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Fang Q, Tomar A, Dunn AK. Wide-field intensity fluctuation imaging. BIOMEDICAL OPTICS EXPRESS 2024; 15:1004-1020. [PMID: 38404351 PMCID: PMC10890890 DOI: 10.1364/boe.506870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/08/2024] [Accepted: 01/09/2024] [Indexed: 02/27/2024]
Abstract
The temporal intensity fluctuations contain important information about the light source and light-medium interaction and are typically characterized by the intensity autocorrelation function, g2(τ). The measurement of g2(τ) is a central topic in many optical sensing applications, ranging from stellar intensity interferometer in astrophysics, to fluorescence correlation spectroscopy in biomedical sciences and blood flow measurement with dynamic light scattering. Currently, g2(τ) at a single point is readily accessible through high-frequency sampling of the intensity signal. However, two-dimensional wide-field imaging of g2(τ) is still limited by the cameras' frame rate. We propose and demonstrate a 2-pulse within-exposure modulation approach to break through the camera frame rate limit and obtain the quasi g2(τ) map in wide field with cameras of only ordinary frame rates.
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Affiliation(s)
- Qingwei Fang
- Department of Biomedical Engineering, The University of Texas at Austin , Austin, Texas 78712, USA
| | - Alankrit Tomar
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Andrew K Dunn
- Department of Biomedical Engineering, The University of Texas at Austin , Austin, Texas 78712, USA
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, USA
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2
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Kobayashi Frisk L, Verma M, Bešlija F, Lin CHP, Patil N, Chetia S, Trobaugh JW, Culver JP, Durduran T. Comprehensive workflow and its validation for simulating diffuse speckle statistics for optical blood flow measurements. BIOMEDICAL OPTICS EXPRESS 2024; 15:875-899. [PMID: 38404339 PMCID: PMC10890893 DOI: 10.1364/boe.502421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 12/19/2023] [Accepted: 12/20/2023] [Indexed: 02/27/2024]
Abstract
Diffuse optical methods including speckle contrast optical spectroscopy and tomography (SCOS and SCOT), use speckle contrast (κ) to measure deep blood flow. In order to design practical systems, parameters such as signal-to-noise ratio (SNR) and the effects of limited sampling of statistical quantities, should be considered. To that end, we have developed a method for simulating speckle contrast signals including effects of detector noise. The method was validated experimentally, and the simulations were used to study the effects of physical and experimental parameters on the accuracy and precision of κ. These results revealed that systematic detector effects resulted in decreased accuracy and precision of κ in the regime of low detected signals. The method can provide guidelines for the design and usage of SCOS and/or SCOT instruments.
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Affiliation(s)
- Lisa Kobayashi Frisk
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Castelldefels (Barcelona), Spain
| | - Manish Verma
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Castelldefels (Barcelona), Spain
| | - Faruk Bešlija
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Castelldefels (Barcelona), Spain
| | - Chen-Hao P. Lin
- Department of Physics, Washington University in St. Louis, St. Louis, Missouri 63110, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Nishighanda Patil
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Castelldefels (Barcelona), Spain
| | - Sumana Chetia
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Castelldefels (Barcelona), Spain
| | - Jason W. Trobaugh
- Department of Electrical and Systems Engineering, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Joseph P. Culver
- Department of Physics, Washington University in St. Louis, St. Louis, Missouri 63110, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Turgut Durduran
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Castelldefels (Barcelona), Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
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3
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Jain P, Gupta S. Enhancing blood flow prediction in multi-exposure laser speckle contrast imaging through ensemble learning with K-mean clustering. Biomed Phys Eng Express 2024; 10:025005. [PMID: 38109789 DOI: 10.1088/2057-1976/ad16c2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 12/18/2023] [Indexed: 12/20/2023]
Abstract
Purpose.Accurately visualizing and measuring blood flow is of utmost importance in maintaining optimal health and preventing the onset of various chronic diseases. One promising imaging technique that aids in visualizing perfusion in biological tissues is Multi-exposure Laser Speckle Contrast Imaging (MELSCI). MELSCI technique allows real-time quantitative measurements using multiple exposure times to obtain precise and reliable blood flow data. Additionally, the application of machine learning (ML) techniques can further enhance the accuracy of blood flow prediction in this imaging modality.Method.Our study focused on developing and evaluating Ensemble Learning ML techniques along with clustering algorithms for predicting blood flow rates in MELSCI. The effectiveness of these techniques was assessed using performance parameters, including accuracy, F1-score, precision, recall, specificity, and classification error rate.Result.Notably, the study revealed that Ensemble Learning with clustering emerged as the most accurate technique, achieving an impressive accuracy rate of 98.5%. Furthermore, it demonstrated a high recall of more than 91%, F1-score, the precision of more than 90%, higher specificity of 99%, and least classification error of 1.5%, highlighting its suitability and sustainability for flow prediction in MELSCI.Conclusion.The study's findings imply that Ensemble Learning can significantly contribute to enhancing the accuracy of blood flow prediction in MELSCI. This advancement holds substantial promise for healthcare professionals and researchers, as it facilitates improved understanding and assessment of perfusion within biological tissues, which will contribute to the maintenance of good health and prevention of chronic diseases.
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Affiliation(s)
- Pankaj Jain
- National Institute of Technology Raipur, Raipur, CG, 492010, India
| | - Saurabh Gupta
- National Institute of Technology Raipur, Raipur, CG, 492010, India
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Favilla CG, Carter S, Hartl B, Gitlevich R, Mullen MT, Yodh AG, Baker WB, Konecky S. Validation of the Openwater wearable optical system: cerebral hemodynamic monitoring during a breath-hold maneuver. NEUROPHOTONICS 2024; 11:015008. [PMID: 38464864 PMCID: PMC10923543 DOI: 10.1117/1.nph.11.1.015008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 02/10/2024] [Accepted: 02/13/2024] [Indexed: 03/12/2024]
Abstract
Significance Bedside cerebral blood flow (CBF) monitoring has the potential to inform and improve care for acute neurologic diseases, but technical challenges limit the use of existing techniques in clinical practice. Aim Here, we validate the Openwater optical system, a novel wearable headset that uses laser speckle contrast to monitor microvascular hemodynamics. Approach We monitored 25 healthy adults with the Openwater system and concurrent transcranial Doppler (TCD) while performing a breath-hold maneuver to increase CBF. Relative blood flow (rBF) was derived from changes in speckle contrast, and relative blood volume (rBV) was derived from changes in speckle average intensity. Results A strong correlation was observed between beat-to-beat optical rBF and TCD-measured cerebral blood flow velocity (CBFv), R = 0.79 ; the slope of the linear fit indicates good agreement, 0.87 (95% CI: 0.83 - 0.92 ). Beat-to-beat rBV and CBFv were also strongly correlated, R = 0.72 , but as expected the two variables were not proportional; changes in rBV were smaller than CBFv changes, with linear fit slope of 0.18 (95% CI: 0.17 to 0.19). Further, strong agreement was found between rBF and CBFv waveform morphology and related metrics. Conclusions This first in vivo validation of the Openwater optical system highlights its potential as a cerebral hemodynamic monitor, but additional validation is needed in disease states.
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Affiliation(s)
- Christopher G. Favilla
- University of Pennsylvania, Department of Neurology, Philadelphia, Pennsylvania, United States
| | - Sarah Carter
- University of Pennsylvania, Department of Neurology, Philadelphia, Pennsylvania, United States
| | - Brad Hartl
- Openwater, San Francisco, California, United States
| | - Rebecca Gitlevich
- University of Pennsylvania, Department of Neurology, Philadelphia, Pennsylvania, United States
| | - Michael T. Mullen
- Temple University, Department of Neurology, Philadelphia, Pennsylvania, United States
| | - Arjun G. Yodh
- University of Pennsylvania, Department of Physics and Astronomy, Philadelphia, Pennsylvania, United States
| | - Wesley B. Baker
- Children’s Hospital of Philadelphia, Department of Neurology, Philadelphia, Pennsylvania, United States
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Robinson MB, Cheng TY, Renna M, Wu MM, Kim B, Cheng X, Boas DA, Franceschini MA, Carp SA. Comparing the performance potential of speckle contrast optical spectroscopy and diffuse correlation spectroscopy for cerebral blood flow monitoring using Monte Carlo simulations in realistic head geometries. NEUROPHOTONICS 2024; 11:015004. [PMID: 38282721 PMCID: PMC10821780 DOI: 10.1117/1.nph.11.1.015004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 12/13/2023] [Accepted: 01/08/2024] [Indexed: 01/30/2024]
Abstract
Significance The non-invasive measurement of cerebral blood flow based on diffuse optical techniques has seen increased interest as a research tool for cerebral perfusion monitoring in critical care and functional brain imaging. Diffuse correlation spectroscopy (DCS) and speckle contrast optical spectroscopy (SCOS) are two such techniques that measure complementary aspects of the fluctuating intensity signal, with DCS quantifying the temporal fluctuations of the signal and SCOS quantifying the spatial blurring of a speckle pattern. With the increasing interest in the use of these techniques, a thorough comparison would inform new adopters of the benefits of each technique. Aim We systematically evaluate the performance of DCS and SCOS for the measurement of cerebral blood flow. Approach Monte Carlo simulations of dynamic light scattering in an MRI-derived head model were performed. For both DCS and SCOS, estimates of sensitivity to cerebral blood flow changes, coefficient of variation of the measured blood flow, and the contrast-to-noise ratio of the measurement to the cerebral perfusion signal were calculated. By varying complementary aspects of data collection between the two methods, we investigated the performance benefits of different measurement strategies, including altering the number of modes per optical detector, the integration time/fitting time of the speckle measurement, and the laser source delivery strategy. Results Through comparison across these metrics with simulated detectors having realistic noise properties, we determine several guiding principles for the optimization of these techniques and report the performance comparison between the two over a range of measurement properties and tissue geometries. We find that SCOS outperforms DCS in terms of contrast-to-noise ratio for the cerebral blood flow signal in the ideal case simulated here but note that SCOS requires careful experimental calibrations to ensure accurate measurements of cerebral blood flow. Conclusion We provide design principles by which to evaluate the development of DCS and SCOS systems for their use in the measurement of cerebral blood flow.
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Affiliation(s)
- Mitchell B. Robinson
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Boston, Massachusetts, United States
| | - Tom Y. Cheng
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Boston, Massachusetts, United States
- Boston University, Neurophotonics Center, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Marco Renna
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Boston, Massachusetts, United States
| | - Melissa M. Wu
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
| | - Byungchan Kim
- Boston University, Neurophotonics Center, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Xiaojun Cheng
- Boston University, Neurophotonics Center, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - David A. Boas
- Boston University, Neurophotonics Center, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Maria Angela Franceschini
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Boston, Massachusetts, United States
| | - Stefan A. Carp
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Boston, Massachusetts, United States
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Seong M. Comparison of numerical-integration-based methods for blood flow estimation in diffuse correlation spectroscopy. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 241:107766. [PMID: 37647812 DOI: 10.1016/j.cmpb.2023.107766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 07/31/2023] [Accepted: 08/13/2023] [Indexed: 09/01/2023]
Abstract
BACKGROUND AND OBJECTIVE Diffuse correlation spectroscopy (DCS) is an optical blood flow monitoring technology that has been utilized in various biomedical applications. In signal processing of DCS, nonlinear fitting of the experimental data and the theoretical model can be a hindrance in real-time blood flow monitoring. As one of the approaches to resolve the issue, INISg1, the inverse of numerical integration of squared g1 (a normalized electric field autocorrelation function), that could surpass the state-of-the-art technique at the time in terms of signal processing speed, has been introduced. While it is possible to implement INISg1 using various numerical integration methods, no relevant studies have been performed. Meanwhile, INISg1 was only tested within limited experimental conditions, which cannot guarantee the robustness of INISg1 in various experimental conditions. Thus, this study aims to introduce variants of INISg1 and perform a thorough comparison of the original INISg1 and its variants. METHODS In this study, based on the right Riemann sum (RR) and trapezoid rule (TR) of numerical integration, INISg1_RR and INISg1_TR are suggested. They are thoroughly compared with the original INISg1 using model-based simulations that offer us control of most of the experimental conditions, including integration time, β, and photon count rate. RESULTS Except for some extreme cases, INISg1 performed more robustly than INISg1_RR and INISg1_TR. However, in extreme conditions, variants of INISg1 performed better than INISg1. With the same condition, the signal processing speed of INISg1 was 1.63 and 1.98 times faster than INISg1_RR and INISg1_TR, respectively. CONCLUSION This study shows that INISg1 is robust in most cases and the study can be a guide for researchers using INISg1 and its variants in different types of DCS applications.
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Affiliation(s)
- Myeongsu Seong
- Research Center for Intelligent Information Technology, Nantong University, Nantong 226019, China; Department of Mechatronics and Robotics, School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China.
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Frisk LK, Verma M, Bešlija F, Lin CHP, Patil N, Chetia S, Trobaugh J, Culver JP, Durduran T. A comprehensive workflow and its validation for simulating diffuse speckle statistics for optical blood flow measurements. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.03.551830. [PMID: 37577491 PMCID: PMC10418286 DOI: 10.1101/2023.08.03.551830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Diffuse optical methods including speckle contrast optical spectroscopy and tomography (SCOS and SCOT), use speckle contrast (κ ) to measure deep blood flow. In order to design practical systems, parameters such as signal-to-noise ratio (SNR) and the effects of limited sampling of statistical quantities, should be considered. To that end, we have developed a method for simulating speckle contrast signals including effects of detector noise. The method was validated experimentally, and the simulations were used to study the effects of physical and experimental parameters on the accuracy and precision of κ . These results revealed that systematic detector effects resulted in decreased accuracy and precision of κ in the regime of low detected signals. The method can provide guidelines for the design and usage of SCOS and/or SCOT instruments.
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Affiliation(s)
- Lisa Kobayashi Frisk
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Castelldefels (Barcelona), Spain
| | - Manish Verma
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Castelldefels (Barcelona), Spain
| | - Faruk Bešlija
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Castelldefels (Barcelona), Spain
| | - Chen-Hao P. Lin
- Department of Physics, Washington University in St. Louis, St. Louis, Missouri 63110, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Nishighanda Patil
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Castelldefels (Barcelona), Spain
| | - Sumana Chetia
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Castelldefels (Barcelona), Spain
| | - Jason Trobaugh
- Department of Electrical and Systems Engineering, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Joseph P. Culver
- Department of Physics, Washington University in St. Louis, St. Louis, Missouri 63110, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Turgut Durduran
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Castelldefels (Barcelona), Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
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8
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Jain P, Gupta S. Blood Flow Prediction in Multi-Exposure Speckle Contrast Imaging Using Conditional Generative Adversarial Network. Cureus 2023; 15:e37349. [PMID: 37182031 PMCID: PMC10170186 DOI: 10.7759/cureus.37349] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/05/2023] [Indexed: 05/16/2023] Open
Abstract
Purpose Blood perfusion is an important physiological parameter that can be quantitatively assessed using various imaging techniques. Blood flow prediction in laser speckle contrast imaging is important for medical diagnosis, drug development, tissue engineering, biomedical research, and continuous monitoring. Deep learning is a new and promising approach for predicting blood flow whenever the condition varies, but it comes with a high learning cost for real-world scenarios with a variable flow value derived from multi-exposure laser speckle contrast imaging (MECI) data. A generative adversarial network (GAN) is presented in this research for the reliable prediction of blood flows in diverse scenarios in MECI. Method We suggested a time-efficient approach using a low frame rate camera that can be used to predict blood flow in MECI data by using conditional GAN architecture. Our approach is implemented by extending our work to the entire flow as well as the specific region of interest (ROI) in the flow. Results Results show that conditional GAN exhibits improved generalization ability to predict blood flow in MECI when compared to classifications-based deep learning approaches with an accuracy of 98.5% with a relative mean error of 1.57% for the whole field and 7.53% for a specific ROI. Conclusion The conditional GAN is very effective in predicting blood flows in MECI, entirely or within ROI, compared with other deep learning approaches.
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Affiliation(s)
- Pankaj Jain
- Biomedical Engineering, National Institute of Technology, Raipur, IND
| | - Saurabh Gupta
- Biomedical Engineering, National Institute of Technology, Raipur, IND
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Wayne MA, Sie EJ, Ulku AC, Mos P, Ardelean A, Marsili F, Bruschini C, Charbon E. Massively parallel, real-time multispeckle diffuse correlation spectroscopy using a 500 × 500 SPAD camera. BIOMEDICAL OPTICS EXPRESS 2023; 14:703-713. [PMID: 36874503 PMCID: PMC9979680 DOI: 10.1364/boe.473992] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/01/2022] [Accepted: 12/24/2022] [Indexed: 06/02/2023]
Abstract
Diffuse correlation spectroscopy (DCS) is a promising noninvasive technique for monitoring cerebral blood flow and measuring cortex functional activation tasks. Taking multiple parallel measurements has been shown to increase sensitivity, but is not easily scalable with discrete optical detectors. Here we show that with a large 500 × 500 SPAD array and an advanced FPGA design, we achieve an SNR gain of almost 500 over single-pixel mDCS performance. The system can also be reconfigured to sacrifice SNR to decrease correlation bin width, with 400 ns resolution being demonstrated over 8000 pixels.
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Affiliation(s)
- Michael A. Wayne
- Advanced Quantum Architecture Laboratory, École polytechnique fédérale de Lausanne, Rue de la Maladière 71B, Neuchatel, NE 2000, Switzerland
| | - Edbert J. Sie
- Reality Labs Research, Meta Platforms Inc., Menlo Park, CA 94025, USA
| | - Arin C. Ulku
- Advanced Quantum Architecture Laboratory, École polytechnique fédérale de Lausanne, Rue de la Maladière 71B, Neuchatel, NE 2000, Switzerland
| | - Paul Mos
- Advanced Quantum Architecture Laboratory, École polytechnique fédérale de Lausanne, Rue de la Maladière 71B, Neuchatel, NE 2000, Switzerland
| | - Andrei Ardelean
- Advanced Quantum Architecture Laboratory, École polytechnique fédérale de Lausanne, Rue de la Maladière 71B, Neuchatel, NE 2000, Switzerland
| | - Francesco Marsili
- Reality Labs Research, Meta Platforms Inc., Menlo Park, CA 94025, USA
| | - Claudio Bruschini
- Advanced Quantum Architecture Laboratory, École polytechnique fédérale de Lausanne, Rue de la Maladière 71B, Neuchatel, NE 2000, Switzerland
| | - Edoardo Charbon
- Advanced Quantum Architecture Laboratory, École polytechnique fédérale de Lausanne, Rue de la Maladière 71B, Neuchatel, NE 2000, Switzerland
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K M, Varma HM. Laser speckle simulation tool based on stochastic differential equations for bio imaging applications. BIOMEDICAL OPTICS EXPRESS 2022; 13:6745-6762. [PMID: 36589556 PMCID: PMC9774864 DOI: 10.1364/boe.470926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 10/24/2022] [Accepted: 10/25/2022] [Indexed: 06/17/2023]
Abstract
Laser speckle-based blood flow imaging is a well-accepted and widely used method for pre-clinical and clinical applications. Although it was introduced as a method to measure only superficial blood flow (< 1mm depth), several recently introduced variants resulted in measuring deep tissue blood flow (a few cm) as well. A means of simulating laser speckles is often necessary for the analysis and development of these imaging modalities, as evident from many such attempts towards developing simulation tools in the past. Such methods often employ Fourier transforms or statistical tools to simulate speckles with desired statistical properties. We present the first method to use a stochastic differential equation to generate laser speckles with a pre-determined probability density function and a temporal auto-correlation. The method allows the choice of apriori gamma distribution along with simple exponential or more complex temporal auto-correlation statistics for simulated speckles, making it suitable for different blood flow profiles. In contrast to the existing methods that often generate speckles associated with superficial flow, we simulate both superficial and diffuse speckles leading to applications in deep tissue blood flow imaging. In addition, we have also incorporated appropriate models for noise associated with the detectors to simulate realistic speckles. We have validated our model by comparing the simulated speckles with those obtained from in-vivo studies in mice and healthy human subject.
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11
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Paul R, Murali K, Varma HM. High-density diffuse correlation tomography with enhanced depth localization and minimal surface artefacts. BIOMEDICAL OPTICS EXPRESS 2022; 13:6081-6099. [PMID: 36733746 PMCID: PMC9872877 DOI: 10.1364/boe.469405] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/14/2022] [Accepted: 09/21/2022] [Indexed: 05/08/2023]
Abstract
A spatially weighted filter applied to both the measurement and the Jacobian is proposed for high-density diffuse correlation tomography (DCT) to remove unwanted extracerebral interferences and artefacts along with better depth localization in the reconstructed blood flow images. High-density DCT is implemented by appropriate modification of recently introduced Multi-speckle Diffuse Correlation Spectroscopy (M-DCS) system. Additionally, we have used autocorrelation measurements at multiple delay-times in an iterative manner to improve the reconstruction results. The proposed scheme has been validated by simulations, phantom experiments and in-vivo human experiments.
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Herranz Olazábal J, Wieringa F, Hermeling E, Van Hoof C. Camera-Derived Photoplethysmography (rPPG) and Speckle Plethysmography (rSPG): Comparing Reflective and Transmissive Mode at Various Integration Times Using LEDs and Lasers. SENSORS (BASEL, SWITZERLAND) 2022; 22:6059. [PMID: 36015822 PMCID: PMC9412985 DOI: 10.3390/s22166059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 08/10/2022] [Accepted: 08/12/2022] [Indexed: 06/15/2023]
Abstract
Background: Although both speckle plethysmography (SPG) and photoplethysmography (PPG) examine pulsatile changes in the vasculature using opto-electronics, PPG has a long history, whereas SPG is relatively new and less explored. The aim of this study was to compare the effects of integration time and light-source coherence on signal quality and waveform morphology for reflective and transmissive rSPG and rPPG. Methods: (A) Using time-domain multiplexing, we illuminated 10 human index fingers with pulsed lasers versus LEDs (both at 639 and 850 nm), in transmissive versus reflective mode. A synchronized camera (Basler acA2000-340 km, 25 cm distance, 200 fps) captured and demultiplexed four video channels (50 fps/channel) in four stages defined by illumination mode. From all video channels, we derived rPPG and rSPG, and applied a signal quality index (SQI, scale: Good > 0.95; Medium 0.95−0.85; Low 0.85−0.8; Negligible < 0.8); (B) For transmission videos only, we additionally calculated the intensity threshold area (ITA), as the area of the imaging exceeding a certain intensity value and used linear regression analysis to understand unexpected similarities between rPPG and rSPG. Results: All mean SQI-values. Reflective mode: Laser-rSPG > 0.965, LED-rSPG < 0.78, rPPG < 0.845. Transmissive mode: 0.853−0.989 for rSPG and rPPG at all illumination settings. Coherent mode: Reflective rSPG > 0.951, reflective rPPG < 0.740, transmissive rSPG and rPPG 0.990−0.898. Incoherent mode: Reflective all <0.798 and transmissive all 0.92−0.987. Linear regressions revealed similar R2 values of rPPG with rSPG (R2 = 0.99) and ITA (R2 = 0.98); Discussion: Laser-rSPG and LED-rPPG produced different waveforms in reflection, but not in transmission. We created the concept of ITA to investigate this behavior. Conclusions: Reflective Laser-SPG truly originated from coherence. Transmissive Laser-rSPG showed a loss of speckles, accompanied by waveform changes towards rPPG. Diffuse spatial intensity modulation polluted spatial-mode SPG.
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Affiliation(s)
- Jorge Herranz Olazábal
- IMEC, 3000 Leuven, Belgium
- Faculty of Engineering Science, Katholieke Universiteit Leuven (KUL), 3000 Leuven, Belgium
| | - Fokko Wieringa
- IMEC NL, 5656 AE Eindhoven, The Netherlands
- Division of Internal Medicine, Department of Nephrology, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | | | - Chris Van Hoof
- IMEC, 3000 Leuven, Belgium
- Faculty of Engineering Science, Katholieke Universiteit Leuven (KUL), 3000 Leuven, Belgium
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Seong M, Oh Y, Lee K, Kim JG. Blood flow estimation via numerical integration of temporal autocorrelation function in diffuse correlation spectroscopy. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 222:106933. [PMID: 35728393 DOI: 10.1016/j.cmpb.2022.106933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 05/27/2022] [Accepted: 06/02/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Diffuse correlation spectroscopy (DCS) is an optical technique widely used to monitor blood flow. Recently, efforts have been made to derive new signal processing methods to minimize the systems used and shorten the signal processing time. Herein, we propose alternative approaches to obtain blood flow information via DCS by numerically integrating the temporal autocorrelation curves. METHODS We use the following methods: the inverse of K2 (IK2)-based on the framework of diffuse speckle contrast analysis-and the inverse of the numerical integration of squared g1 (INISg1) which, based on the normalized electric field autocorrelation curve, is more simplified than IK2. In addition, g1 thresholding is introduced to further reduce computational time and make the suggested methods comparable to the conventional nonlinear fitting approach. To validate the feasibility of the suggested methods, studies using simulation, liquid phantom, and in vivo settings were performed. In the meantime, the suggested methods were implemented and tested on three types of Arduino (Arduino Due, Arduino Nano 33 BLE Sense, and Portenta H7) to demonstrate the possibility of miniaturizing the DCS systems using microcotrollers for signal processing. RESULTS The simulation and experimental results confirm that both IK2 and INISg1 are sufficiently relevant to capture the changes in blood flow information. More interestingly, when g1 thresholding was applied, our results showed that INISg1 outperformed IK2. It was further confirmed that INISg1 with g1 thresholding implemented on a PC and Portenta H7, an advanced Arduino board, performed faster than did the deep learning-based, state-of-the-art processing method. CONCLUSION Our findings strongly indicate that INISg1 with g1 thresholding could be an alternative approach to derive relative blood flow information via DCS, which may contribute to the simplification of DCS methodologies.
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Affiliation(s)
- Myeongsu Seong
- School of Information Science and Technology, Nantong University, Nantong, Jiangsu, China; Research Center for Intelligent Information Technology, Nantong University, Nantong, Jiangsu, China; Nantong Research Institute for Advanced Communication Technologies, Nantong, Jiangsu, China
| | - Yoonho Oh
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Kijoon Lee
- Department of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea.
| | - Jae G Kim
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea.
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James E, Powell S, Munro P. Simulation of statistically accurate time-integrated dynamic speckle patterns in biomedical optics. OPTICS LETTERS 2021; 46:4390-4393. [PMID: 34470023 DOI: 10.1364/ol.435812] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 08/10/2021] [Indexed: 06/13/2023]
Abstract
The simulation of statistically accurate time-integrated dynamic speckle patterns using a physics-based model that accounts for spatially varying sample properties is yet to be presented in biomedical optics. In this Letter, we propose a solution to this important problem based on the Karhunen-Loève expansion of the electric field and apply our method to the formalisms of both laser speckle contrast imaging and diffuse correlation spectroscopy. We validate our technique against solutions for speckle contrast for different forms of homogeneous field and also show that our method can readily be extended to cases with spatially varying sample properties.
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Murali K, Varma HM. Multi-speckle diffuse correlation spectroscopy to measure cerebral blood flow. BIOMEDICAL OPTICS EXPRESS 2020; 11:6699-6709. [PMID: 33282518 PMCID: PMC7687951 DOI: 10.1364/boe.401702] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 10/05/2020] [Accepted: 10/06/2020] [Indexed: 05/07/2023]
Abstract
We present a multi-speckle diffuse correlation spectroscopy (DCS) system for measuring cerebral blood flow in the healthy adult human brain. In contrast to the need for a high frame rate camera to measure the multi-speckle intensity auto-correlation, we employ a low frame rate camera to measure the auto-correlation using the recently introduced multi-step volterra integral method (MVIM). The results are validated by comparison against the blood flow measured using standard DCS system.
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Murali K, Nandakumaran AK, Varma HM. On the equivalence of speckle contrast-based and diffuse correlation spectroscopy methods in measuring in vivo blood flow. OPTICS LETTERS 2020; 45:3993-3996. [PMID: 32667336 DOI: 10.1364/ol.397979] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 06/22/2020] [Indexed: 05/23/2023]
Abstract
We establish the equivalence between laser speckle contrast-based and diffuse correlation spectroscopy methods inin vivo imaging of blood flow using the Volterra integral equation theory. We further substantiate the need of regularized fitting while employing the multiexposure speckle contrast imaging to recover autocorrelation function.
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