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Bauer AQ, Gibson EA, Wang H, Srinivasan VJ. Introduction to the Optics and the Brain 2023 feature issue. BIOMEDICAL OPTICS EXPRESS 2024; 15:2110-2113. [PMID: 38633102 PMCID: PMC11019680 DOI: 10.1364/boe.517678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Indexed: 04/19/2024]
Abstract
A feature issue is being presented by a team of guest editors containing papers based on contributed submissions including studies presented at Optics and the Brain, held April 24-27, 2023 as part of Optica Biophotonics Congress: Optics in the Life Sciences, in Vancouver, Canada.
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Affiliation(s)
- Adam Q. Bauer
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Emily A. Gibson
- Department of Bioengineering, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colorado 80045, USA
| | - Hui Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA 02129, USA
| | - Vivek J. Srinivasan
- Tech4Health Institute, NYU Langone Health, New York, New York 10010, USA
- Department of Ophthalmology, NYU Langone Health, New York, New York 10017, USA
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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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