To enhance spatial localization and temporal resolution in probing dynamic scattering media.
  • To enable non-label-free classification of transient decorrelation events in turbid media.

Main Methods:

  • Utilized a 32x32 pixel SPAD array for parallelized speckle detection.
  • Employed a tissue-like phantom with dynamic scattering media to simulate subsurface events.
  • Generated decorrelation patterns using a digital micromirror device and a fluidic vessel phantom.
  • Applied a deep contrastive learning algorithm for unsupervised classification of decorrelation patterns.

Main Results:

  • Demonstrated accurate detection and classification of transient decorrelation events (0.1-0.4 s) in turbid media.
  • Achieved high sensitivity in probing spatially varying decorrelating events.
  • The deep contrastive learning approach outperformed traditional unsupervised methods.
  • Successfully classified decorrelation patterns without requiring labeled data.

Conclusions:

  • CREPE offers a sensitive and accurate method for noninvasive probing and classification of deep tissue motion.
  • The technique shows potential for real-time monitoring of physiological events, such as cerebral blood flow.
  • This parallelized single-photon detection approach advances DCS capabilities for biomedical applications.