arXiv subjects

Mingliang Pan

Publications and source records attributed to Mingliang Pan.

6 recordsLinked to original sources

arXiv2025

Comparison of diffuse correlation spectroscopy analytical models for cerebral blood flow measurements

Multi-layer diffuse correlation spectroscopy (DCS) models have been developed to reduce the contamination of superficial signals in cerebral blood flow index (CBFi) measurements. However, a systematic comparison of these models and clear guidance on model selection are still lacking. This study compares three DCS analytical models: semi-infinite, two-layer, and three-layer, focusing on their fitting strategies, performance, and suitability for CBFi and relative CBFi (rCBFi) estimation. We simulated DCS data using a four-layer slab head model with the Monte Carlo eXtreme (MCX) toolkit. Multiple fitting strategies were evaluated: early time lag range (ETLR) fitting with fixed or variable beta for the semi-infinite model, and single-distance (SD) and multi-distance (MD) fitting for the two- and three-layer models. Model performance was assessed based on CBFi sensitivity, accuracy of CBFi and rCBFi recovery, resistance to signal contamination from scalp and skull, sensitivity to assumed parameter errors, and computational efficiency across source-detector separations of 20 to 35 mm. Optimal fitting methods include ETLR with fixed beta for the semi-infinite model, SD with fixed beta for the two-layer model, and MD for the three-layer model. The multi-layer models achieved higher CBFi sensitivity (up to 100%) compared to 36.8% for the semi-infinite model. The two-layer model offered the best balance of accuracy and robustness, while the three-layer model enabled simultaneous recovery of CBFi, scalp BFi, and rCBFi. The semi-infinite model was the most computationally efficient, requiring only 0.38 seconds for 500 samples, supporting its use in real-time monitoring. This work offers a practical and systematic evaluation of DCS analytical models and provides guidance for selecting the most appropriate model based on application needs.

physics.med-ph↗

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

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

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↗

arXiv2024

A comprehensive overview of diffuse correlation spectroscopy: theoretical framework, recent advances in hardware, analysis, and applications

Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances in sensors, lasers, and deep learning have further boosted the development of new DCS methods. However, newcomers might feel overwhelmed, not only by the already complex DCS theoretical framework but also by the broad range of component options and system architectures. To facilitate new entry into this exciting field, we present a comprehensive review of DCS hardware architectures (continuous-wave, frequency-domain, and time-domain) and summarize corresponding theoretical models. Further, we discuss new applications of highly integrated silicon single-photon avalanche diode (SPAD) sensors in DCS, compare SPADs with existing sensors, and review other components (lasers, fibers, and correlators), as well as new data analysis tools, including deep learning. Potential applications in medical diagnosis are discussed, and an outlook for the future directions is provided, to offer effective guidance to embark on DCS research.

physics.ins-det↗

arXiv2023

Smartphone-based Optical Sectioning (SOS) Microscopy with A Telecentric Design for Fluorescence Imaging

We proposed a Smartphone-based Optical Sectioning (SOS) microscope based on the HiLo technique, with a single smartphone replacing a high-cost illumination source and a camera sensor.We built our SOS with off-the-shelf optical mechanical cage systems with 3D-printed adapters to integrate the smartphone with the SOS main body seamlessly.The liquid light guide can be integrated with the adapter, guiding the smartphone LED light to the digital mirror device with neglectable loss.We used an electrically tunable lens (ETL) instead of a mechanical translation stage to realize low-cost axial scanning. The ETL was conjugated to the objective lens back pupil plane (BPP) to construct a telecentric design by a 4f configuration. This can exempt images of different layers from the variation in magnification. SOS has a 571.5 μm telecentric scanning range and an 11.7 μm axial resolution. The broadband smartphone LED torch can effectively excite fluorescent polystyrene (PS) beads. We successfully used SOS for high contrast fluorescent PS beads imaging with different wavelengths and optical sectioning imaging of accumulated fluorescent PS beads. To our knowledge, the proposed SOS is the first smartphone-based HiLo optical sectioning microscopy. It is a powerful, low-cost tool for biomedical research in resource-limited areas.

physics.optics↗