Mingliang Pan

• We thoroughly derive and compare different layered analytical models used in continuous-wave (CW), time-domain (TD), and frequency-domain (FD)-DCS, highlighting their strengths and applications. • We discuss novel artificial intelligence (AI)-enhanced DCS analysis strategies, addressing their effectiveness and potential. • We conclude new applications of CMOS SPAD cameras and compare them with existing sensors used in DCS. • We compare TD-DCS and CW-DCS systems and emphasize the benefits of TD-DCS and its potential for future development. • The authors are leading scientists in DCS and SPAD sensors. • Discussion and outlooks are also provided. 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 to 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, sensors, and correlators), as well as 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.

Abstract Background Sepsis-induced organ failure and high mortality are largely ascribed to the failure of bacterial clearance from the infected tissues. Recently, probiotic bacteria-released extracellular vesicles (BEVs) have been implicated as critical mediators of intercellular communication which are widely involved in the regulation of the inflammatory response. However, their functional role in macrophage phagocytosis during sepsis has never been explored. Methods BEVs were collected from three different strains of probiotics including Lactiplantibacillus plantarum WCFS1 (LP WCFS1), Lactobacillus rhamnosus Gorbach-Goldin (LGG), and Escherichia coli Nissle 1917 (EcN), or from LGG cultured under three pH conditions (pH5-acid, pH6.5-standard, pH8-akaline) through differential centrifugation, filtration, and ultracentrifugation of their culture supernatants. In vitro phagocytosis was measured in Raw264.7 cells and bone marrow-derived macrophages using pHrodo red E. coli BioParticles. The in vivo therapeutic effects of BEVs were tested using a feces-injection-in-peritoneum (FIP) model of polymicrobial sepsis. Results LGG-derived EVs (BEV LGG ) were the best among these three probiotics BEVs in stimulating macrophages to take up bacteria. Furthermore, BEV LGG collected from pH8 culture condition (BEV pH8 ) exhibited the strongest capacity of phagocytosis, compared with BEV pH5 and BEV pH6.5 . Treatment of septic mice with BEV pH8 significantly prolonged animal survival; increased bacterial clearance from the blood, peritoneal lavage fluid, and multiple organs; and decreased serum levels of pro-inflammatory cytokines/chemokines, as well as reduced multiple organ injuries, in comparison with control-treated septic mice. Mechanistically, RNA-seq and bioinformatic analysis identified that the FPR1/2 signaling was remarkably activated, along with its downstream pathways (PI3K-Akt-MARCO and NADPH-ROS) in BEV pH8 -treated macrophages, compared with control cells. Accordingly, pre-addition of Boc2, a specific antagonist of FPR1/FPR2, to macrophages significantly attenuated BEV pH8 -mediated phagocytosis, compared to controls. Conclusions This study demonstrates that LGG-derived BEVs may have therapeutic effects against sepsis-induced organ injury and mortality through enhancing FPR1/2-mediated macrophage phagocytosis.

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 sensitivity, time resolution, and high frame-rate imaging capabilities. Despite this, the application of large-format SPAD arrays in speckle contrast analysis is still relatively uncommon. This study introduces 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 validated the proposed approach's potential for cerebral blood flow and functional tissue monitoring. This work highlights the transformative impact of large SPAD cameras on DSCA, setting the stage for breakthroughs in optical imaging.

• We provided a comprehensive discussion of the rigorous mathematical model of diffuse correlation spectroscopy (DCS) and used it to generate synthetic data for training Deep Neural Networks (DNNs). We conducted an extensive comparison of our model with conventional non-linear fitting algorithms and vanilla DNNs, evaluating their performance in reconstructing the blood flow index (BFi). Our results highlighted the robustness of our model against high levels of noise, using a combination of analytical, in-silico simulation, and real liquid phantom datasets. Additionally, we explored relative BFi measurements for practical clinical applications and provided insights into the interpretability of our DNN model. • We implemented the compact DNN on FPGA to validate its computational efficiency. Our implementation results show faster speeds and a higher throughput-power ratio compared to high-performance CPUs and GPUs across different batch sizes. Our efficient hardware implementation, employing various fixed-point bit-width compression strategies, has shown outstanding performance compared to CPUs and GPUs when executing the same tasks. • We acquired real data from an APD-based DCS platform. Liquid phantom (diluted milk) was used to generate the autocorrelation function from a commercial hardware correlator for quantitative evaluation. This study proposes a compact deep learning (DL) architecture and a highly parallelized computing hardware platform to reconstruct the blood flow index (BFi) in diffuse correlation spectroscopy (DCS). We leveraged a rigorous analytical model to generate autocorrelation functions (ACFs) to train the DL network. We assessed the accuracy of the proposed DL using simulated and milk phantom data. Compared to convolutional neural networks (CNN), our lightweight DL architecture achieves 66.7% and 18.5% improvement in MSE for BFi and the coherence factor β , using synthetic data evaluation. The accuracy of rBFi over different algorithms was also investigated. We further simplified the DL computing primitives using subtraction for feature extraction, considering further hardware implementation. We extensively explored computing parallelism and fixed-point quantization within the DL architecture. With the DL model's compact size, we employed unrolling and pipelining optimizations for computation-intensive for-loops in the DL model while storing all learned parameters in on-chip BRAMs. We also achieved pixel-wise parallelism, enabling simultaneous, real-time processing of 10 and 15 autocorrelation functions on Zynq-7000 and Zynq-UltraScale+ field programmable gate array (FPGA), respectively. Unlike existing FPGA accelerators that produce BFi and the β from autocorrelation functions on standalone hardware, our approach is an encapsulated, end-to-end on-chip conversion process from intensity photon data to the temporal intensity ACF and subsequently reconstructing BFi and β . This hardware platform achieves an on-chip solution to replace post-processing and miniaturize modern DCS systems that use single-photon cameras. We also comprehensively compared the computational efficiency of our FPGA accelerator to CPU and GPU solutions.