Improved accuracy of cerebral blood flow quantification in the presence of systemic physiology cross-talk using multi-layer Monte Carlo modeling

Wu, Melissa M.; Chan, Suk-Tak; Mazumder, Dibbyan; Tamborini, Davide; Stephens, Kimberly A.; Deng, Bin; Farzam, Parya; Chu, Joyce Yawei; Franceschini, Maria Angela; Qu, Jason; Carp, Stefan A.

doi:10.1117/1.nph.8.1.015001

Cited by 38 publications

(59 citation statements)

References 63 publications

(104 reference statements)

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“… 12 – 14 Recent validation with transcranial Doppler ultrasound (TCD) supports the superiority of the three-layer models over two-layer models and highlights the need for including a negligible flow layer (skull) when assessing cerebral perfusion with DCS. 15 …”

Section: Introductionmentioning

confidence: 99%

Influence of source–detector separation on diffuse correlation spectroscopy measurements of cerebral blood flow with a multilayered analytical model

Zhao

Buckley

2022

Neurophoton.

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Significance: Diffuse correlation spectroscopy (DCS) is an emerging noninvasive optical technology for bedside monitoring of cerebral blood flow. However, extracerebral hemodynamics can significantly influence DCS estimations of cerebral perfusion. Advanced analytical models can be used to remove the contribution of extracerebral hemodynamics; however, these models are highly sensitive to measurement noise. There is a need for an empirical determination of the optimal source-detector separation(s) (SDS) that improves the accuracy and reduces sensitivity to noise in the estimation of cerebral blood flow with these models.Aim: To determine the influence of SDS on solution uniqueness, measurement accuracy, and sensitivity to inaccuracies in model parameters when using the three-layer model to estimate cerebral blood flow with DCS.Approach: We performed a series of in silico simulations on samples spanning a wide range of physiologically-relevant layer optical properties, thicknesses, and flow. Data were simulated at SDS ranging from 0.5 to 3.0 cm using the three-layer solution to the correlation diffusion equation (with and without noise added) and using three-layer slab Monte Carlo simulations. We quantified the influence of SDS on uniqueness, accuracy, and sensitivity to inaccuracies in model parameters using the three-layer inverse model.Results: Two SDS are required to ensure a unique solution of cerebral blood flow index (CBFi). Combinations of 0.5/1.0/1.5 and 2.5 cm provide the optimal choice for balancing the depth penetration with signal-to-noise ratio to minimize the error in CBFi across a wide range of samples with varying optical properties, thicknesses, and dynamics.Conclusions: These results suggest that the choice of SDS is critical for minimizing the estimated error of cerebral blood flow when using the three-layer model to analyze DCS data.

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Section: Introductionmentioning

confidence: 99%

Influence of source–detector separation on diffuse correlation spectroscopy measurements of cerebral blood flow with a multilayered analytical model

Zhao

Buckley

2022

Neurophoton.

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“…Limitations of homogeneous tissue-models to describe light transport in layered biological media have been discussed previously 4,43 . Although analytical models that incorporate heterogeneous optical properties are becoming more frequent 28,44,45 , their use, particularly in inverse calculations, is limited by their numerical accuracy and efficiency 27 . Therefore, homogeneous models are typically used given their simplicity and efficiency in solving inverse problems that require 100-1,000 evaluations of the forward model to reach convergence.…”

Section: Discussionmentioning

confidence: 99%

“…In Fig. 4, we reconstruct the time-domain signal for high scattering media and large layer thicknesses over a wide range of times which has previously been previously difficult due to numerical overflow [27][28][29] . In Fig.…”

Section: φ(ρ)mentioning

confidence: 99%

“…These methods do not give closed-form expressions directly in the spatial or time-domains for the photon fluence. Instead, the fluence in real-space is computed using numerical transforms 22 or root-finding techniques 24 which tend to increase numerical errors and computational costs 27 . For example, the integral transform approach 22 solves the diffusion equation in the spatial-frequency domain which then must be inverse space-transformed (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Light diffusion in layered media: A numerical study in the spatial and time-domains

Helton

Zerafa

Vishwanath

et al. 2022

Preprint

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Accurate and efficient forward models of photon migration in heterogeneous geometries are important for many applications of light in medicine because many biological tissues exhibit a layered structure, with each layer having independent optical properties and thickness. Unfortunately, closed form analytical solutions are not readily available for layered tissue-models, and often are modeled using computationally expensive numerical techniques or theoretical approximations that limit accuracy and real-time analysis. Here, we develop an open-source accurate, efficient, and stable numerical routine to solve the diffusion equation in the steady-state and time-domain for a layered cylinder tissue model with an arbitrary number of layers and specified thickness and optical coefficients. We show that the steady-state (< 0.1 ms) and time-domain (< 0.5 ms) fluence (for an 8-layer medium) can be calculated with absolute numerical errors approaching machine precision. The numerical implementation increased computation speed by 3 to 4 orders of magnitude compared to previously reported theoretical solutions in layered media. We verify our solutions asymptotically to homogeneous tissue geometries using closed form analytical solutions to assess convergence and numerical accuracy. Approximate solutions to compute the reflected intensity are presented which can decrease the computation time by an additional 2-3 orders of magnitude. We also compare our solutions for 2, 3, and 5 layered media to gold-standard Monte Carlo simulations in layered tissue models of high interest in biomedical optics (e.g. skin/fat/muscle and brain). The presented routine could enable more robust real-time data analysis tools in heterogeneous tissues that are important in many clinical applications such as functional brain imaging and diffuse optical spectroscopy.

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“…There is also scope to improve nICP measurement accuracy through the development of improved tissue models that account for the effects of superficial tissue contamination in the FD-DOS/DCS optical signals (which likely are more pronounced in older patients). 34,35 A third direction for future research is the use of machine learning strategies. 13,14 Anesthesia is a potential confound for our secondary analysis of intraoperative to postoperative changes.…”

Section: Discussionmentioning

confidence: 99%

Optical Detection of Intracranial Pressure and Perfusion Changes in Neonates With Hydrocephalus

Flanders

Lang

et al. 2021

The Journal of Pediatrics

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Objective To demonstrate that a novel noninvasive index of intracranial pressure (ICP) derived from diffuse optics-based techniques is associated with intracranial hypertension. Study designWe compared noninvasive and invasive ICP measurements in infants with hydrocephalus. Infants born term and preterm were eligible for inclusion if clinically determined to require cerebrospinal fluid (CSF) diversion. Ventricular size was assessed preoperatively via ultrasound measurement of the fronto-occipital (FOR) and frontotemporal (FTHR) horn ratios. Invasive ICP was obtained at the time of surgical intervention with a manometer. Intracranial hypertension was defined as invasive ICP ³15 mmHg. Diffuse optical measurements of cerebral perfusion, oxygen extraction, and noninvasive ICP were performed preoperatively, intraoperatively, and postoperatively. Optical and ultrasound measures were compared with invasive ICP measurements, and their change in values after CSF diversion were obtained. ResultsWe included 39 infants, 23 with intracranial hypertension. No group difference in ventricular size was found by FOR (P = .93) or FTHR (P = .76). Infants with intracranial hypertension had significantly higher noninvasive ICP (P = .02) and oxygen extraction fraction (OEF) (P = .01) compared with infants without intracranial hypertension. Increased cerebral blood flow (P = .005) and improved OEF (P < .001) after CSF diversion were observed only in infants with intracranial hypertension.Conclusions Noninvasive diffuse optical measures (including a noninvasive ICP index) were associated with intracranial hypertension. The findings suggest that impaired perfusion from intracranial hypertension was independent of ventricular size. Hemodynamic evidence of the benefits of CSF diversion was seen in infants with intracranial hypertension. Noninvasive optical techniques hold promise for aiding the assessment of CSF diversion timing.

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Improved accuracy of cerebral blood flow quantification in the presence of systemic physiology cross-talk using multi-layer Monte Carlo modeling

Cited by 38 publications

References 63 publications

Influence of source–detector separation on diffuse correlation spectroscopy measurements of cerebral blood flow with a multilayered analytical model

Influence of source–detector separation on diffuse correlation spectroscopy measurements of cerebral blood flow with a multilayered analytical model

Light diffusion in layered media: A numerical study in the spatial and time-domains

Optical Detection of Intracranial Pressure and Perfusion Changes in Neonates With Hydrocephalus

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