Beneath the surface: revealing deep-tissue blood flow in human subjects with massively parallelized diffuse correlation spectroscopy.

Lucas Kreiss¹

Melissa Wu¹

Michael Wayne²

Shiqi Xu¹

Paul McKee¹

Derrick Dwamena¹

Kanghyun Kim¹

Kyung Chul Lee³

Kyle R. Cowdrick^4,5

Wenhui Liu⁶

Arin Ülkü²

Mark Harfouche⁷

Xi Yang¹

Clare B Cook¹

Seung Ah Lee⁸

Erin Buckley^4,5

Claudio Bruschini²

Edoardo Charbon²

Scott Huettel¹

Roarke Horstmeyer^1,6

Neurophotonics (2025)

¹Duke University, Durham NC, USA., ²EPFL (Switzerland) , ³Seoul National University (Korea, Republic of) , ⁴Georgia Institute of Technology (United States) , ⁵Emory University (United States), ⁶Tsinghua University (China), ⁷Ramona Optics, Inc. (United States) , ⁸Seoul National University (Republic of Korea)

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Fig 1 — Fig.: Basic principle and experimental setup of parallelized diffuse correlation spectroscopy (PDCS). A coherent laser source connected to an optical fiber delivers light to the area under investigation, e.g., the forehead for measurement of cerebral blood flow (CBF). Photons emitted by the source fiber (S) scatter diffusely within tissue and a small fraction reach the detection fiber (D). A “banana” shaped probability distribution describes the average optical path between S and D. Photons at D are transmitted to a single-photon avalanche diode (SPAD) array. A short source detector separation (SDS) predominately measures photons scattered in superficial tissue layers (blue). A larger SDS detects more photons that have traveled through deeper tissue layers (orange). Each pixel in the SPAD array measures the temporal dynamics of a single speckle and thereby performs an independent DCS measurement. These individual intensity time traces are then used to compute autocorrelation curves, whose slope (i.e., decorrelation rate) are proportional to blood flow speed within the tissue. PDCS averages autocorrelations across thousands of SPAD array pixels to increase system signal-to-noise ratio.

Abstract

Diffuse correlation spectroscopy (DCS) allows label-free, non-invasive investigation of microvascular dynamics deep within tissue, such as cerebral blood flow (CBF). However, the signal-to-noise ratio (SNR) in DCS limits its effective cerebral sensitivity in adults, in which the depth to the brain, through the scalp and skull, is substantially larger than in infants. Therefore, we aim to increase its SNR and, ultimately, its sensitivity to CBF through new DCS techniques. We present an in vivo demonstration of parallelized DCS (PDCS) to measure cerebral and muscular blood flow in healthy adults. Our setup employs an innovative array with hundreds of thousands single photon avalanche diodes (SPAD) in a 500×500 grid to boost SNR by averaging all independent pixel measurements. We tested this device on different total pixel counts and frame rates. A secondary, smaller array was used for reference measurements from shallower tissue at lower source-detector-separation (SDS). The new system can measure pulsatile blood flow in cerebral and muscular tissue, at up to 4 cm SDS, while maintaining a similar measurement noise as compared with a previously published 32×32 PDCS system at 1.5 cm SDS. Data from a cohort of 15 adults provide strong experimental evidence for functional CBF activity during a cognitive memory task and allowed analysis of pulse markers. Additional control experiments on muscular blood flow in the forearm with a different technical configuration provide converging evidence for the efficacy of this technique. Our results outline successful PDCS measurements with large SPAD arrays to enable detect CBF in human adults. The ongoing development of SPAD camera technology is expected to result in larger and faster detectors in the future. In combination with new data processing techniques, tailored for the sparse signal of binary photon detection events in SPADs, this could lead to even greater SNR increase and ultimately greater depth sensitivity of PDCS.