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diffuse correlation spectroscopy Analysis Implemented on a Field Programmable Gate Array

IEEE ACCESS 2019年 7卷 122503-122512页

作者： Lin, Wei Busch, David R. Goh, Chia Chief Barsi, James Floyd, Thomas F. SUNY Stony Brook Dept Biomed Engn Stony Brook NY 11794 USA Univ Texas Southwestern Med Ctr Dallas Dept Anesthesiol & Pain Management Dallas TX 75390 USA SUNY Stony Brook Dept Orthoped Stony Brook NY 11794 USA

Diffusive correlation spectroscopy (DCS) is an emerging optical technique that measures blood perfusion in deep tissue. In a DCS measurement, temporal changes in the interference pattern of light, which has passed through tissue, are quantified by an autocorrelation function. This autocorrelation function is further parameterized through a non-linear curve fit to a solution to the diffusion equation for coherence transport. The computational load for this non-linear curve fitting is a barrier for deployment of DCS for clinical use, where real-time results, as well as instrument size and simplicity, are important considerations. We have mitigated this computational bottleneck through development of a hardware analyzer for DCS. This analyzer implements the DCS curving fitting algorithm on digital logic circuit using Field Programmable Gate Array (FPGA) technology. The FPGA analyzer is more efficient than a typical software analysis solution. The analyzer module can be easily duplicated for processing multiple channels of DCS data in real-time. We have demonstrated the utility of this analyzer in pre-clinical large animal studies of spinal cord ischemia. In combination with previously described FPGA implementations of auto-correlators, this hardware analyzer can provide a complete device-on-a-chip solution for DCS signal processing. Such a component will enable new DCS applications demanding mobility and real-time processing.

关键词： High performance computing field programmable gate array diffuse correlation spectroscopy

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diffuse correlation spectroscopy: current status and future outlook

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NEUROPHOTONICS 2023年第1期10卷 013509页

作者： Carp, Stefan A. Robinson, Mitchell B. Franceschini, Maria A. Harvard Med Sch Massachusetts Gen Hosp Optic Martinos Res Grp Charlestown MA 02129 USA

diffuse correlation spectroscopy (DCS) has emerged as a versatile, noninvasive method for deep tissue perfusion assessment using near-infrared light. A broad class of applications is being pursued in neuromonitoring and beyond. However, technical limitations of the technology as originally implemented remain as barriers to wider adoption. A wide variety of approaches to improve measurement performance and reduce cost are being explored;these include interferometric methods, camera-based multispeckle detection, and long path photon selection for improved depth sensitivity. We review here the current status of DCS technology and summarize future development directions and the challenges that remain on the path to widespread adoption. (c) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

关键词： diffuse correlation spectroscopy multispeckle detection interferometric detection pathlength-resolved detection near-infrared blood flow

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diffuse correlation spectroscopy for non-invasive, micro-vascular cerebral blood flow measurement

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NEUROIMAGE 2014年第part1期85卷 51-63页

作者： Durduran, Turgut Yodh, Arjun G. ICFO Barcelona 08860 Spain Univ Penn Dept Phys & Astron Philadelphia PA 19104 USA

diffuse correlation spectroscopy (DCS) uses the temporal fluctuations of near-infrared (NIR) light to measure cerebral blood flow (CBF) non-invasively. Here, we provide a brief history of DCS applications in the brain with an emphasis on the underlying physical ideas, common instrumentation and validation. Then we describe recent clinical research that employs DCS-measured CBF as a biomarker of patient well-being, and as an indicator of hemodynamic and metabolic responses to functional stimuli. (C) 2013 Elsevier Inc. All rights reserved.

关键词： diffuse correlation spectroscopy Cerebral blood flow Functional neuroimaging diffuse optics Near-infrared spectroscopy Cerebral metabolic rate of oxygen extraction Ischemic stroke Neurocritical care Neonatology

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diffuse correlation spectroscopy and frequency-domain near-infrared spectroscopy for measuring microvascular blood flow in dynamically exercising human muscles

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JOURNAL OF APPLIED PHYSIOLOGY 2019年第5期127卷 1328-1337页

作者： Quaresima, Valentina Farzam, Parisa Anderson, Pamela Farzam, Parya Y. Wiese, Daniel Carp, Stefan A. Ferrari, Marco Franceschini, Maria Angela Univ LAquila Dept Life Hlth & Environm Sci I-67100 Laquila Italy Harvard Med Sch Opt Athinoula A Martinos Ctr Biomed Imaging Massachusetts Gen Hosp Charlestown MA 02115 USA Dynometr Cambridge MA USA

In the last 20 yr. near-infrared diffuse correlation spectroscopy (DCS) has been developed for providing a noninvasive estimate of microvascular blood flow (BF) as a BF index (BFi) in the human skin. muscle. breast, brain, and other tissue types. In this study, we proposed a new motion correction algorithm for DCS-derived BFi able to remove motion artifacts during cycling exercise. We tested this algorithm on DCS data collected during cycling exercise and demonstrated that DCS can be used to quantify muscle BFi during dynamic high-intensity exercise. In addition, we measured tissue regional oxygen metabolic rate (MRO2i) by combining frequency-domain multidistance near-infrared spectroscopy (FDNIRS) oximetry with DCS flow measures. Recreationally active subjects (n = 12;31 +/- 8 yr, 183 +/- 4 cm, 79 +/- 10 kg) pedaled at 80-100 revolutions/min until volitional fatigue with a work rate increase of 30 W every 4 min. Exercise intensity was normalized in each subject to the cycling power peak (W-peak). Both rectus femoris BFi and MRO2i increased from 15% up to 75% W-peak and then plateaued to the end of the exercise. During the recovery at 30 W cycling power. BFi remained almost constant, whereas MRO2i started to decrease. The BFi/MRO2i plateau was associated with the rising of the lactate concentration. indicating the progressive involvement of the anaerobic metabolism. These findings further highlight the utility of DCS and FDNIRS oximetry as effective, reproducible, and noninvasive techniques to assess muscle BFi and MRO2i in real time during a dynamic exercise such as cycling. NEW & NOTEWORTHY To the best of our knowledge, this study is the first to demonstrate that diffuse correlation spectroscopy in combination with frequency-domain near-infrared spectroscopy can monitor human quadriceps microvascular blood flow and oxygen metabolism with high temporal resolution during a cycling exercise. The optically measured parameters confirm the expected relationship between

关键词： blood flow diffuse correlation spectroscopy exercise muscle oxidative metabolism near-infrared spectroscopy

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diffuse correlation spectroscopy for Intracranial Pressure Estimation through Cardiac Pulse Waveform Analysis 7

Diffuse Correlation Spectroscopy for Intracranial Pressure E...

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Conference on diffuse Optical spectroscopy and Imaging VII held at European Conferences on Biomedical Optics

作者： Ruesch, Alexander Yang, Jason Acharya, Deepshikha Schmitt, Samantha Issar, Deepa Smith, Matthew A. Kainerstorfer, Jana M. Carnegie Mellon Univ Dept Biomed Engn 5000 Forbes Ave Pittsburgh PA 15213 USA Univ Pittsburgh Dept Ophthalmol 203 Lothrop St Pittsburgh PA 15213 USA Univ Pittsburgh Dept Bioengn 3700 OHara St Pittsburgh PA 15260 USA Ctr Neural Basis Cognit 4400 Fifth Ave Pittsburgh PA 15213 USA

ISBN: (数字)9781510628427

ISBN: (纸本)9781510628410;9781510628427

Measuring intracranial pressure (ICP) is typically a highly invasive procedure, in which a ventricular catheter or pressure sensor is placed into the brain. To improve the availability of ICP measurements in non-intensive care patients and research and to reduce the invasiveness and underlying risks of ICP sensing, we developed a non-invasive method to measure ICP with diffuse correlation spectroscopy (DCS) and machine learning. ICP baseline changes were induced in non-human primates (Macaca mulatta) through adjusting the height of a saline reservoir connected to the lateral ventricle via a catheter. ICP was precisely measured with an invasive parenchymal pressure sensor. Cerebral blood flow (CBF) was measured with DCS. The DCS system was operated by a software correlator able to resolve cardiac pulse waves at a sampling rate of 100Hz. To increase signal-to-noise ratio, multiple cardiac pulse waves in CBF were averaged based on systolic peak maximum in invasively measured arterial blood pressure. We hypothesized that the cerebral blood flow pulse waves will change their shape with increasing ICP. The shape of the curve was expressed in numerical features and passed into a regression forest training algorithm. Preliminary results show successful prediction of underlying ICP baselines by the decision forest in one animal. The prediction of non-invasive ICP was achieved with a sampling rate of 1 Hz, an equivalent of about 120 averaged pulses. A larger data set for increased generalizability is the next step to push this approach further.

关键词： Intracranial Pressure diffuse correlation spectroscopy Regression Forest Instrumentation

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Using a simulation approach to optimize time-domain diffuse correlation spectroscopy measurement on human head

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NEUROPHOTONICS 2018年第2期5卷 025007-025007页

作者： Qiu, Lina Cheng, Huiyi Torricelli, Alessandro Li, Jun Politecn Milan Dipartimento Fis Milan Italy South China Normal Univ Natl Ctr Int Res Green Optoelect Guangdong Prov Key Lab Opt Informat Mat & Technol MOE Int Lab Opt Informat TechnolSouth China Acad Guangzhou Guangdong Peoples R China CNR Ist Foton & Nanotecnol Milan Italy

Time-domain diffuse correlation spectroscopy (TD-DCS) has been recently proposed to improve detection of deep blood flow dynamics in a biological tissue, such as human brain. To obtain a high sensitive measurement, several experimental parameters such as the source-detector (SD) distance, gate opening time, and width need to be considered and optimized. We use a simulation approach to optimize these parameters based on Monte Carlo computations using a realistic human head model. Two cortical regions are investigated including the frontal and temporal lobes. SD distance ranging from 0 to 45 mm, gate opening time from 400 to 1000 ps, and gate width from 50 to 3000 ps are considered. The goal is to find out the optimal combinations of these parameters by which the higher contrast measurement on the cortical dynamics can be achieved. The simulations show that with an acceptable input power of light, the combinations of SD distance ranging from 0 to 15 mm, gate opening time at 700 to 800 ps, and gate width of 800 ps are optimal for achieving higher contrast measurement on the cortical dynamics. The simulation approach and results are helpful for the optimization of TD-DCS experimental design focused on brain functional detection. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

关键词： diffuse correlation spectroscopy time-domain simulation optimization Monte Carlo brain functional detection

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Quantification of blood flow index in diffuse correlation spectroscopy using a robust deep learning method

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JOURNAL OF BIOMEDICAL OPTICS 2024年第1期29卷 015004-015004页

作者： Wang, Quan Pan, Mingliang Zang, Zhenya Li, David Day-Uei Univ Strathclyde Fac Engn Dept Biomed Engn Glasgow City Scotland

Significance: diffuse correlation spectroscopy (DCS) is a powerful, non-invasive optical technique for measuring blood flow. Traditionally the blood flow index (BFi) is derived through nonlinear least-square fitting the measured intensity autocorrelation function (ACF). However, the fitting process is computationally intensive, susceptible to measurement noise, and easily influenced by optical properties (absorption coefficient mu_a and reduced scattering coefficient mu_s<^>') and scalp and skull thicknesses. Aim: We aim to develop a data-driven method that enables rapid and robust analysis of multiple-scattered light's temporal ACFs. Moreover, the proposed method can be applied to a range of source-detector distances instead of being limited to a specific source-detector distance. Approach: We present a deep learning architecture with one-dimensional convolution neural networks, called DCS neural networks (DCS-NET), for BFi and coherent factor (beta) estimation. This DCS-NET was performed using simulated DCS data based on a three-layer brain model. We quantified the impact from physiologically relevant optical property variations, layer thicknesses, realistic noise levels, and multiple source-detector distances (5, 10, 15, 20, 25, and 30 mm) on BFi and beta estimations among DCS-NET, semi-infinite, and three-layer fitting models. Results: DCS-NET shows a much faster analysis speed, around 17,000-fold and 32-fold faster than the traditional three-layer and semi-infinite models, respectively. It offers higher intrinsic sensitivity to deep tissues compared with fitting methods. DCS-NET shows excellent anti-noise features and is less sensitive to variations of mu_a and mu_s<^>' at a source-detector separation of 30 mm. Also, we have demonstrated that relative BFi (rBFi) can be extracted by DCS-NET with a much lower error of 8.35%. By contrast, the semi-infinite and three-layer fitting models result in significant errors in rBFi of 43.76% and 19.66%, respectively. Concl

关键词： deep learning blood flow diffuse correlation spectroscopy

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Agreement in cerebrovascular reactivity assessed with diffuse correlation spectroscopy across experimental paradigms improves with short separation regression

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NEUROPHOTONICS 2023年第2期10卷 025002页

作者： Cowdrick, Kyle R. Urner, Tara Sathialingam, Eashani Fang, Zhou Quadri, Ayesha Turrentine, Katherine Yup Lee, Seung Buckley, Erin M. Georgia Inst Technol Atlanta GA 30332 USA Emory Univ Wallace H Coulter Dept Biomed Engn Atlanta GA 30322 USA Childrens Healthcare Atlanta Atlanta GA 30322 USA Emory Univ Sch Med Dept Pediat Atlanta GA 30322 USA Morehouse Sch Med Atlanta GA 30310 USA Kennesaw State Univ Dept Elect & Comp Engn Marietta GA USA Childrens Healthcare Atlanta Atlanta GA 30322 USA

Significance: Cerebrovascular reactivity (CVR), i.e., the ability of cerebral vasculature to dilate or constrict in response to vasoactive stimuli, is a biomarker of vascular health. Exogenous administration of inhaled carbon dioxide, i.e., hypercapnia (HC), remains the "gold-standard" intervention to assess CVR. More tolerable paradigms that enable CVR quantification when HC is difficult/contraindicated have been proposed. However, because these paradigms feature mechanistic differences in action, an assessment of agreement of these more tolerable paradigms to HC is ***: We aim to determine the agreement of CVR assessed during HC, breath-hold (BH), and resting state (RS) ***: Healthy adults were subject to HC, BH, and RS paradigms. End tidal carbon dioxide (EtCO2) and cerebral blood flow (CBF, assessed with diffuse correlation spectroscopy) were monitored continuously. CVR (%/mmHg) was quantified via linear regression of CBF versus EtCO2 or via a general linear model (GLM) that was used to minimize the influence of systemic and extracerebral signal ***: Strong agreement (CCC = 0.69;R = 0.76) among CVR paradigms was demonstrated when utilizing a GLM to regress out systemic/extracerebral signal contributions. Linear regression alone showed poor agreement across paradigms (CCC = 0.35;R = 0.45).Conclusions: More tolerable experimental paradigms coupled with regression of systemic/extracerebral signal contributions may offer a viable alternative to HC for assessing CVR.

关键词： cerebrovascular reactivity diffuse correlation spectroscopy cerebral blood flow

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Development and characterization of a multidistance and multiwavelength diffuse correlation spectroscopy system

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NEUROPHOTONICS 2018年第1期5卷 011015-011015页

作者： Tamborini, Davide Farzam, Parisa Zimmermann, Bernhard Wu, Kuan-Cheng Boas, David A. Franceschini, Maria Angela Harvard Med Sch Massachusetts Gen Hosp MGH HST Athinoula A Martinos Ctr Biomed Imaging Charlestown MA 02115 USA Boston Univ Neurophoton Ctr Boston MA 02215 USA

This paper presents a multidistance and multiwavelength diffuse correlation spectroscopy (DCS) approach and its implementation to simultaneously measure the optical proprieties of deep tissue as well as the blood flow. The system consists of three long coherence length lasers at different wavelengths in the near-infrared, eight single-photon detectors, and a correlator board. With this approach, we collect both light intensity and DCS data at multiple distances and multiple wavelengths, which provide unique information to fit for all the parameters of interest: scattering, blood flow, and hemoglobin concentration. We present the characterization of the system and its validation with phantom measurements. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

关键词： near-infrared spectroscopy diffuse correlation spectroscopy blood flow cerebral hemodynamic

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Measuring neuronal activity with diffuse correlation spectroscopy: a theoretical investigation

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NEUROPHOTONICS 2021年第3期8卷 35004页

作者： Cheng, Xiaojun Sie, Edbert J. Naufel, Stephanie Boas, David A. Marsili, Francesco Boston Univ Neurophoton Ctr Dept Biomed Engn Boston MA 02215 USA Facebook Real Labs Res Menlo Pk CA USA

Significance: diffuse correlation spectroscopy (DCS) measures cerebral blood flow non-invasively. Variations in blood flow can be used to detect neuronal activities, but its peak has a latency of a few seconds, which is slow for real-time monitoring. Neuronal cells also deform during activation, which, in principle, can be utilized to detect neuronal activity on fast time-scales (within 100 ms) using DCS. Aims: We aim to characterize DCS signal variation quantified as the change of the decay time of the speckle intensity autocorrelation function during neuronal activation on both fast (within 100 ms) and slow (100 ms to seconds) timescales. Approach: We extensively modeled the variations in the DCS signal that are expected to arise from neuronal activation using Monte Carlo simulations, including the impacts of neuronal cell motion, vessel wall dilation, and blood flow changes. Results: We found that neuronal cell motion induces a DCS signal variation of similar to 10(-5). We also estimated the contrast and number of channels required to detect hemodynamic signals at different time delays. Conclusions: From this extensive analysis, we do not expect to detect neuronal cell motion using DCS in the near future based on current technology trends. However, multi-channel DCS will be able to detect hemodynamic response with sub-second latency, which is interesting for brain-computer interfaces. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

关键词： diffuse correlation spectroscopy Monte Carlo neuronal activation neuronal cell motion hemodynamics

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