arXiv subjects

Yuanyuan Hua

Publications and source records attributed to Yuanyuan Hua.

5 recordsLinked to original sources

arXiv2026

Real-time diffuse correlation spectroscopy with a chip-based correlator for measuring human cerebral blood flow and brain function

Diffuse correlation spectroscopy (DCS) is a noninvasive optical technique that probes microvascular blood flow in deep tissues. Here, we present and validate a new on-chip hardware correlator for high-speed DCS measurements. The correlator is embedded in a custom-built 512 x 512 single-photon avalanche diode (SPAD) array named ATLAS, which computes intensity autocorrelation functions directly on-chip at a sampling rate of 116 Hz - the fastest DCS acquisition reported to date. Unlike conventional DCS systems that suffer from low light throughput and therefore cannot resolve cardiac pulsations at source-detector separations (rho) beyond 30 mm, our massively parallel on-chip architecture computes autocorrelations within each macropixel, eliminating the data-throughput bottleneck. This enables high-SNR, real-time detection of pulsatile blood flow even at rho = 50 mm on the human forehead. In phantom experiments at rho = 25 mm, ATLAS-DCS achieves a 12-fold improvement in signal-to-noise ratio over a conventional single-channel DCS instrument while operating at 116 Hz. In human subjects, we resolve functional hyperemia during a mental arithmetic task at rho = 30 mm. Furthermore, we integrate ATLAS DCS with a frequency-domain near-infrared spectroscopy (FD-NIRS) module, enabling simultaneous monitoring of blood flow and tissue oxygenation. With this combined system, we can concurrently resolve core hemodynamic parameters. The on-chip parallelized DCS design substantially improves detection speed, depth sensitivity, and real-time capability, paving the way for wearable, high-speed cerebral blood flow monitoring in both clinical and research settings.

physics.ins-det↗

arXiv2025

Cerebral blood flow monitoring using a deep learning implementation of the two-layer DCS analytical model with a 512x512 SPAD array

Diffuse correlation spectroscopy (DCS) analyzes the autocorrelation function of photons scattered by red blood cells, enabling non-invasive, continuous measurement of deep tissue blood flow at the bedside. Multi-layer DCS models (two- and three-layer) enhance cerebral blood flow index (CBFi) sensitivity and mitigate interference from extracerebral tissues. However, these models require multiple predefined parameters and are computationally intensive, making them impractical for real-time bedside monitoring. To address this challenge, we integrate a single-photon avalanche diode (SPAD) array with a deep learning (DL)-based approach trained on data generated by the two-layer analytical model. This method bypasses traditional model fitting, enabling real-time CBFi monitoring while minimizing superficial tissue contamination. We first validate our approach using Monte Carlo-simulated test datasets, demonstrating superior accuracy in relative CBFi estimation (5.8% error vs. 19.1% for conventional fitting) and enhanced CBFi sensitivity (87.1% vs. 55.4%). Additionally, our method effectively isolates shallow blood flow changes and 750-fold faster than single-exponential fitting in a realistic scenario. We further evaluate the system in a healthy adult, achieving real-time CBFi monitoring and pulsatile waveform recovery during a brain activity test using a 512 512 SPAD array sensor. These results highlight the potential of our approach for real-time brain activity monitoring.

physics.med-ph↗

arXiv2025

Fiber-based Ultra-High Speed Diffuse Speckle Contrast Analysis System for Deep Blood Flow Sensing Using a Large SPAD Camera

Diffuse speckle contrast analysis (DSCA), also called speckle contrast optical spectroscopy(SCOS), has emerged as a groundbreaking optical imaging technique for tracking dynamic biological processes, including blood flow and tissue perfusion. Recent advancements in single-photon avalanche diode (SPAD) cameras have unlocked exceptional capabilities in sensitivity, time resolution, and high frame rate imaging. Despite this, the application of large-format SPAD arrays in speckle contrast analysis is still relatively uncommon. In this study, we introduce a pioneering use of a large format SPAD camera for DSCA. By harnessing the camera's high temporal resolution and photon detection efficiency, we significantly enhance the accuracy and robustness of speckle contrast measurements. Our experimental results demonstrate the system's remarkable ability to capture rapid temporal variations over a broad field of view, enabling detailed spatiotemporal analysis. Through simulations, phantom experiments, and in vivo studies, we validate the approach's potential for a wide range of biomedical applications, such as cuff occlusion tests and functional tissue monitoring. This work highlights the transformative impact of large SPAD cameras on DSCA, paving the way for new breakthroughs in optical imaging.

physics.ins-det↗

arXiv2021

A Highly Linear and Flexible FPGA-Based Time-to-Digital Converter

Time-to-Digital Converters (TDCs) are major components for the measurements of time intervals. Recent developments in Field-Programmable Gate Array (FPGA) have enabled the opportunity to implement high-performance TDCs, which were only possible using dedicated hardware. In order to eliminate empty histogram bins and achieve a higher level of linearity, FPGA-based TDCs typically apply compensation methods either using multiple delay lines consuming more resources or post-processing, leading to a permanent loss of temporal information. We propose a novel TDC with a single delay line and without compensation to realize a highly linear TDC by encoding the states of the delay lines instead of the thermometer code used in the conventional TDCs. The experimental results show our states-based approach achieves an improved Differential Non-Linearity (DNL) of [-0.998, -1.533] for time resolution of 5.00 ps, [-0.44,0.49] for 10.04 ps, [-0.16, 0.19] for 21.65 ps, [-0.10, 0.11] for 43.87 ps, [-0.06, 0.07] for 64.12 ps, and [-0.07, 0.05] for 87.73 ps, whilst no empty bins have been observed. To our knowledge, the achieved raw linearity together with the zero empty bins and a simple delay line structure exceeds previously reported of the FPGA-based TDCs.

eess.SY↗

arXiv2021

A pulse oximeter based on Time-of-Flight histograms

A pulse oximeter is an optical device that monitors tissue oxygenation levels. Traditionally, these devices estimate the oxygenation level by measuring the intensity of the transmitted light through the tissue and are embedded into everyday devices such as smartphones and smartwatches. However, these sensors require prior information and are susceptible to unwanted changes in the intensity, including ambient light, skin tone, and motion artefacts. Previous experiments have shown the potential of Time-of-Flight (ToF) techniques in measurements of tissue hemodynamics. Our proposed technology uses histograms of photon flight paths within the tissue to obtain tissue oxygenation, regardless of the changes in the intensity of the source. Our device is based on a 45ps time-to-digital converter (TDC) which is implemented in a Xilinx Zynq UltraScale+ field programmable gate array (FPGA), a CMOS Single Photon Avalanche Diode (SPAD) detector, and a low-cost compact laser source. All these components including the SPAD detector are manufactured using the latest commercially available technology, which leads to increased linearity, accuracy, and stability for ToF measurements. This proof-of-concept system is approximately 10cmx8cmx5cm in size, with a high potential for shrinkage through further system development and component integration. We demonstrate preliminary results of ToF pulse measurements and report the engineering details, trade-offs, and challenges of this design. We discuss the potential for mass adoption of ToF based pulse oximeters in everyday devices such as smartphones and wearables.

physics.med-ph↗