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Wang Q, Pan M, Kreiss L, Samaei S, Carp SA, Johansson JD, Zhang Y, Wu M, Horstmeyer R, Diop M, Li DDU. A comprehensive overview of diffuse correlation spectroscopy: Theoretical framework, recent advances in hardware, analysis, and applications. Neuroimage 2024; 298:120793. [PMID: 39153520 DOI: 10.1016/j.neuroimage.2024.120793] [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: 05/19/2024] [Revised: 07/23/2024] [Accepted: 08/14/2024] [Indexed: 08/19/2024] Open
Abstract
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.
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Affiliation(s)
- Quan Wang
- Department of Biomedical Engineering, Faculty of Engineering, University of Strathclyde, Glasgow, United Kingdom
| | - Mingliang Pan
- Department of Biomedical Engineering, Faculty of Engineering, University of Strathclyde, Glasgow, United Kingdom
| | - Lucas Kreiss
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Saeed Samaei
- Department of Medical and Biophysics, Schulich School of Medical & Dentistry, Western University, London, Ontario, Canada; Lawson Health Research Institute, Imaging Program, London, Ontario, Canada
| | - Stefan A Carp
- Massachusetts General Hospital, Optics at Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Charlestown, MA, United States
| | | | - Yuanzhe Zhang
- Department of Biomedical Engineering, Faculty of Engineering, University of Strathclyde, Glasgow, United Kingdom
| | - Melissa Wu
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Roarke Horstmeyer
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Mamadou Diop
- Department of Medical and Biophysics, Schulich School of Medical & Dentistry, Western University, London, Ontario, Canada; Lawson Health Research Institute, Imaging Program, London, Ontario, Canada
| | - David Day-Uei Li
- Department of Biomedical Engineering, Faculty of Engineering, University of Strathclyde, Glasgow, United Kingdom.
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Baker WB, Forti RM, Heye P, Heye K, Lynch JM, Yodh AG, Licht DJ, White BR, Hwang M, Ko TS, Kilbaugh TJ. Modified Beer-Lambert algorithm to measure pulsatile blood flow, critical closing pressure, and intracranial hypertension. BIOMEDICAL OPTICS EXPRESS 2024; 15:5511-5532. [PMID: 39296411 PMCID: PMC11407241 DOI: 10.1364/boe.529150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 08/12/2024] [Accepted: 08/12/2024] [Indexed: 09/21/2024]
Abstract
We introduce a frequency-domain modified Beer-Lambert algorithm for diffuse correlation spectroscopy to non-invasively measure flow pulsatility and thus critical closing pressure (CrCP). Using the same optical measurements, CrCP was obtained with the new algorithm and with traditional nonlinear diffusion fitting. Results were compared to invasive determination of intracranial pressure (ICP) in piglets (n = 18). The new algorithm better predicted ICP elevations; the area under curve (AUC) from logistic regression analysis was 0.85 for ICP ≥ 20 mmHg. The corresponding AUC for traditional analysis was 0.60. Improved diagnostic performance likely results from better filtering of extra-cerebral tissue contamination and measurement noise.
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Affiliation(s)
- Wesley B Baker
- Division of Neurology, Department of Pediatrics, Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Rodrigo M Forti
- Division of Neurology, Department of Pediatrics, Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Pascal Heye
- Division of General, Thoracic and Fetal Surgery, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Kristina Heye
- Division of Neurology, Department of Pediatrics, Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jennifer M Lynch
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Arjun G Yodh
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Daniel J Licht
- Division of Neurology, Department of Pediatrics, Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Prenatal Pediatrics, Children's National, Washington DC, USA
| | - Brian R White
- Division of Pediatric Cardiology, Department of Pediatrics, Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Misun Hwang
- Department of Radiology, Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Tiffany S Ko
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Todd J Kilbaugh
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Wang Q, Pan M, Zang Z, Li DDU. Quantification of blood flow index in diffuse correlation spectroscopy using a robust deep learning method. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:015004. [PMID: 38283935 PMCID: PMC10821781 DOI: 10.1117/1.jbo.29.1.015004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 12/22/2023] [Accepted: 01/02/2024] [Indexed: 01/30/2024]
Abstract
Significance Diffuse correlation spectroscopy (DCS) is a powerful, noninvasive 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 μ a and reduced scattering coefficient μ 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 network (DCS-NET), for BFi and coherent factor (β ) 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 β 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 μ a and μ 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. Conclusions DCS-NET can robustly quantify blood flow measurements at considerable source-detector distances, corresponding to much deeper biological tissues. It has excellent potential for hardware implementation, promising continuous real-time blood flow measurements.
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Affiliation(s)
- Quan Wang
- University of Strathclyde, Department of Biomedical Engineering, Faculty of Engineering, Glasgow, United Kingdom
| | - Mingliang Pan
- University of Strathclyde, Department of Biomedical Engineering, Faculty of Engineering, Glasgow, United Kingdom
| | - Zhenya Zang
- University of Strathclyde, Department of Biomedical Engineering, Faculty of Engineering, Glasgow, United Kingdom
| | - David Day-Uei Li
- University of Strathclyde, Department of Biomedical Engineering, Faculty of Engineering, Glasgow, United Kingdom
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Zhao H, Sathialingam E, Cowdrick KR, Urner T, Lee SY, Bai S, Akbik F, Samuels OB, Kandiah P, Sadan O, Buckley EM. Comparison of diffuse correlation spectroscopy analytical models for measuring cerebral blood flow in adults. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:126005. [PMID: 38107767 PMCID: PMC10723621 DOI: 10.1117/1.jbo.28.12.126005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 10/30/2023] [Accepted: 11/21/2023] [Indexed: 12/19/2023]
Abstract
Significance Although multilayer analytical models have been proposed to enhance brain sensitivity of diffuse correlation spectroscopy (DCS) measurements of cerebral blood flow, the traditional homogeneous model remains dominant in clinical applications. Rigorous in vivo comparison of these analytical models is lacking. Aim We compare the performance of different analytical models to estimate a cerebral blood flow index (CBFi) with DCS in adults. Approach Resting-state data were obtained on a cohort of 20 adult patients with subarachnoid hemorrhage. Data at 1 and 2.5 cm source-detector separations were analyzed with the homogenous, two-layer, and three-layer models to estimate scalp blood flow index and CBFi. The performance of each model was quantified via fitting convergence, fit stability, brain-to-scalp flow ratio (BSR), and correlation with transcranial Doppler ultrasound (TCD) measurements of cerebral blood flow velocity in the middle cerebral artery (MCA). Results The homogeneous model has the highest pass rate (100%), lowest coefficient of variation (CV) at rest (median [IQR] at 1 Hz of 0.18 [0.13, 0.22]), and most significant correlation with MCA blood flow velocities (R s = 0.59 , p = 0.010 ) compared with both the two- and three-layer models. The multilayer model pass rate was significantly correlated with extracerebral layer thicknesses. Discarding datasets with non-physiological BSRs increased the correlation between DCS measured CBFi and TCD measured MCA velocities for all models. Conclusions We found that the homogeneous model has the highest pass rate, lowest CV at rest, and most significant correlation with MCA blood flow velocities. Results from the multilayer models should be taken with caution because they suffer from lower pass rates and higher coefficients of variation at rest and can converge to non-physiological values for CBFi. Future work is needed to validate these models in vivo, and novel approaches are merited to improve the performance of the multimodel models.
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Affiliation(s)
- Hongting Zhao
- Emory University, Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Eashani Sathialingam
- Emory University, Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Kyle R. Cowdrick
- Emory University, Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Tara Urner
- Emory University, Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Seung Yup Lee
- Emory University, Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
- Kennesaw State University, Department of Electrical and Computer Engineering, Marietta, Georgia, United States
| | - Shasha Bai
- Emory University, School of Medicine, Department of Pediatrics, Atlanta, Georgia, United States
| | - Feras Akbik
- Emory University, School of Medicine, Department of Neurology and Neurosurgery, Division of Neurocritical Care, Atlanta, Georgia, United States
| | - Owen B. Samuels
- Emory University, School of Medicine, Department of Neurology and Neurosurgery, Division of Neurocritical Care, Atlanta, Georgia, United States
| | - Prem Kandiah
- Emory University, School of Medicine, Department of Neurology and Neurosurgery, Division of Neurocritical Care, Atlanta, Georgia, United States
| | - Ofer Sadan
- Emory University, School of Medicine, Department of Neurology and Neurosurgery, Division of Neurocritical Care, Atlanta, Georgia, United States
| | - Erin M. Buckley
- Emory University, Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
- Emory University, School of Medicine, Department of Pediatrics, Atlanta, Georgia, United States
- Children’s Healthcare of Atlanta, Children’s Research Scholar, Atlanta, Georgia, United States
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Forti RM, Hobson LJ, Benson EJ, Ko TS, Ranieri NR, Laurent G, Weeks MK, Widmann NJ, Morton S, Davis AM, Sueishi T, Lin Y, Wulwick KS, Fagan N, Shin SS, Kao SH, Licht DJ, White BR, Kilbaugh TJ, Yodh AG, Baker WB. Non-invasive diffuse optical monitoring of cerebral physiology in an adult swine-model of impact traumatic brain injury. BIOMEDICAL OPTICS EXPRESS 2023; 14:2432-2448. [PMID: 37342705 PMCID: PMC10278631 DOI: 10.1364/boe.486363] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/17/2023] [Accepted: 04/12/2023] [Indexed: 06/23/2023]
Abstract
In this study, we used diffuse optics to address the need for non-invasive, continuous monitoring of cerebral physiology following traumatic brain injury (TBI). We combined frequency-domain and broadband diffuse optical spectroscopy with diffuse correlation spectroscopy to monitor cerebral oxygen metabolism, cerebral blood volume, and cerebral water content in an established adult swine-model of impact TBI. Cerebral physiology was monitored before and after TBI (up to 14 days post injury). Overall, our results suggest that non-invasive optical monitoring can assess cerebral physiologic impairments post-TBI, including an initial reduction in oxygen metabolism, development of cerebral hemorrhage/hematoma, and brain swelling.
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Affiliation(s)
- Rodrigo M. Forti
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Resuscitation Science Center of Emphasis, CHOP Research Institute, Philadelphia, PA 19104, USA
| | - Lucas J. Hobson
- Resuscitation Science Center of Emphasis, CHOP Research Institute, Philadelphia, PA 19104, USA
- Department of Anesthesiology and Critical Care Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Emilie J. Benson
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tiffany S. Ko
- Resuscitation Science Center of Emphasis, CHOP Research Institute, Philadelphia, PA 19104, USA
- Department of Anesthesiology and Critical Care Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Nicolina R. Ranieri
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Resuscitation Science Center of Emphasis, CHOP Research Institute, Philadelphia, PA 19104, USA
| | - Gerard Laurent
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Resuscitation Science Center of Emphasis, CHOP Research Institute, Philadelphia, PA 19104, USA
| | - M. Katie Weeks
- Resuscitation Science Center of Emphasis, CHOP Research Institute, Philadelphia, PA 19104, USA
- Department of Anesthesiology and Critical Care Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Nicholas J. Widmann
- Resuscitation Science Center of Emphasis, CHOP Research Institute, Philadelphia, PA 19104, USA
- Department of Anesthesiology and Critical Care Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Sarah Morton
- Resuscitation Science Center of Emphasis, CHOP Research Institute, Philadelphia, PA 19104, USA
- Department of Anesthesiology and Critical Care Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Anthony M. Davis
- Resuscitation Science Center of Emphasis, CHOP Research Institute, Philadelphia, PA 19104, USA
- Department of Anesthesiology and Critical Care Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Takayuki Sueishi
- Resuscitation Science Center of Emphasis, CHOP Research Institute, Philadelphia, PA 19104, USA
- Department of Anesthesiology and Critical Care Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Yuxi Lin
- Resuscitation Science Center of Emphasis, CHOP Research Institute, Philadelphia, PA 19104, USA
- Department of Anesthesiology and Critical Care Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Karli S. Wulwick
- Resuscitation Science Center of Emphasis, CHOP Research Institute, Philadelphia, PA 19104, USA
- Department of Anesthesiology and Critical Care Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Nicholas Fagan
- Resuscitation Science Center of Emphasis, CHOP Research Institute, Philadelphia, PA 19104, USA
- Department of Anesthesiology and Critical Care Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Samuel S. Shin
- Resuscitation Science Center of Emphasis, CHOP Research Institute, Philadelphia, PA 19104, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shih-Han Kao
- Resuscitation Science Center of Emphasis, CHOP Research Institute, Philadelphia, PA 19104, USA
- Department of Anesthesiology and Critical Care Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Daniel J. Licht
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Brian R. White
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Division of Cardiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Todd J. Kilbaugh
- Resuscitation Science Center of Emphasis, CHOP Research Institute, Philadelphia, PA 19104, USA
- Department of Anesthesiology and Critical Care Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Arjun G. Yodh
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Wesley B. Baker
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Resuscitation Science Center of Emphasis, CHOP Research Institute, Philadelphia, PA 19104, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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