Pith. sign in

REVIEW 3 major objections 6 minor 88 references

Comparison of diffuse correlation spectroscopy analytical models for cerebral blood flow measurements

T0 review · 3 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The two-layer DCS model best balances accuracy and robustness for CBFi and rCBFi recovery at 20–35 mm, while the three-layer model alone recovers CBFi, SBFi, and rCBFi together.

desk verdict Useful systematic DCS model comparison, but the ranking is biased by in-sample fitting-strategy selection and the abstract overclaims a parameter-error analysis. read the letter →

arxiv 2507.21853 v1 pith:RJ7DSCXK submitted 2025-07-29 physics.med-ph physics.optics

Mingliang Pan , Quan Wang , Yuanzhe Zhang , David Day-Uei Li This is my paper
classification physics.med-phphysics.optics
keywords diffusecorrelationspectroscopycerebralbloodflowmulti-layertissuemodelMonteCarlosimulationindexsource-detectorseparationautocorrelationfunctionselection
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish which analytical model for diffuse correlation spectroscopy (DCS) should be used to measure cerebral blood flow index (CBFi) from the head's surface. It compares the standard semi-infinite model with two-layer and three-layer models that account for contamination from the scalp and skull, using Monte Carlo simulations of a four-layer head phantom as ground truth. The paper claims that the two-layer model offers the best balance of accuracy and robustness for both absolute CBFi and relative CBFi (rCBFi) across all tested source-detector separations (20–35 mm), while the three-layer model is the only one that simultaneously recovers CBFi, scalp blood flow index (SBFi), and rCBFi. It also finds that the semi-infinite model is only reliable for rCBFi at separations of 30 mm or more. A reader should care because choosing the wrong model or fitting strategy can distort cerebral blood flow measurements, and this work gives concrete selection guidance.

What carries the argument

The paper's engine is the correlation diffusion equation for the electric-field autocorrelation function $G_1(\mathbf{r},\tau)$, whose normalized form $g_1$ is linked to the measured intensity autocorrelation function through the Siegert relation $g_2(\tau)=1+\beta|g_1(\tau)|^2$. Three analytical Green's-function solutions are used as forward models: a semi-infinite homogeneous medium, a two-layer slab (scalp and skull over the brain), and a three-layer slab (scalp, skull, and a semi-infinite brain). The fitted parameters are blood flow indices of the form BFi $=\alpha D_B$, and the paper selects an optimal fitting strategy for each model: early-time-lag fitting with fixed $\beta$ for the semi-infinite model, single-distance fitting with fixed $\beta$ for the two-layer model, and multi-distance fitting for the three-layer model. The ground-truth data come from a Monte Carlo simulation of a four-layer slab head, with realistic Gaussian noise added through a published DCS noise model; this ground truth allows the paper to quantify sensitivity, contamination, and accuracy across source-detector separations of 20 to 35 mm.

What would settle it

Re-run the comparison with subject-specific head geometries obtained from magnetic resonance imaging, or with a two-layer liquid phantom whose top- and bottom-layer flows are controlled independently, across source-detector separations of 20, 25, 30, and 35 mm; if the two-layer single-distance fit no longer recovers CBFi and rCBFi more accurately than the three-layer multi-distance fit, or if the semi-infinite model matches the two-layer model for rCBFi at 30 mm and beyond, the central ranking would be contradicted.

Watch

Extended reading notes

Core claim

The central claim is that multi-layer DCS analytical models nearly double CBFi sensitivity compared with the semi-infinite model, reaching approximately 100% versus 36.8% at a 30 mm source-detector separation, but that the best model depends on what quantity is being measured. The semi-infinite model, when processed with early-time-lag fitting and a fixed coherence factor, underestimates absolute CBFi and should be used only for relative CBFi recovery at larger separations (≥30 mm). The two-layer model, using single-distance fitting with a fixed coherence factor, recovers both CBFi and rCBFi accurately across all tested separations of 20–35 mm and is the most resistant to blood flow changes in the scalp and skull. The three-layer model, using multi-distance fitting, is the only tested model that simultaneously recovers CBFi, SBFi, and rCBFi, but it is far more computationally expensive (about 35,000 seconds per 500 samples) and remains sensitive to extracerebral blood flow changes. The semi-infinite model and a simplified single-exponential fit are fast enough for real-time monitoring, processing 500 samples in under half a second.

Load-bearing premise

The ranking depends on treating the four-layer slab Monte Carlo simulation, with fixed layer thicknesses (scalp 5 mm, skull 7 mm, CSF 2 mm) and a low assumed skull blood flow (8e-8 mm2/s), as a faithful stand-in for the real adult head; if those tissue values are wrong, the model ranking could change.

Editorial extensions

If this is right

  • For absolute CBFi measurements at source-detector separations of 20–35 mm, the two-layer model with single-distance fitting and a fixed coherence factor is the recommended choice; the semi-infinite model underestimates CBFi and should be avoided.
  • For relative CBFi only, the semi-infinite model with early-time-lag fitting, or even a single-exponential fit, is adequate at separations of 30 mm or more and is the only tested option fast enough for real-time monitoring.
  • The three-layer model with multi-distance fitting is the only one that simultaneously recovers CBFi, SBFi, and rCBFi, but it costs about 35,000 seconds per 500 samples and remains sensitive to scalp and skull blood flow changes.
  • Errors in assumed optical properties and layer thicknesses barely affect rCBFi in any of the models, so relative measurements need less prior anatomical information than absolute ones.
  • Multi-layer models increase CBFi sensitivity from about 37% to near 100% at 30 mm separation, implying that the standard semi-infinite analysis can miss a large portion of cerebral blood flow changes.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the ranking rests on one simulated head geometry (5 mm scalp, 7 mm skull, 2 mm CSF) and a small assumed skull blood flow, the two-layer advantage may not carry over to infants, skull defects, or conditions that alter scalp and skull thickness; repeating the comparison with varied thicknesses is a natural next test.
  • The result that a simpler single-distance, fixed-beta fit outperforms multi-distance fitting for the two-layer model suggests that adding fitting dimensions can trade accuracy for bias, a trade-off that may generalize to other layered optical measurements.
  • The near-equivalence of single-exponential and semi-infinite early-time-lag fitting at 30 mm implies that rCBFi monitors could drop model parameters entirely and rely on decorrelation speed, a simplification that could be tested directly on existing clinical hypercapnia datasets.
  • The fixed noise setting (10 kcps at 30 mm, 60 s integration) was chosen as one realistic point; lower photon counts or shorter integration times would likely worsen the large-separation instability seen in two-layer and three-layer fits, potentially shrinking the separation range over which the paper's ranking holds.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. This paper compares three analytical models for diffuse correlation spectroscopy (semi-infinite, two-layer, and three-layer) for estimating cerebral blood flow index (CBFi) and relative CBFi (rCBFi). The authors generate synthetic DCS data with a four-layer slab head model using the Monte Carlo eXtreme (MCX) toolkit, add realistic Gaussian noise validated against a milk-phantom experiment, and then evaluate multiple fitting strategies for each model: ETLR fitting with fixed or variable beta for the semi-infinite model, and single-distance (SD) versus multi-distance (MD) fitting for the two- and three-layer models. They assess CBFi sensitivity, absolute and relative CBFi recovery accuracy, crosstalk from scalp and skull blood flow variations, sensitivity to assumed parameter errors, and computational cost across source-detector separations of 20 to 35 mm. The optimal strategies are reported as ETLR with fixed beta for the semi-infinite model, SD with fixed beta for the two-layer model, and MD for the three-layer model. The central conclusions are that the two-layer model offers the best balance of accuracy and robustness for both CBFi and rCBFi, the three-layer model enables simultaneous recovery of CBFi, scalp BFi, and rCBFi, and the semi-infinite model is suitable only for rCBFi recovery at larger separations (>=30 mm).

Significance. The paper addresses a practical gap in DCS: there is no systematic, head-to-head comparison of the semi-infinite, two-layer, and three-layer analytical models, nor clear guidance on model selection and fitting strategies. The study is carefully designed: the Monte Carlo ground truth is generated independently of the analytical models, the noise model is validated against experimental phantom data, and the performance metrics (CBFi sensitivity, rCBFi recovery, crosstalk, computation time) are directly relevant to clinical and research users. The finding that multi-layer models nearly double CBFi sensitivity (up to ~100%, versus 36.8% for the semi-infinite model at 30 mm) is a useful quantitative benchmark, and the recommendation of a single-exponential alternative for real-time rCBFi monitoring is practical and falsifiable. However, the central model-ranking claim is weakened by the fact that each model's 'optimal' fitting strategy is selected on the same simulated dataset used for the subsequent performance comparison; this in-sample selection procedure could bias the relative ranking.

major comments (3)
  1. [Sec. 3.1 and Figs. 5-9, Table 4] The choice of 'optimal' fitting strategy for each model is made by comparing strategies on the same simulated dataset that is subsequently used to rank the models. Sec. 3.1 concludes 'ETLR fitting with beta fixed is optimal for the semi-infinite model, SD fitting with beta fixed is preferable for the two-layer model, and MD fitting is best suited for the three-layer model,' and all later comparisons use these selected strategies. Because the strategies are chosen for best CBFi accuracy on the evaluation data, the reported model ranking is a comparison of 'model plus strategy tuned to this test set,' not a comparison of the models on an equal footing. This is load-bearing for the central claim that the two-layer model best balances accuracy and robustness. A concrete illustration is that two-layer MD fitting was rejected because it overestimates rCBFi (Fig. S3), yet three-layer MD fitting was adopted; had a different, equally plausible strategy been selected for the two-layer model, the comparison at large separations could change. I recommend validating the selected strategies on a held-out MC dataset (e.g., different noise realizations or perturbed tissue parameters) or performing a sensitivity analysis showing that the ranking is robust to this selection. Without such evidence, the relative merits of the models are not established independently of the fitting-strategy choice.
  2. [Sec. 2.6, Sec. 3.5, Table 1, Abstract] The manuscript claims, in the abstract and in Table 1, that 'sensitivity to assumed parameter errors' was assessed in this work, but Sec. 2.6 explicitly states that 'we provide only a brief review of this topic in the present work and do not perform a quantitative analysis.' Sec. 3.5 is likewise a literature review, not a new evaluation. Because this is one of the six performance dimensions listed as a contribution of the paper, the claimed scope exceeds what is actually delivered. This is a load-bearing inconsistency with respect to the stated aims, and it should be fixed either by adding a quantitative perturbation analysis (varying, e.g., mu_a, mu_s', or layer thicknesses for each model) or by removing the claim from the abstract, the introduction, and Table 1.
  3. [Sec. 2.2, Sec. 4 (Discussion, limitations)] The entire model comparison is based on a single set of assumed tissue parameters in the four-layer slab simulation (scalp 5 mm, skull 7 mm, CSF 2 mm, and skull DB = 8e-8 mm2/s, among others). The paper itself acknowledges in the Discussion that 'the thicknesses of the extracerebral layers were kept constant' and that 'variations in layer thickness can influence brain sensitivity at different rho values.' However, the central recommendation that the two-layer model offers the best balance of accuracy and robustness across all tested separations is derived from this one parameter set. Given that real adult heads vary in scalp and skull thickness and in skull blood flow, the ranking could change under different but still physiologically plausible parameters. I recommend adding a small parameter-sweep study, at least for scalp/skull thickness and skull DB, to demonstrate that the reported ranking is stable. Without such a robustness check, the generalizability of the model-selection guidance is uncertain, although this is a limitation rather than an internal inconsistency.
minor comments (6)
  1. [Sec. 2.3] The noise model in Eq. (5) depends on the coherence factor beta, which is fixed to 0.5 when generating the simulated g2 curves. It would be helpful to state explicitly that the same beta is used in the noise model and in the fixed-beta fits, to avoid ambiguity in the role of beta as a 'preknown' parameter (Table 3).
  2. [Sec. 3.6 / Fig. 10] The comparison between semi-infinite analytical fitting and single-exponential fitting is presented only for rho = 30 mm. Since the paper later recommends the semi-infinite model for rCBFi at rho >= 30 mm, including rho = 35 mm in the Bland-Altman analysis would make the recommendation more complete.
  3. [Sec. 2.5] The sentence defining a negative sensitivity appears twice in essentially identical form ('A negative sensitivity indicates that the recovered perturbed CBFi decreases...'). One of the two duplicate sentences should be removed.
  4. [Sec. 2.4 / Eq. (9)] There is a typographical error in the main text: 'fminsearchbnd function was used to to minimize the penalty function' should read 'to minimize'.
  5. [Table 3] For the three-layer model, the preknown parameters are listed as including L_scalp and L_skull, but the values (5 mm and 7 mm) are only given in Table 2. It would be clearer to repeat these values in Table 3 or to refer explicitly to Table 2.
  6. [Sec. 2.2] The CSF layer has mu_s = 0.002 mm^-1, which is extremely low; the authors cite prior work (Custo et al., Zhao and Buckley) to justify including it. It would strengthen the paper to add a brief note in Sec. 2.2 that this low-scattering layer may challenge the diffusion approximation, and to refer to the supplementary derivation where the analytical models assume diffusive layers.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the MC ground truth is external to the analytical models. Minor in-sample strategy selection is acknowledged as a limitation, and one self-citation is non-load-bearing.

full rationale

The derivation is self-contained as a simulation benchmark. Ground truth is generated by an independent four-layer MCX simulation (Sec. 2.2), and all three analytical models are fitted to these synthetic g2 curves with known parameters; recovery accuracy, rCBFi, sensitivity, and crosstalk are scored against that independent ground truth, not against the fitted parameters themselves. The only in-sample element is the choice of fitting strategy in Sec. 3.1, where 'ETLR fitting with β fixed is optimal for the semi-infinite model, SD fitting with β fixed is preferable for the two-layer model, and MD fitting is best suited for the three-layer model' is decided on the same dataset later used for comparisons. This is a selection-bias limitation rather than circularity, because the MC ground truth is external to the analytical models and the paper explicitly acknowledges the issue in Sec. 4: 'we selected MD fitting for the three-layer model because it provides the most accurate estimates of both CBFi and SBFi. However, three-layer MD fitting tends to be sensitive to BFi changes in the extracerebral layers. To further evaluate the three-layer model’s ability to separate extracerebral confounders, a comparison using three-layer SD fitting should also be conducted.' The only self-citation (ref. 82, Pan et al.) supports a review statement about parameter-error effects on rCBFi; the same conclusion for the three-layer model is independently attributed to Zhao et al., and it is not load-bearing for the central ranking. No fitted parameter is renamed as a prediction, and no uniqueness or ansatz is imported from the authors' prior work. Overall circularity is minor.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central comparison rests on the simulated four-layer MC dataset being a faithful proxy for the human head. The analytical models are taken from prior literature without new derivation. The free parameters are analysis choices (ETLR cutoff, noise assumptions) rather than hidden fitted quantities. No new physical entities are introduced.

free parameters (3)
  • ETLR window = tau <= 30 us
    Hand-chosen early time lag cutoff for semi-infinite and single-exponential fitting; affects CBFi sensitivity differently at each source-detector separation (Sec 2.4, Fig. 1).
  • Reference photon count rate = 10 kcps at rho = 30 mm
    Assumed count rate used to set noise levels at all separations, scaled by the Green's function G1(0); impacts SNR and therefore all fit variability comparisons (Sec 2.3).
  • Integration time = 60 s
    Assumed for the noise model; affects noise magnitude and thus the robustness comparisons (Sec 2.3).
assumptions (5)
  • standard math The correlation diffusion equation (CDE) with extrapolated boundary conditions accurately models DCS light transport in layered tissue.
    Used as the basis for all three analytical models; derivation is prior literature (Supplementary Material, refs 45-48).
  • domain assumption Brownian motion model for scatterer dynamics with alpha = 1, so BFi = DB.
    Invoked in Sec 2.1 to relate mean square displacement to the fitted BFi; accepted in prior DCS work.
  • domain assumption The four-layer slab with the specified optical properties and thicknesses faithfully represents the human head for DCS evaluation.
    The MC ground truth is generated with this model (Table 2), and all conclusions depend on its fidelity; fixed thicknesses are acknowledged as a limitation.
  • domain assumption The CSF layer has minor influence on brain sensitivity and rCBFi recovery, justifying grouping it with the brain layer in the two- and three-layer fits.
    Stated in Sec 2.2, relying on Custo et al. and Zhao et al.; if incorrect, the layer mismatch between simulation and fitted models could bias the comparison.
  • domain assumption The Gaussian noise model of Zhou et al. with hardware-derived bin widths reproduces experimental DCS noise.
    Adopted in Sec 2.3 and validated on a milk phantom (Supplementary Fig. S2); underpins all uncertainty estimates and robustness rankings.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Comparison of diffuse correlation spectroscopy analytical models for cerebral blood flow measurements." pith.science (2026). https://pith.science/paper/RJ7DSCXK

@misc{pith2026250721853,
  author       = {Pith},
  title        = {Pith review of: Comparison of diffuse correlation spectroscopy analytical models for cerebral blood flow measurements},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RJ7DSCXK}},
  note         = {Machine review of arXiv:2507.21853}
}
read the original abstract

Multi-layer diffuse correlation spectroscopy (DCS) models have been developed to reduce the contamination of superficial signals in cerebral blood flow index (CBFi) measurements. However, a systematic comparison of these models and clear guidance on model selection are still lacking. This study compares three DCS analytical models: semi-infinite, two-layer, and three-layer, focusing on their fitting strategies, performance, and suitability for CBFi and relative CBFi (rCBFi) estimation. We simulated DCS data using a four-layer slab head model with the Monte Carlo eXtreme (MCX) toolkit. Multiple fitting strategies were evaluated: early time lag range (ETLR) fitting with fixed or variable beta for the semi-infinite model, and single-distance (SD) and multi-distance (MD) fitting for the two- and three-layer models. Model performance was assessed based on CBFi sensitivity, accuracy of CBFi and rCBFi recovery, resistance to signal contamination from scalp and skull, sensitivity to assumed parameter errors, and computational efficiency across source-detector separations of 20 to 35 mm. Optimal fitting methods include ETLR with fixed beta for the semi-infinite model, SD with fixed beta for the two-layer model, and MD for the three-layer model. The multi-layer models achieved higher CBFi sensitivity (up to 100%) compared to 36.8% for the semi-infinite model. The two-layer model offered the best balance of accuracy and robustness, while the three-layer model enabled simultaneous recovery of CBFi, scalp BFi, and rCBFi. The semi-infinite model was the most computationally efficient, requiring only 0.38 seconds for 500 samples, supporting its use in real-time monitoring. This work offers a practical and systematic evaluation of DCS analytical models and provides guidance for selecting the most appropriate model based on application needs.

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

88 extracted references · 76 canonical work pages

  1. [1]

    Heart Metabolic Disturbances in Cardiovascular Diseases,

    K. Carvajal and R. Moreno -Sánchez, “Heart Metabolic Disturbances in Cardiovascular Diseases,” Archives of Medical Research 34(2), 89–99 (2003) [doi:10.1016/S0188-4409(03)00004-3]

    work page doi:10.1016/s0188-4409(03)00004-3 2003

  2. [2]

    Diffuse correla�on spectroscopy for non -invasive, micro -vascular cerebral blood flow measurement,

    T. Durduran and A. G. Yodh, “Diffuse correla�on spectroscopy for non -invasive, micro -vascular cerebral blood flow measurement,” NeuroImage 85, 51 –63 (2014) [doi:10.1016/j.neuroimage.2013.06.017]

    work page doi:10.1016/j.neuroimage.2013.06.017 2014

  3. [3]

    Scatering and Imaging with Diffusing Temporal Field Correla�ons,

    D. A. Boas, L. E. Campbell, and A. G. Yodh, “Scatering and Imaging with Diffusing Temporal Field Correla�ons,” Phys. Rev. Let. 75(9), 1855–1858 (1995) [doi:10.1103/PhysRevLet.75.1855]

    work page doi:10.1103/physrevlet.75.1855 1995

  4. [4]

    Inves�ga�on of diffuse correla�on spectroscopy in mul� -layered media including the human head,

    L. Gagnon et al., “Inves�ga�on of diffuse correla�on spectroscopy in mul� -layered media including the human head,” Opt. Express 16(20), 15514 (2008) [doi:10.1364/OE.16.015514]

    work page doi:10.1364/oe.16.015514 2008

  5. [5]

    F a s t b l o o d fl o w m o n i t o r i n g i n d e e p � s s u e s w i t h r e a l-�me so�ware correlators,

    D . W a n g e t a l . , “ F a s t b l o o d fl o w m o n i t o r i n g i n d e e p � s s u e s w i t h r e a l-�me so�ware correlators,” Biomed. Opt. Express 7(3), 776 (2016) [doi:10.1364/BOE.7.000776]

    work page doi:10.1364/boe.7.000776 2016

  6. [6]

    Influence of oversimplifying the head anatomy on cerebral blood flow measurements with diffuse correla�on spectroscopy,

    H. Zhao and E. M. Buckley, “Influence of oversimplifying the head anatomy on cerebral blood flow measurements with diffuse correla�on spectroscopy,” NPh 10(1), 015010, SPIE (2023) [doi:10.1117/1.NPh.10.1.015010]

    work page doi:10.1117/1.nph.10.1.015010 2023

  7. [7]

    Cerebral Blood Flow Response to Hypercapnia in Children with Obstruc�ve Sleep Apnea Syndrome,

    D. R. Busch et al., “Cerebral Blood Flow Response to Hypercapnia in Children with Obstruc�ve Sleep Apnea Syndrome,” Sleep 39(1), 209–216 (2016) [doi:10.5665/sleep.5350]

    work page doi:10.5665/sleep.5350 2016

  8. [8]

    Preictal neuronal and vascular ac�vity precedes the onset of childhood absence seizure: direct current poten�al shi�s and their correla�on with hemodynamic ac�vity,

    M. Nourhashemi et al., “Preictal neuronal and vascular ac�vity precedes the onset of childhood absence seizure: direct current poten�al shi�s and their correla�on with hemodynamic ac�vity,” NPh 10(2), 025005, SPIE (2023) [doi:10.1117/1.NPh.10.2.025005]

    work page doi:10.1117/1.nph.10.2.025005 2023

Show all 88 references
  1. [9]

    Quan�fying the Cerebral Hemometabolic Response to Blood Transfusion in Pediatric Sickle Cell Disease With Diffuse Op�cal Spectroscopies,

    S. Y . Lee et al., “Quan�fying the Cerebral Hemometabolic Response to Blood Transfusion in Pediatric Sickle Cell Disease With Diffuse Op�cal Spectroscopies,” Front. Neurol. 13, Fron�ers (2022) [doi:10.3389/fneur.2022.869117]

    2022

  2. [10]

    Transcranial op�cal monitoring of cerebrovascular hemodynamics in acute stroke pa�ents,

    T. Durduran et al., “Transcranial op�cal monitoring of cerebrovascular hemodynamics in acute stroke pa�ents,” Opt. Express 17(5), 3884 (2009) [doi:10.1364/OE.17.003884]

    2009 doi

  3. [11]

    Diffuse op�cal characteriza�on of an exercising pa�ent group with peripheral artery disease,

    R. C. Mesquita et al., “Diffuse op�cal characteriza�on of an exercising pa�ent group with peripheral artery disease,” JBO 18(5), 057007, SPIE (2013) [doi:10.1117/1.JBO.18.5.057007]

    2013 doi

  4. [12]

    Intraopera�ve Cerebral Hemodynamic Monitoring during Caro�d Endarterectomy via Diffuse Correla�on Spectroscopy and Near -Infrared Spectroscopy,

    K. Kaya et al., “Intraopera�ve Cerebral Hemodynamic Monitoring during Caro�d Endarterectomy via Diffuse Correla�on Spectroscopy and Near -Infrared Spectroscopy,” 8, Brain Sciences 12(8), 1025, Mul�disciplinary Digital Publishing Ins�tute (2022) [doi:10.3390/brainsci12081025]

    2022 doi

  5. [13]

    Comparison of op�cal measurements of cri�cal closing pressure acquired before and during induced ventricular arrhythmia in adults,

    A. Lafontant et al., “Comparison of op�cal measurements of cri�cal closing pressure acquired before and during induced ventricular arrhythmia in adults,” NPh 9(3), 035004, SPIE (2022) [doi:10.1117/1.NPh.9.3.035004]

    2022 doi

  6. [14]

    Dual- task related frontal cerebral blood flow changes in older adults with mild cogni�ve impairment: A func�onal diffuse correla�on spectroscopy study,

    C. Udina et al., “Dual- task related frontal cerebral blood flow changes in older adults with mild cogni�ve impairment: A func�onal diffuse correla�on spectroscopy study,” Front. Aging Neurosci. 14, Fron�ers (2022) [doi:10.3389/fnagi.2022.958656]

    2022

  7. [15]

    Agreement in cerebrovascular reac�vity assessed with diffuse correla�on spectroscopy across experimental paradigms improves with short separa�on regression,

    K. R. Cowdrick et al., “Agreement in cerebrovascular reac�vity assessed with diffuse correla�on spectroscopy across experimental paradigms improves with short separa�on regression,” NPh 10(2), 025002, SPIE (2023) [doi:10.1117/1.NPh.10.2.025002]

    2023 doi

  8. [16]

    Diffuse op�cal measurements of head and neck tumor hemodynamics for early predic�on of chemoradia�on therapy outcomes,

    L. Dong et al., “Diffuse op�cal measurements of head and neck tumor hemodynamics for early predic�on of chemoradia�on therapy outcomes,” J. Biomed. Opt 21(8), 085004 (2016) [doi:10.1117/1.JBO.21.8.085004]

    2016 doi

  9. [17]

    Noninvasive diffuse op�cal monitoring of head and neck tumor blood flow and oxygena�on during radia�on delivery,

    L. Dong et al., “Noninvasive diffuse op�cal monitoring of head and neck tumor blood flow and oxygena�on during radia�on delivery,” Biomed. Opt. Express 3(2), 259 (2012) 25 [doi:10.1364/BOE.3.000259]

    2012 doi

  10. [18]

    Diffuse Correla�on Spectroscopy (DCS): A Diagnos�c T ool for Assessing Tissue Blood Flow in Vascular-Related Diseases and Therapies,

    G. Yu, “Diffuse Correla�on Spectroscopy (DCS): A Diagnos�c T ool for Assessing Tissue Blood Flow in Vascular-Related Diseases and Therapies,” CMIR 8(3), 194 –210 (2012) [doi:10.2174/157340512803759875]

    2012 doi

  11. [19]

    Spa�ally varying dynamical proper�es of turbid media probed with diffusing temporal light correla�on,

    D. A. Boas and A. G. Yodh, “Spa�ally varying dynamical proper�es of turbid media probed with diffusing temporal light correla�on,” J. Opt. Soc. Am. A 14(1), 192 (1997) [doi:10.1364/JOSAA.14.000192]

    1997 doi

  12. [20]

    Design and construc�on of a realis�c digital brain phantom,

    D. L. Collins et al., “Design and construc�on of a realis�c digital brain phantom,” IEEE Trans. Med. Imaging 17(3), 463–468 (1998) [doi:10.1109/42.712135]

    1998 doi

  13. [21]

    Modeling anisotropic light propaga�on in a realis�c model of the human head,

    J. Heiskala et al., “Modeling anisotropic light propaga�on in a realis�c model of the human head,” Appl. Opt., AO 44(11), 2049–2057, Op�ca Publishing Group (2005) [doi:10.1364/AO.44.002049]

    2005 doi

  14. [22]

    Improved accuracy of cerebral blood flow quan�fica�on in the presence of systemic physiology cross -talk using mul� -layer Monte Carlo modeling,

    M. M. Wu et al., “Improved accuracy of cerebral blood flow quan�fica�on in the presence of systemic physiology cross -talk using mul� -layer Monte Carlo modeling,” Neurophoton. 8(01) (2021) [doi:10.1117/1.NPh.8.1.015001]

    2021 doi

  15. [23]

    Op�mizing a two -layer method for hybrid diffuse correla�on spectroscopy and frequency-domain diffuse op�cal spectroscopy cerebral measurements in adults,

    R. M. For� et al., “Op�mizing a two -layer method for hybrid diffuse correla�on spectroscopy and frequency-domain diffuse op�cal spectroscopy cerebral measurements in adults,” Neurophoton. 10(02) (2023) [doi:10.1117/1.NPh.10.2.025008]

    2023 doi

  16. [24]

    Sensi�vity of near -infrared spectroscopy and diffuse correla�on spectroscopy to brain hemodynamics: simula�ons and experimental findings during hypercapnia,

    J. Selb et al., “Sensi�vity of near -infrared spectroscopy and diffuse correla�on spectroscopy to brain hemodynamics: simula�ons and experimental findings during hypercapnia,” Neurophoton 1(1), 015005 (2014) [doi:10.1117/1.NPh.1.1.015005]

    2014 doi

  17. [25]

    A s s e s s m e n t o f a m u l �-layered diffuse correla�on spectroscopy method for monitoring cerebral blood flow in adults,

    K . V e r d e c c h i a e t a l . , “ A s s e s s m e n t o f a m u l �-layered diffuse correla�on spectroscopy method for monitoring cerebral blood flow in adults,” Biomed. Opt. Express 7(9), 3659 (2016) [doi:10.1364/BOE.7.003659]

    2016 doi

  18. [26]

    Direct assessment of extracerebral signal contamina�on on op�cal measurements of cerebral blood flow, oxygena�on, and metabolism,

    D. Milej et al., “Direct assessment of extracerebral signal contamina�on on op�cal measurements of cerebral blood flow, oxygena�on, and metabolism,” Neurophoton. 7(04) (2020) [doi:10.1117/1.NPh.7.4.045002]

    2020 doi

  19. [27]

    Somatosensory predic�on in the premature neonate brain,

    V. Dumont et al., “Somatosensory predic�on in the premature neonate brain,” Developmental Cogni�ve Neuroscience 57, 101148 (2022) [doi:10.1016/j.dcn.2022.101148]

    2022

  20. [28]

    Quan�fica�on of blood flow index in diffuse correla�on spectroscopy using a robust deep learning method,

    Q. Wang et al., “Quan�fica�on of blood flow index in diffuse correla�on spectroscopy using a robust deep learning method,” J. Biomed. Opt. 29(01) (2024) [doi:10.1117/1.JBO.29.1.015004]

    2024 doi

  21. [29]

    Diffuse op�cal measurement of blood flow, blood oxygena�on, and metabolism in a human brain during sensorimotor cortex ac�va�on,

    T. Durduran et al., “Diffuse op�cal measurement of blood flow, blood oxygena�on, and metabolism in a human brain during sensorimotor cortex ac�va�on,” Opt. Let. 29(15), 1766 (2004) [doi:10.1364/OL.29.001766]

    2004 doi

  22. [30]

    Pressure modula�on algorithm to separate cerebral hemodynamic signals from extracerebral ar�facts,

    W. B. Baker et al., “Pressure modula�on algorithm to separate cerebral hemodynamic signals from extracerebral ar�facts,” NPh 2(3), 035004, SPIE (2015) [doi:10.1117/1.NPh.2.3.035004]

    2015 doi

  23. [31]

    Analy�cal models for �me -domain diffuse correla�on spectroscopy for mul� -layer and heterogeneous turbid media,

    J. Li et al., “Analy�cal models for �me -domain diffuse correla�on spectroscopy for mul� -layer and heterogeneous turbid media,” Biomed. Opt. Express 8(12), 5518 (2017) [doi:10.1364/BOE.8.005518]

    2017 doi

  24. [32]

    T i m e-domain diffuse correla�on spectroscopy,

    J . S u � n e t a l . , “ T i m e-domain diffuse correla�on spectroscopy,” Op�ca 3(9), 1006 (2016) [doi:10.1364/OPTICA.3.001006]

    2016 doi

  25. [33]

    Time -domain diffuse correla�on spectroscopy (TD -DCS) for noninvasive, depth - dependent blood flow quan�fica�on in human �ssue in vivo,

    S. Samaei et al., “Time -domain diffuse correla�on spectroscopy (TD -DCS) for noninvasive, depth - dependent blood flow quan�fica�on in human �ssue in vivo,” Sci Rep 11(1), 1817 (2021) [doi:10.1038/s41598-021-81448-5]

    2021 doi

  26. [34]

    Time-of-flight resolved light field fluctua�ons reveal deep human �ssue physiology,

    O. Kholiqov et al., “Time-of-flight resolved light field fluctua�ons reveal deep human �ssue physiology,” Nat Commun 11(1), 391 (2020) [doi:10.1038/s41467-019-14228-5]

    2020 doi

  27. [35]

    Portable, high speed blood flow measurements enabled by long wavelength, interferometric diffuse correla�on spectroscopy (LW -iDCS),

    M. B. Robinson et al., “Portable, high speed blood flow measurements enabled by long wavelength, interferometric diffuse correla�on spectroscopy (LW -iDCS),” Sci Rep 13(1), 8803 (2023) [doi:10.1038/s41598-023-36074-8]

    2023 doi

  28. [36]

    Diffuse correla�on spectroscopy measurements of blood flow using 1064 nm light,

    S. A. Carp et al., “Diffuse correla�on spectroscopy measurements of blood flow using 1064 nm light,” 26 J. Biomed. Opt. 25(09) (2020) [doi:10.1117/1.JBO.25.9.097003]

    2020 doi

  29. [37]

    High-sensi�vity mul�speckle diffuse correla�on spectroscopy,

    E. J. Sie e t al., “High-sensi�vity mul�speckle diffuse correla�on spectroscopy,” Neurophoton. 7(03) (2020) [doi:10.1117/1.NPh.7.3.035010]

    2020 doi

  30. [38]

    Massively parallel, real-�me mul�speckle diffuse correla�on spectroscopy using a 500 × 500 SPAD camera,

    M. A. Wayne et al., “Massively parallel, real-�me mul�speckle diffuse correla�on spectroscopy using a 500 × 500 SPAD camera,” Biomed. Opt. Express 14(2), 703 (2023) [doi:10.1364/BOE.473992]

    2023 doi

  31. [39]

    Noninvasive detec�on of func�onal brain ac�vity with near -infrared diffusing- wave spectroscopy,

    J. Li et al., “Noninvasive detec�on of func�onal brain ac�vity with near -infrared diffusing- wave spectroscopy,” J. Biomed. Opt. 10(4), 044002 (2005) [doi:10.1117/1.2007987]

    2005 doi

  32. [40]

    Modified Beer-Lambert law for blood flow,

    W. B. Baker et al., “Modified Beer-Lambert law for blood flow,” Biomed. Opt. Express 5(11), 4053 (2014) [doi:10.1364/BOE.5.004053]

    2014 doi

  33. [41]

    Scanning interferometric near-infrared spectroscopy,

    O. Kholiqov et al., “Scanning interferometric near-infrared spectroscopy,” Opt. Let. 47(1), 110 (2022) [doi:10.1364/OL.443533]

    2022 doi

  34. [42]

    Accuracy of diffuse correla�on spectroscopy measurements of cerebral blood flow when using a three -layer analy�cal model,

    H. Zhao, E. Sathialingam, and E. M. Buckley, “Accuracy of diffuse correla�on spectroscopy measurements of cerebral blood flow when using a three -layer analy�cal model,” Biomed. Opt. Express 12(11), 7149 (2021) [doi:10.1364/BOE.438303]

    2021 doi

  35. [43]

    Recovery of brain blood flow changes from diffuse correla�on spectroscopy data using a layered Monte Carlo forward model,

    S. A. Carp et al., “Recovery of brain blood flow changes from diffuse correla�on spectroscopy data using a layered Monte Carlo forward model,” in Biomedical Op�cs 2014, p. BM3A.20, OSA, Miami, Florida (2014) [doi:10.1364/BIOMED.2014.BM3A.20]

    2014 doi

  36. [44]

    Comparison of diffuse correla�on spectroscopy analy�cal models for measuring cerebral blood flow in adults,

    H. Zhao et al., “Comparison of diffuse correla�on spectroscopy analy�cal models for measuring cerebral blood flow in adults,” J. Biomed. Opt. 28(12) (2023) [doi:10.1117/1.JBO.28.12.126005]

    2023 doi

  37. [45]

    Boundary condi�ons for the diffusion equa�on in radia�ve transfer,

    R. C. Haskell et al., “Boundary condi�ons for the diffusion equa�on in radia�ve transfer,” J. Opt. Soc. Am. A 11(10), 2727, Op�ca Publishing Group (1994) [doi:10.1364/josaa.11.002727]

    1994 doi

  38. [46]

    Diffuse photon probes of structural and dynamical proper�es of turbid media: Theory and biomedical applica�ons,

    D. A. Boas, “Diffuse photon probes of structural and dynamical proper�es of turbid media: Theory and biomedical applica�ons,” University of Pennsylvania (1996)

    1996

  39. [47]

    Linear Transport Theory,

    K. M. Case, P . F. Zweifel, and G. C. Pomraning, “Linear Transport Theory,” Physics Today 21(10), 72–73 (1968) [doi:10.1063/1.3034554]

    1968 doi

  40. [48]

    C o r r e l a � o n t r a n s f e r - Applica�on of radia�ve transfer solu�on methods to photon correla�on problems,

    B . J . A c k e r s o n e t a l . , “ C o r r e l a � o n t r a n s f e r - Applica�on of radia�ve transfer solu�on methods to photon correla�on problems,” Journal of Thermophysics and Heat Transfer 6(4), 577 –588 (1992) [doi:10.2514/3.11537]

    1992 doi

  41. [49]

    Influences of �ssue absorp�on and scatering on diffuse correla�on spectroscopy blood flow measurements,

    D. Irwin et al., “Influences of �ssue absorp�on and scatering on diffuse correla�on spectroscopy blood flow measurements,” Biomed. Opt. Express 2(7), 1969 (2011) [doi:10.1364/BOE.2.001969]

    2011 doi

  42. [50]

    Noninvasive measurements of �ssue hemodynamics with hybrid diffuse op�cal methods,

    T. Durduran, “Noninvasive measurements of �ssue hemodynamics with hybrid diffuse op�cal methods,” Medical Physics 31(7), 2178–2178 (2004) [doi:10.1118/1.1763412]

    2004 doi

  43. [51]

    Establishing the diffuse correla�on spectroscopy signal rela�onship with blood flow,

    D. A. Boas et al., “Establishing the diffuse correla�on spectroscopy signal rela�onship with blood flow,” Neurophoton 3(3), 031412 (2016) [doi:10.1117/1.NPh.3.3.031412]

    2016 doi

  44. [52]

    Small separa�on diffuse correla�on spectroscopy for measurement of cerebral blood flow in rodents,

    E. Sathialingam et al., “Small separa�on diffuse correla�on spectroscopy for measurement of cerebral blood flow in rodents,” Biomed Opt Express 9(11), 5719–5734 (2018) [doi:10.1364/BOE.9.005719]

    2018 doi

  45. [53]

    Diffusing wave spectroscopy,

    D. J. Pine et al., “Diffusing wave spectroscopy,” Phys. Rev. Let. 60(12), 1134– 1137 (1988) [doi:10.1103/PhysRevLet.60.1134]

    1988 doi

  46. [54]

    Monte Carlo Simula�on of Photon Migra�on in 3D Turbid Media Accelerated by Graphics Processing Units,

    Q. Fang and D. A. Boas, “Monte Carlo Simula�on of Photon Migra�on in 3D Turbid Media Accelerated by Graphics Processing Units,” Opt. Express 17(22), 20178 (2009) [doi:10.1364/OE.17.020178]

    2009 doi

  47. [55]

    Influence of source –detector separa�on on diffuse correla�on spectroscopy measurements of cerebral blood flow with a mul�layered analy�cal model,

    H. Zhao and E. M. Buckley, “Influence of source –detector separa�on on diffuse correla�on spectroscopy measurements of cerebral blood flow with a mul�layered analy�cal model,” Neurophoton. 9(03) (2022) [doi:10.1117/1.NPh.9.3.035002]

    2022 doi

  48. [56]

    Diffusing -wave spectroscopy from head -like �ssue phantoms: influence of a non - scatering layer,

    F. Jaillon et al., “Diffusing -wave spectroscopy from head -like �ssue phantoms: influence of a non - scatering layer,” Opt. Express 14(22), 10181, Op�ca Publishing Group (2006) [doi:10.1364/oe.14.010181]

    2006 doi

  49. [57]

    Effec�ve scatering coefficient of the cerebral spinal fluid in adult head models for diffuse op�cal imaging,

    A. Custo et al., “Effec�ve scatering coefficient of the cerebral spinal fluid in adult head models for diffuse op�cal imaging,” Appl. Opt., AO 45(19), 4747 –4755, Op�ca Publishing Group (2006) 27 [doi:10.1364/AO.45.004747]

    2006 doi

  50. [58]

    Comple t e head ce r ebr al s ensi�vi ty mapping f or diffuse c or r el a �on spe ctr osc op y using subject-specific magne�c resonance imaging models,

    M. M. W u e t al., “Comple t e head ce r ebr al s ensi�vi ty mapping f or diffuse c or r el a �on spe ctr osc op y using subject-specific magne�c resonance imaging models,” Biomed. Opt. Express 13(3), 1131 (2022) [doi:10.1364/BOE.449046]

    2022 doi

  51. [59]

    Theore�cal and experimental inves�ga�on of near -infrared light propaga�on in a model of the adult head,

    E. Okada et al., “Theore�cal and experimental inves�ga�on of near -infrared light propaga�on in a model of the adult head,” Appl. Opt. 36(1), 21 (1997) [doi:10.1364/AO.36.000021]

    1997 doi

  52. [60]

    Op�cal proper�es of selected na�ve and coagulated human brain �ssues in vitro in the visible and near infrared spectral range,

    A. N. Yaroslavsky et al., “Op�cal proper�es of selected na�ve and coagulated human brain �ssues in vitro in the visible and near infrared spectral range,” Phys. Med. Biol. 47(12), 2059 (2002) [doi:10.1088/0031-9155/47/12/305]

    2002 doi

  53. [61]

    F a c t o r s a ff e c � n g t h e a c c u r a c y of n e a r-infrared spectroscopy concentra�on calcula�ons for focal changes in oxygena�on parameters,

    G . S t r a n g m a n , M. A. F r a n c e s c h i n i , a n d D . A. B oa s , “ F a c t o r s a ff e c � n g t h e a c c u r a c y of n e a r-infrared spectroscopy concentra�on calcula�ons for focal changes in oxygena�on parameters,” NeuroImage 18(4), 865–879 (2003) [doi:10.101...

    2003 doi

  54. [62]

    Op�miza�on of �me domain diffuse correla�on spectroscopy parameters for measuring brain blood flow,

    D. Mazumder et al., “Op�miza�on of �me domain diffuse correla�on spectroscopy parameters for measuring brain blood flow,” NPh 8(3), 035005, SPIE (2021) [doi:10.1117/1.NPh.8.3.035005]

    2021 doi

  55. [63]

    Near-infrared light propaga�on in an adult head model I Modeling of low- level scatering in the cerebrospinal fluid layer,

    E. Okada and D. T. Delpy, “Near-infrared light propaga�on in an adult head model I Modeling of low- level scatering in the cerebrospinal fluid layer,” Appl. Opt. 42(16), 2906 (2003) [doi:10.1364/AO.42.002906]

    2003 doi

  56. [64]

    Three dimensional Monte Carlo code for photon migra�on through complex heterogeneous media including the adult human head,

    D. A. Boas et al., “Three dimensional Monte Carlo code for photon migra�on through complex heterogeneous media including the adult human head,” Opt. Express, OE 10(3), 159 –170, Op�ca Publishing Group (2002) [doi:10.1364/OE.10.000159]

    2002 doi

  57. [65]

    Improved sensi�vity to cerebral hemodynamics during brain ac�va�on with a �me - gated op�cal system: analy�cal model and experimental valida�on,

    J. Selb et al., “Improved sensi�vity to cerebral hemodynamics during brain ac�va�on with a �me - gated op�cal system: analy�cal model and experimental valida�on,” J. Biomed. Opt. 10(1), 011013 (2005) [doi:10.1117/1.1852553]

    2005 doi

  58. [66]

    Robust inference of baseline op�cal proper�es of the human head with three - dimensional segmenta�on from magne�c resonance imaging,

    A. H. Barnet et al., “Robust inference of baseline op�cal proper�es of the human head with three - dimensional segmenta�on from magne�c resonance imaging,” Appl. Opt. 42(16), 3095 (2003) [doi:10.1364/AO.42.003095]

    2003 doi

  59. [67]

    Cerebral hemodynamics evalua�on by near -infrared �me-resolved spectroscopy: Correla�on with simultaneous positron emission tomography measurements,

    E. Ohmae et al., “Cerebral hemodynamics evalua�on by near -infrared �me-resolved spectroscopy: Correla�on with simultaneous positron emission tomography measurements,” NeuroImage 29(3), 697–705 (2006) [doi:10.1016/j.neuroimage.2005.08.008]

    2006 doi

  60. [68]

    The Endocranial Vascular System: Tracing Vessels,

    G. Rangel de Lázaro et al., “The Endocranial Vascular System: Tracing Vessels,” in Digital Endocasts: From Skulls to Brains, E. Bruner, N. Ogihara, and H. C. Tanabe, Eds., pp. 71–91, Springer Japan, Tokyo (2018) [doi:10.1007/978-4-431-56582-6_6]

    2018 doi

  61. [69]

    Diffuse op�cal correla�on tomography of cerebral blood flow during cor�cal spreading depression in rat brain,

    C. Zhou et al., “Diffuse op�cal correla�on tomography of cerebral blood flow during cor�cal spreading depression in rat brain,” Opt. Express 14(3), 1125 (2006) [doi:10.1364/OE.14.001125]

    2006 doi

  62. [70]

    ANSI Z136.1 -2014 - American Na�onal Standard for Safe Use of Lasers,

    “ANSI Z136.1 -2014 - American Na�onal Standard for Safe Use of Lasers,” <htps://webstore.ansi.org/standards/lia/ansiz1362014> (accessed 13 November 2024)

    2014

  63. [71]

    Diffuse op�cs for �ssue monitoring and tomography,

    T. Durduran et al., “Diffuse op�cs for �ssue monitoring and tomography,” Rep. Prog. Phys. 73(7), 076701 (2010) [doi:10.1088/0034-4885/73/7/076701]

    2010 doi

  64. [72]

    In -vivo op�cal imaging and spectroscopy of cerebral hemodynamics,

    C. Zhou, “In -vivo op�cal imaging and spectroscopy of cerebral hemodynamics,” Ph.D., in ProQuest Disserta�ons and Theses, University of Pennsylvania (2007)

    2007

  65. [73]

    Simultaneously Extrac�ng Mul�ple Parameters via Fi�ng One Single Autocorrela�on Func�on Curve in Diffuse Correla�on Spectroscopy,

    L. Dong et al., “Simultaneously Extrac�ng Mul�ple Parameters via Fi�ng One Single Autocorrela�on Func�on Curve in Diffuse Correla�on Spectroscopy,” IEEE Trans. Biomed. Eng. 60(2), 361–368 (2013) [doi:10.1109/TBME.2012.2226885]

    2013

  66. [74]

    Mul�distance diffuse correla�on spectroscopy for simultaneous es�ma�on of blood flow index and op�cal proper�es,

    P . Farzam and T. Durduran, “Mul�distance diffuse correla�on spectroscopy for simultaneous es�ma�on of blood flow index and op�cal proper�es,” JBO 20(5), 055001, SPIE (2015) [doi:10.1117/1.JBO.20.5.055001]

    2015 doi

  67. [75]

    Valida�on of diffuse correla�on spectroscopic measurement of cerebral blood flow using phase -encoded velocity mapping magne�c resonance imaging,

    E. M. Buckley et al., “Valida�on of diffuse correla�on spectroscopic measurement of cerebral blood flow using phase -encoded velocity mapping magne�c resonance imaging,” J. Biomed. Opt. 17(3), 28 037007 (2012) [doi:10.1117/1.JBO.17.3.037007]

    2012 doi

  68. [76]

    ATLAS: a large array, on -chip compute SPAD camera for mul�speckle diffuse correla�on spectroscopy,

    A. Gorman et al., “ATLAS: a large array, on -chip compute SPAD camera for mul�speckle diffuse correla�on spectroscopy,” Biomed. Opt. Express, BOE 15(11), 6499–6515, Op�ca Publishing Group (2024) [doi:10.1364/BOE.531416]

    2024 doi

  69. [77]

    Deep learning model for ultrafast quan�fica�on of blood flow in diffuse correla�on spectroscopy,

    C.-S. Poon, F. Long, and U. Sunar, “Deep learning model for ultrafast quan�fica�on of blood flow in diffuse correla�on spectroscopy,” Biomed. Opt. Express 11(10), 5557 (2020) [doi:10.1364/BOE.402508]

    2020 doi

  70. [78]

    Development of a Monte Carlo -wave model to simulate �me domain diffuse correla�on spectroscopy measurements from first principles,

    X. Cheng et al., “Development of a Monte Carlo -wave model to simulate �me domain diffuse correla�on spectroscopy measurements from first principles,” J. Biomed. Opt. 27(08) (2022) [doi:10.1117/1.JBO.27.8.083009]

    2022 doi

  71. [79]

    Flow visualiza�on by means of single -exposure speckle photography,

    A. F. Fercher and J. D. Briers, “Flow visualiza�on by means of single -exposure speckle photography,” Op�cs Communica�ons 37(5), 326–330 (1981) [doi:10.1016/0030-4018(81)90428-4]

    1981 doi

  72. [80]

    Diffuse correla�on spectroscopy for measurement of cerebral blood flow: future prospects,

    E. M. Buckley et al., “Diffuse correla�on spectroscopy for measurement of cerebral blood flow: future prospects,” Neurophoton 1(1), 011009 (2014) [doi:10.1117/1.NPh.1.1.011009]

    2014 doi

  73. [81]

    False posi�ves and false nega�ves in func�onal near -infrared spectroscopy: issues, challenges, and the way forward,

    I. Tachtsidis and F. Scholkmann, “False posi�ves and false nega�ves in func�onal near -infrared spectroscopy: issues, challenges, and the way forward,” NPh 3(3), 031405, SPIE (2016) [doi:10.1117/1.NPh.3.3.031405]

    2016 doi

  74. [82]

    Cerebral blood flow monitoring using a deep learning implementa�on of the two-layer DCS analy�cal model with a 512 512 SPAD array,

    M. Pan et al., “Cerebral blood flow monitoring using a deep learning implementa�on of the two-layer DCS analy�cal model with a 512 512 SPAD array,” arXiv:2504.06997, arXiv (2025) [doi:10.48550/arXiv.2504.06997]

    2025 doi

  75. [83]

    A comprehensive overview of diffuse correla�on spectroscopy: Theore�cal framework, recent advances in hardware, analysis, and applica�ons,

    Q. Wang et al., “A comprehensive overview of diffuse correla�on spectroscopy: Theore�cal framework, recent advances in hardware, analysis, and applica�ons,” NeuroImage 298, 120793 (2024) [doi:10.1016/j.neuroimage.2024.120793]

    2024

  76. [84]

    Fast blood flow index reconstruc�on of diffuse correla�on spectroscopy using a back- propaga�on-free data -driven algorithm,

    Z. Zang et al., “Fast blood flow index reconstruc�on of diffuse correla�on spectroscopy using a back- propaga�on-free data -driven algorithm,” Biomed. Opt. Express, BOE 16(3), 1254 –1269, Op�ca Publishing Group (2025) [doi:10.1364/BOE.549363]. 29 Supplementary Material:

    2025 doi

  77. [85]

    DCS analytical models’ derivation Semi-infinite model For a semi-infinite homogeneous medium, the correlation diffusion equation can be expressed as: �𝐷𝐷 𝜐𝜐 ∇2 − 𝜇𝜇𝑎𝑎 − 1 3 𝜇𝜇𝑠𝑠 ′ 𝑘𝑘0 2𝛼𝛼〈∆𝑟𝑟2(𝜏𝜏)〉� 𝐺𝐺1(𝐫𝐫, 𝜏𝜏) = −𝑆𝑆(𝐫𝐫), (5) The analytical solution to Eq. (1) under the assump...

  78. [86]

    The milk (1.8% fat, semi -skimmed, purchased from ALDI grocery) was diluted with water at a ratio of 1:3

    SNR analysis of the synthesized experimental data We validated our noise model using experimental results obtained from measurements on a milk phantom. The milk (1.8% fat, semi -skimmed, purchased from ALDI grocery) was diluted with water at a ratio of 1:3. We developed a trad...

  79. [87]

    Compared to two-layer SD fitting, the two- layer MD fitting achieves better convergence at larger 𝜌𝜌 values (35 mm)

    Two-layer MD fitting for rCBFi recovery We performed two-layer MD fitting to estimate rCBFi. Compared to two-layer SD fitting, the two- layer MD fitting achieves better convergence at larger 𝜌𝜌 values (35 mm). However, the two-layer MD fitting tends to overestimate rCBFi acros...

  80. [88]

    As shown in Fig

    Comparison between fminsearchbnd and lsqcurvefit for single-exponential fitting We compared rCBFi recovered by single -exponential fitting using the fminsearchbnd and lsqcurvefit functions in MATLAB. As shown in Fig. S4, the rCBFi estimated with lsqcurvefit exhibits greater fl...

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.