Publication

High-sensitivity multispeckle diffuse correlation spectroscopy

Neurophotonics


Abstract

Significance: Cerebral blood flow is an important biomarker of brain health and function as it regulates the delivery of oxygen and substrates to tissue and the removal of metabolic waste products. Moreover, blood flow changes in specific areas of the brain are correlated with neuronal activity in those areas. Diffuse correlation spectroscopy (DCS) is a promising noninvasive optical technique for monitoring cerebral blood flow and for measuring cortex functional activation tasks. However, the current state-of-the-art DCS adoption is hindered by a trade-off between sensitivity to the cortex and signal-to-noise ratio (SNR).

Aim: We aim to develop a scalable method that increases the sensitivity of DCS instruments.

Approach: We report on a multispeckle DCS (mDCS) approach that is based on a 1024-pixel single-photon avalanche diode (SPAD) camera. Our approach is scalable to >100;000 independent speckle measurements since large-pixel-count SPAD cameras are becoming available, owing to the investments in LiDAR technology for automotive and augmented reality applications.

Results: We demonstrated a 32-fold increase in SNR with respect to traditional single-speckle DCS.

Conclusion: A mDCS system that is based on a SPAD camera serves as a scalable method toward high-sensitivity DCS measurements, thus enabling both high sensitivity to the cortex and high SNR.

Neurophotonics 7, 035010 (2020)

Related Publications

All Publications

CVPR - June 19, 2021

SimPoE: Simulated Character Control for 3D Human Pose Estimation

Ye Yuan, Shih-En Wei, Tomas Simon, Kris Kitani, Jason Saragih

CVPR - June 19, 2021

Pixel Codec Avatars

Shugao Ma, Tomas Simon, Jason Saragih, Dawei Wang, Yuecheng Li, Fernando De la Torre, Yaser Sheikh

CVPR - June 1, 2021

Semi-supervised Synthesis of High-Resolution Editable Textures for 3D Humans

Bindita Chaudhuri, Nikolaos Sarafianos, Linda Shapiro, Tony Tung

ICASSP - June 6, 2021

Multi-Channel Speech Enhancement Using Graph Neural Networks

Panagiotis Tzirakis, Anurag Kumar, Jacob Donley

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy