Transient motion classification through turbid volumes via parallelized single-photon detection and deep contrastive embedding

TL;DR

This paper introduces CREPE, a novel parallelized single-photon detection method combined with deep contrastive learning, enabling high-resolution, real-time classification of transient decorrelation events beneath turbid media, such as brain tissue.

Contribution

The work presents a new DCS technique with a 32x32 SPAD array and a deep contrastive learning algorithm, significantly improving spatial localization and classification of deep tissue motion.

Findings

Accurately classifies decorrelation events in 0.1-0.4 seconds

Uses a 32x32 SPAD array for parallel speckle detection

Outperforms classic unsupervised learning methods

Abstract

Fast noninvasive probing of spatially varying decorrelating events, such as cerebral blood flow beneath the human skull, is an essential task in various scientific and clinical settings. One of the primary optical techniques used is diffuse correlation spectroscopy (DCS), whose classical implementation uses a single or few single-photon detectors, resulting in poor spatial localization accuracy and relatively low temporal resolution. Here, we propose a technique termed Classifying Rapid decorrelation Events via Parallelized single photon dEtection (CREPE)}, a new form of DCS that can probe and classify different decorrelating movements hidden underneath turbid volume with high sensitivity using parallelized speckle detection from a $32\times32$ pixel SPAD array. We evaluate our setup by classifying different spatiotemporal-decorrelating patterns hidden beneath a 5mm tissue-like phantom made with rapidly decorrelating dynamic scattering media. Twelve multi-mode fibers are used to collect scattered light from different positions on the surface of the tissue phantom. To validate our setup, we generate perturbed decorrelation patterns by both a digital micromirror device (DMD) modulated at multi-kilo-hertz rates, as well as a vessel phantom containing flowing fluid. Along with a deep contrastive learning algorithm that outperforms classic unsupervised learning methods, we demonstrate our approach can accurately detect and classify different transient decorrelation events (happening in 0.1-0.4s) underneath turbid scattering media, without any data labeling. This has the potential to be applied to noninvasively monitor deep tissue motion patterns, for example identifying normal or abnormal cerebral blood flow events, at multi-Hertz rates within a compact and static detection probe.