On the Normalization of Cerebral Blood Flow

Guibert, Romain; Fonta, Caroline; Estève, François; Plouraboué, Franck

doi:10.1038/jcbfm.2013.39

Cited by 8 publications

(10 citation statements)

References 20 publications

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“…In order to demonstrate our results we introduced two definitions of voxelwise perfusion, global perfusion P s and local perfusion P v . Local perfusion P v is in line with [ 7 ] where the authors demonstrated a discretization dependent flow without connecting it mathematically to perfusion. Theory and examples in our work show that this definition of perfusion does not comply with the physical understanding of perfusion as a feeding arterial blood flow.…”

Section: Discussionsupporting

confidence: 71%

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Estimating the discretization dependent accuracy of perfusion in coupled capillary flow measurements

et al. 2018

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One-compartment models are widely used to quantify hemodynamic parameters such as perfusion, blood volume and mean transit time. These parameters are routinely used for clinical diagnosis and monitoring of disease development and are thus of high relevance. However, it is known that common estimation techniques are discretization dependent and values can be erroneous. In this paper we present a new model that enables systematic quantification of discretization errors. Specifically, we introduce a continuous flow model for tracer propagation within the capillary tissue, used to evaluate state-of-the-art one-compartment models. We demonstrate that one-compartment models are capable of recovering perfusion accurately when applied to only one compartment, i.e. the whole region of interest. However, substantial overestimation of perfusion occurs when applied to fractions of a compartment. We further provide values of the estimated overestimation for various discretization levels, and also show that overestimation can be observed in real-life applications. Common practice of using compartment models for fractions of tissue violates model assumptions and careful interpretation is needed when using the computed values for diagnosis and treatment planning.

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Section: Discussionsupporting

confidence: 71%

“…It was previously suggested to normalize the flow by surface instead of volume [ 7 ]. Our experiments suggest that a surface normalization is nevertheless discretization dependent, and traditional 1C models are not able to restore this type of perfusion, neither for global, nor for local perfusion (cfr.…”

Section: Discussionmentioning

confidence: 99%

Estimating the discretization dependent accuracy of perfusion in coupled capillary flow measurements

et al. 2018

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“…34 It is worth noting at this point that the concept of a well-defined absolute blood flow has been questioned as the value of absolute blood flow obtained has been theoretically demonstrated to depend on vascular geometry, measurement procedure, and spatial scale sampled. 35,36 Further research is needed to better understand the implications of these theoretical arguments.…”

Section: Discussionmentioning

confidence: 99%

Establishing the diffuse correlation spectroscopy signal relationship with blood flow

et al. 2016

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Abstract. Diffuse correlation spectroscopy (DCS) measurements of blood flow rely on the sensitivity of the temporal autocorrelation function of diffusively scattered light to red blood cell (RBC) mean square displacement (MSD). For RBCs flowing with convective velocity v RBC , the autocorrelation is expected to decay exponentially with ðv RBC τÞ 2 , where τ is the delay time. RBCs also experience shear-induced diffusion with a diffusion coefficient D shear and an MSD of 6D shear τ. Surprisingly, experimental data primarily reflect diffusive behavior. To provide quantitative estimates of the relative contributions of convective and diffusive movements, we performed Monte Carlo simulations of light scattering through tissue of varying vessel densities. We assumed laminar vessel flow profiles and accounted for shear-induced diffusion effects. In agreement with experimental data, we found that diffusive motion dominates the correlation decay for typical DCS measurement parameters. Furthermore, our model offers a quantitative relationship between the RBC diffusion coefficient and absolute tissue blood flow. We thus offer, for the first time, theoretical support for the empirically accepted ability of the DCS blood flow index (BF i ) to quantify tissue perfusion. We find BF i to be linearly proportional to blood flow, but with a proportionality modulated by the hemoglobin concentration and the average blood vessel diameter. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…Several methods can be used to measure CBF, such as computed tomography perfusion (CT perfusion), positron emission tomography (PET), and single-photon emission computed tomography; however, CBF measured by different methods normally cannot be compared directly (Kudo et al, 2003; Guibert et al, 2013). In addition to the methods mentioned above, an increasingly popular method to quantify CBF is to use arterial spin labeling (ASL) magnetic resonance imaging (MRI).…”

Section: Introductionmentioning

confidence: 99%

On the Validation of a Multiple-Network Poroelastic Model Using Arterial Spin Labeling MRI Data

Guo

Lyu

et al. 2019

Front. Comput. Neurosci.

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The Multiple-Network Poroelastic Theory (MPET) is a numerical model to characterize the transport of multiple fluid networks in the brain, which overcomes the problem of conducting separate analyses on individual fluid compartments and losing the interactions between tissue and fluids, in addition to the interaction between the different fluids themselves. In this paper, the blood perfusion results from MPET modeling are partially validated using cerebral blood flow (CBF) data obtained from arterial spin labeling (ASL) magnetic resonance imaging (MRI), which uses arterial blood water as an endogenous tracer to measure CBF. Two subjects—one healthy control and one patient with unilateral middle cerebral artery (MCA) stenosis are included in the validation test. The comparison shows several similarities between CBF data from ASL and blood perfusion results from MPET modeling, such as higher blood perfusion in the gray matter than in the white matter, higher perfusion in the periventricular region for both the healthy control and the patient, and asymmetric distribution of blood perfusion for the patient. Although the partial validation is mainly conducted in a qualitative way, it is one important step toward the full validation of the MPET model, which has the potential to be used as a testing bed for hypotheses and new theories in neuroscience research.

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On the Normalization of Cerebral Blood Flow

Cited by 8 publications

References 20 publications

Estimating the discretization dependent accuracy of perfusion in coupled capillary flow measurements

Estimating the discretization dependent accuracy of perfusion in coupled capillary flow measurements

Establishing the diffuse correlation spectroscopy signal relationship with blood flow

On the Validation of a Multiple-Network Poroelastic Model Using Arterial Spin Labeling MRI Data

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