Oxygen Advection and Diffusion in a Three Dimensional Vascular Anatomical Network

doi:10.1364/oe.16.17530

Opt Express. Author manuscript; available in PMC 2008 November 27.

Published in final edited form as:

Opt Express. 2008 October 27; 16(22): 17530–17541.

doi: 10.1364/oe.16.17530

PMCID: PMC2584207

NIHMSID: NIHMS77385

Oxygen Advection and Diffusion in a Three Dimensional Vascular Anatomical Network

Qianqian Fang,¹ Sava Sakadžić,¹ Lana Ruvinskaya,¹ Anna Devor,^1,² Anders M. Dale,² and David A. Boas¹^*

¹Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th St, Charlestown, Massachusetts, 02129, USA

²Department of Neurosciences, University of California, San Diego, CA, USA

^* Corresponding author: Email: dboas/at/nmr. mgh. harvard. edu

The publisher's final edited version of this article is available at Opt Express

See other articles in PMC that cite the published article.

Abstract

There is an increasing need for quantitative and computationally affordable models for analyzing tissue metabolism and hemodynamics in microvascular networks. In this work, we develop a hybrid model to solve for the time-varying oxygen advection-diffusion equation in the vessels and tissue. To obtain a three-dimensional temporal evolution of tissue oxygen concentration for realistic complex vessel networks, we used a graph-based advection model combined with a finite-element based diffusion model and an implicit time-advancing scheme. We validated this algorithm for both static and dynamic conditions. We also applied it to a complex vascular network obtained from a rodent somatosensory cortex. Qualitative agreement was found with in-vivo experiments.

1. Introduction

Numerous optical methods are utilized to quantify tissue blood flow, blood volume, and oxygen delivery, ranging from the macroscopic whole tissue level to the microscopic capillary network level and sub-cellular level. Near-infrared spectroscopy (NIRS) and imaging utilize near-infrared light to quantify the concentrations of oxygenated and deoxygenated hemoglobin over cubic centimeters of tissue [1-3]. Visible light tissue reflectance imaged with a CCD camera is frequently used to image superficial changes in hemoglobin concentrations with light spectroscopy [4] and blood flow with laser speckle contrast [5, 6], particularly in the neurosciences. Confocal and multiphoton microscopy enable sub-micrometer resolution with depth penetration of up to ~100 μm and ~500 μm or more, respectively, and are used to image the three-dimensional (3D) vascular anatomy and red blood cell velocity within vessels [7, 8], and oxygenation of the blood [9-11]. Doppler optical coherence tomography is enabling rapid volumetric imaging of flow within the vascular network over potentially cubic millimeters in seconds [12-14]. There seems to be a unique opportunity for optics to provide a detailed characterization of oxygen delivery to the tissue by combining these measurements of blood flow, volume, and oxygenation with fluorescence-based measurements of cellular activity [15, 16] and metabolism [17, 18].

The advanced analysis of near-infrared spectroscopy data from centimeters of tissue and visible light imaging from hundreds of micrometers of tissue are yielding information about oxygen metabolism [19-23]. While these estimates of oxygen metabolism appear precise, validating the accuracy is challenged by the lack of a gold standard method. On the other hand, the microscopic measures of individual microvessels and surrounding tissue provide more direct measures of oxygen delivery, but detailed models are needed to provide an analysis framework to quantify physiological parameters like vessel wall oxygen permeability, tissue oxygen diffusion, and oxygen consumption that are not directly measurable. Further, due to the time varying nature of oxygen delivery, particularly during brain activity, there is a need for models that can handle dynamic, time-varying processes.

In this paper, we describe a detailed computational model of tissue oxygen delivery by a microvascular network that will provide a framework for analyzing multimodal microscopic measures to better quantify additional physiological parameters, and will enable more detailed and realistic analyses of the accuracy of near-infrared spectroscopy and visible light imaging. We start by describing our method for solving the time-varying oxygen advection diffusion equation that estimates velocity within individual microvessel segments, oxygen flux across the permeable vascular walls, and then oxygen diffusion and consumption within the tissue. After validating our solver, we demonstrate its application by calculating oxygen delivery by a microvascular network acquired in rodent somatosensory cortex by two-photon microscopy over a 230 × 230 × 450 μm³ volume of tissue.

2. Methods

2.1 Models and Domain Discretization

The transport and consumption of oxygen in the vessel network and tissue are characterized by the advection-diffusion equation [24], which can be expressed as the following equations:

(1)

(2)

where C_F and C_B are the free and bounded oxygen concentrations (μM), respectively; C_T = C_B + C_F denotes the total oxygen concentration, equation M3

denotes the velocity (m/s), D_O2 is the oxygen diffusion coefficient (m/s²), and OC is the tissue oxygen consumption rate (μM/s). Equation (2) denotes the equilibrium between the hemoglobin bound oxygen and free oxygen where H denotes hematocrit, C_Hb is the hemoglobin concentration within a red blood cell, SO₂(C_F) is the hemoglobin oxygen saturation and it is in equilibrium with the concentration of free oxygen, C_F, as described by the oxygen disassociation curve. We use an invertible form for the oxygen disassociation curve given by [25]. We note that under physiological conditions, all of these parameters can vary in space and time.

Certainly, one could solve Eq. (1) using a single numerical approach, such as a finite element (FE) or a finite difference (FD) method, by applying a full spatial discretization of the tissue volume, the exterior surface and the interior volume of vessels. Because of the small length-scale of the vessel wall, the meshing and computational expense can be prohibitive with increasing complexity of the vessel networks. Instead, we devised a hybrid model that includes a graph-based one-dimensional (1D) oxygen advection model within the vessel, a 1D oxygen flux conservation model across the vessel wall, and an FE-based 3D oxygen diffusion model within the tissue. An implicit (Crank-Nickson) [26] scheme was implemented to compute the updates at each time step. A schematic of the problem domain decomposition and our spatial discretization scheme is shown in Fig. 1. The computational space is separated into three domains to characterize three distinct physical and physiological processes:

Fig. 1

The decomposition of the problem domain.

vessel network: inside the vessel, the oxygen transport is represented by the advection equation subject to the equilibrium of the free and hemoglobin bound oxygen:

(3)
An oriented graph representation was created to represent the vessel network: the axial line of each vessel was extracted and discretized by nodes and oriented edges (see Fig. 1). The connection (including branches) of the vessel network can be naturally expressed by the adjacency matrix [27] of the graph. We calculated a vessel volume associated with each vessel node and assumed uniform oxygen distribution within the volume.
vessel wall: the oxygen flux across a thin vessel wall is a diffusion process. To simplify analysis, we only discretized the exterior of vessel walls by a triangular surface mesh. Because the diffusion profile within the vessel wall layer is not the primary interest of this study, we used an oxygen conversation relationship based on Fick's law [28]:

(4)
where J_i is the oxygen flux (mol/m²/s) across the vessel wall at the i^th vessel wall node, A_i and V_i are the associated vessel wall area and tissue volume for this node. The right-hand-side of Eq. (4) represents the extracted oxygen from the vessel per unit time. The flux, J_i, is computed by

(5)
where K_w is the vessel wall permeability, w is the vessel wall thickness, C_F,i and C_F,i0 are the free oxygen concentrations at the exterior and interior surfaces of the vessel, respectively; α is the Bunsen solubility coefficient and equals 1.27 × 10^-15 μmol/(μm³ mmHg) [28].
tissue: the free oxygen that is transferred across the vessel wall diffuses through the whole tissue volume governed by the diffusion equation

(6)
where D_O2 is the tissue oxygen diffusion coefficient and the consumption of oxygen is governed by OC. The tissue volume is discretized by a tetrahedron mesh and is truncated by a bounding box.

In the following subsections, we detail our numerical implementation for solving the above models for each domain.

Vessel Blood Advection

Equation (3) is solved in two steps for each time advance. We first solve the two advection equations in Eq. (3). Integrated within the associated volume of each vessel node, the advection equations lead to the conservation equations of free and bound oxygen. Assuming that the adjacency matrix for the vessel graph is K, for the i^th vessel node (excluding the vessel end nodes) the conservation of oxygen implies that

(7)

where N_v is the total number of vessel nodes; k_i,j is the (i,j)^th element of K with a value of 0 if nodes i and j are not adjacent, 1 for in-flow from node j to i, and -1 for out-flow from node i to j; d_i,j is the distance between the i^th and j^th nodes and C_i and C_j are the oxygen concentrations (free or hemoglobin bound); v_i,j is the velocity, V_i and V_j are the associated vessel volumes of the two nodes. An implicit finite difference scheme is used for the time-derivative term in (7). This results in a linear equation with a form of

(8)

where θ

[0,1] is a constant, Δt is the time step and n is the time index. This can be rewritten in matrix form as

(9)

The equilibrium of C_F and C_B is achieved by solving

(10)

subject to the conservation of C_T.

Oxygen Flux Across Vessel Wall

From Eq.(4), we can get

(11)

which can be converted into an implicit time update as

(12)

The matrix form of Eq. (12) can be written as a linear equation defined between the vessel wall and axial nodes:

(13)

Tissue Oxygen Diffusion

To accommodate for the arbitrary shape of a complex vessel network, we used a tetrahedral mesh to discretize the tissue region outside the vessel. A finite element method (FEM) coupled with implicit time-stepping is used to solve for the diffusion of oxygen in the tissue.

Using the Galerkin's method, the FEM weak form of Eq. (6) can be written as [26]

(14)

where _i and _j are the basis functions in a tetrahedral element; [partial differential]

Ω is the surface of the tissue, and is a unit vector pointing to the normal direction of a surface element; < • > denotes volume integration inside an element. After applying the implicit method to discretize the time derivative in Eq. (14), we obtain

(15)

where the elements of matrices A_d and B_d and vector R are, respectively, defined by

Lumped System

The discretized advection equation, Eq. (9), flux equation, Eq. (13) and diffusion equation, Eq. (15), are defined on the vessel axial nodes, vessel surface/axial nodes and vessel surface/tissue nodes, respectively. In order to solve the entire domain, we can assemble these equations into a lumped system by assigning a global index for the nodes located in three regions. Because of the nonlinear nature of Eq. (10), we solve for the advection separately. We combine Eq. (13) and Eq. (15) as

(16)

Then, we solve for the time evolution of the oxygen concentration (including both free and hemoglobin bound oxygen) by solving Eqs. (9), (10), and (16) sequentially.

We also note that the unconditional stability of implicit time-stepping may no longer hold due to the presence of Eq. (10). A rigorous derivation of the stability condition for this model is beyond the scope of this paper. We confirmed the stability of our solution by obtaining identical solutions with smaller time steps.

Boundary Conditions

Equations (9) and (16) must be solved with proper boundary conditions. The boundary nodes include all the nodes on the exterior surface of the domain, which are either tissue surface nodes or the in-flow/out-flow vertices of vessel axes. For the tissue surface nodes, we used a mirror boundary condition. i.e.

(17)

This effectively eliminates the surface integration term in Eq. (14) and the expression for the elements of the vector R is simplified to

For the vessel axes end nodes, the flow rates at all in-flow peripheral points are specified (type-1 boundary condition) and a sink condition is used for the out-flow peripheral points.

2.2 Two-photon microscopy measurements of rodent microvasculature network

Sprague Dawley rats (250-320 g) were initially anesthetized with isofluorane and ventilated with a mixture of air and oxygen during surgical procedures. Cannulas were inserted in the femoral artery and vein and heart rate, blood pressure, body temperature and blood gases were monitored. A cranial window 3 mm × 3 mm in size was opened in the parietal bone and dura was removed. The window was filled with agarose and sealed with a coverslip. During the measurement isofluorane was discontinued and anesthesia was maintained with 50 mg/kg intravenous bolus of alpha-chloralose followed by continuous intravenous infusion at 40 mg/(kg h). All experimental procedures were approved by the Massachusetts General Hospital Subcommittee on Research Animal Care.

Blood plasma was labeled with Dextran-conjugated fluorescein (FD 2000S, Sigma-Aldrich; 500 nM concentration in blood). Imaging of the cortical vasculature was performed with a two-photon microscope (Ultima, Prairie Technology Inc.) at 800 nm and with an Olympus 40X water immersion objective (NA = 0.8). The individual 256 × 256 pixels frames in the X-Y planes perpendicular to the optical axis spanned 230 μm × 230 μm. They were obtained as an average of four images in order to smooth the appearance of the red blood cells (not labeled with the dye) and to improve further image analysis. The step size between frames along the optical axis was set to Δz = 1 μm and adjustment of optical power was made continuously with the imaging depth to address increased scattering and absorption along the optical path-length. An image stack was acquired over a depth of 450 μm.

2.3 Graphing of the vessel network

Our two-photon microscopy images of the vascular network have a high contrast-to-noise ratio enabling easy identification of the vascular networks. Rather than employing an automatic segmentation procedure for graphing the vascular network, we manually graph the network using custom software to accelerate the process. The software enables us to place node points and connecting edges along the vessels. Vessel diameter is estimated at each node by thresholding the image at a low value of approximately 2% of the maximum image intensity, but still above the image noise, considering lines through the node point oriented every 3 degrees in the local X-Y plane perpendicular to the vessel axis, and finding the minimum distance from vessel edge to vessel edge. At the graph end points we need to establish boundary conditions for velocity or blood pressure. We set the blood pressure boundary condition for the major arterioles and venules and use a zero velocity boundary condition for minor vessel branches that do not connect back to our draining vein. The vascular resistance for each edge between 2 node points is given by Poiseuille's law

(18)

where η is the viscosity of blood (η = 15×10^-6 mmHg s [29]), l is the length of the vessel, and d is the vessel diameter. Given the resistor network and boundary conditions on blood pressure and velocity, it is straight-forward to solve the set of simultaneous equations for blood pressure and blood velocity at every node point. We follow the procedure we described previously, in [30].

3. Results

In this section, we first present our simulation results for a simple vessel network: a single vessel across the tissue. Using these results, we validate our model proposed in the previous section for both steady-state and dynamic simulations. In the second half of this section, we apply our analysis to a more complex vessel network extracted from a two-photon microscopy scan of a rodent somatosensory cortex. These simulations further demonstrate the accuracy and scalability of the proposed model. We note that, while our calculations are for the concentration of oxygen, we report the partial pressure of oxygen (PO₂) in our figures as it is commonly reported in experimental papers. The PO₂ is related to the oxygen concentration, C, by PO₂ = C/α.

3.1 Validation

The tissue that we are simulating is 100 × 100 × 100 μm³ in size and contains a single blood vessel with diameter of d = 10 μm. The vessel is passing through the center of the tissue and parallel to the x-axis. Unless otherwise stated, for this simple simulation the velocity of blood inside the vessel is 400 μm/s, hematocrit is set to 0.1, and PO₂ = 90 mmHg at the input boundary of the vessel. The vessel wall permeability is K_w = 1.115×10^-12 μmol/(μm s mmHg) and wall thickness is 1 μm. The oxygen diffusion coefficient is D_O2 = 2.4×10⁶ μm²/s. Oxygen consumption is 41.7×10^-16 μmol/(μm³s), or 10% of the baseline value for the rat cortex [31]. A mesh with 25554 nodes and 120942 tetrahedral elements was created using our customized mesh generator. The meshing process took about 14 seconds on a 1.8GHz Intel Xeon (64bit) PC and building the matrices in Eqs. (9) and (16) took about 11 seconds. Subsequently, we solved Eqs. (9), (10) and (16) for about 1.3 seconds per time step.

In Fig. 2(a) we show a contour plot of the steady state PO₂, obtained for the above simulation parameters in X-Y plane that contains the vessel axis. We see a high PO₂ for the inflowing blood on the left (starting at 90 mmHg), diminishing as the blood flows across the vessel to a minimum of 64 mmHg indicated by the last contour on the right. In Fig. 2(b), (c), and (d), we plot the PO₂ along the interior of the vessel for various values of the blood velocity, hematocrit, and tissue oxygen consumption, respectively. As we increase velocity from 0.4 mm/s to 0.8 mm/s and 1.6 mm/s, we see that the PO₂ is increasing as more oxygen is being delivered by the blood per unit time, but the oxygen extraction by the tissue is constant, and thus the oxygen extraction fraction is expected to decrease. Similarly, as we increase the hematocrit from 0 to 0.2 we see that PO₂ increases as more oxygen is delivered by the blood. When tissue oxygen consumption is increased, the PO₂ in the blood decreases, as expected. The difference between the oxygen flowing into the volume per unit time and the oxygen flowing out should be equal to the tissue volume integral of the oxygen consumption. Assuming the uniform oxygen consumption (OC, in μM/s), the oxygen conservation requires

Fig. 2

Model validations with a single vessel: (a) PO₂ distribution across an X-Y plane along the vessel; (b-d) PO₂ along the vessel for different values of (b) blood velocity, (c) hematocrit, and (d) oxygen consumption.

(19)

where v is the velocity of blood, C_T,in and C_T,out are the total concentrations of oxygen at the input and output boundaries of the blood vessel, respectively, and V_tis is the tissue volume. The left-hand-side of Eq. (19) denotes the total oxygen extracted from the vessel per unit time while the right-hand-side denotes the quantity of oxygen consumed by the tissue.

To validate the steady-state solution obtained by our finite element model, we calculated the PO2 distributions under various velocities, hematocrit rates and OC values, and the solutions are shown in Fig. 2. The expected conservation relationship, i.e. Eq. (19), holds for all the steady-state simulations.

The second step is to validate the model for dynamic processes. In these simulations, we separately explored the dynamics of oxygen advection within the vessel, flux across the vessel membrane and diffusion in the tissue and compared the calculations with simple analytic solutions. These results are shown in Fig. 3 and detailed below.

Fig. 3

Validation of dynamic processes with a single vessel. (a) PO₂ profiles along the vessel at 20 ms intervals for isolated advection. The modeled PO₂ profile is given by the solid line. The star represents the expected advancement of oxygen pressure profile (more ...)

Oxygen advection within the vessel was isolated from oxygen diffusion by setting the vessel wall permeability K_w = 0. We started with no oxygen in the vessel and introduced 90 mmHg of oxygen pressure at the input of the vessel ramping down to 0 mmHg at 40 μm into the vessel. Fig. 3(a) shows the oxygen pressure profiles along the vessel at 20 ms time intervals. We see that the oxygen wavefront is advancing almost 8 μm every 20 ms, consistent with the velocity of 0.4 mm/s. It is known that a two-level implicit advection solver produces solutions that underestimate the true velocity at high spatial frequencies and accurately estimate the velocity at low spatial frequencies [26]. This is evident in Fig. 3(a) with smoothing of the edges at 90 mmHg and 0 mmHg as the oxygen advects along the vessel. We note that this is an intrinsic limitation of the discretization scheme, and although we have implemented the most accurate solver, it places limits on the accuracy of our solution for oxygen content varying with high spatial frequency along a vessel. As noted in Ref. [26], this limitation can be reduced by using a refined time step or smaller grid spacing.

Flux of oxygen across the vessel wall was validated by fixing the vessel PO₂ at 90 mmHg, initiating the tissue PO₂ at 0 mmHg, setting the tissue oxygen consumption to 0, and increasing the oxygen diffusion coefficient within the tissue by a factor of 1000 such that the system dynamics would be limited by the vessel wall permeability K_w. In Fig. 3(b), we plot the tissue temporal evolution of PO₂, which is increasing to 90 mmHg due to oxygen flux across the permeable vessel wall. When we reduce K_w by a factor of 2, we see that the rate of increase for the tissue PO₂ is reduced. The rate of change in the total oxygen content of the tissue volume is given by Fick's law [28] as

(20)

where N_O2 is the number of moles of oxygen in the vessel or tissue, d is the diameter of the vessel, l is the vessel length, w is the vessel wall thickness, and we assume that any oxygen that crosses the vessel wall distributes uniformly throughout the tissue volume effectively immediately (i.e., the diffusion time constant is much shorter than the vessel wall flux time constant). Under these conditions, the solution of Eq. (20) is a simple exponential. Note that

(21)

where x indicates either tissue (tis) or vessel (ves). The solution of Eq. (21) is shown in Fig. 3(b) and agrees well with the solution of our finite element model.

Oxygen diffusion was validated by setting oxygen consumption to 0 and initiating the tissue PO₂ to 0 mmHg except for a single volume element far from the boundaries of the tissue volume that was given a unit quantity of oxygen. This oxygen was then diffused with our finite element model. The early responses of diffusion after 20 (blue), 25 (green), and 30 ms (red) are plotted in Fig. 3(c) and they roughly agree with the analytic solution of the diffusion equation for a homogeneous infinite medium. We also computed the finite element solution reducing the diffusion coefficient by a factor of 2 and plot the results at 40 (blue), 50 (green), and 60 ms (red) and observed the expected D_O2t scaling of the diffusion equation. That is, if we half the diffusion coefficient and double the time, the analytic solution [32] predicts the same solution.

Tissue oxygen consumption was validated by setting PO_2,tis to 90 mmHg throughout the tissue volume, inhibiting oxygen flux across the vessel wall by setting K_w = 0, and increasing oxygen consumption. Our simulation result (not shown) demonstrates a linear decrease in oxygen concentration in the tissue with time, as we expected.

3.2 Application to a microvessel network

After validating the steady state and dynamic solutions of our oxygen advection-diffusion solver, we proceed in solving for the delivery of oxygen to tissue by a realistic microvessel network. We obtain a volumetric image of the microvessel network from the somatosensory cortex of a rat using two-photon microscopy as described in the methods. The volumetric image spanning 230 × 230 × 450 μm³ is shown in Fig. 4(a). In this image, we identify a 25 μm in diameter surface pial arteriole in the upper right delivering oxygenated blood to the tissue and a draining ascending venule in the upper left of the image, connected by the intervening capillary network.

Fig. 4

Anatomical vessel network extracted from two-photon scans of a rodent somatosensory cortex. (a) Raw image (Media 1, View 1). (b-d) Top view projections of the 3D maps of (b) vessel types: arterioles - red, venules - blue, and capillaries - green, (c) (more ...)

We manually graphed this vessel network in under 2 hours using custom software to accelerate the process, by placing node points and connecting edges along the vessels. The diameter of the vessel at each node point, the blood pressure, and velocity of blood was calculated as described in the methods. The velocity at each node in the vascular graph was used in solving the oxygen advection diffusion equation to obtain the partial pressure of oxygen at every node point in the vessels and throughout the tissue. The vessel in-flow and out-flow points (i.e., the boundary points as mentioned in Section 2.1) were manually labeled and used for the subsequent advection modeling. The generated 3D mesh for this case contains 138685 nodes and 607406 elements. The total time for mesh generation, calculating the matrices and solving for each time step are 136, 62 and 6 seconds, respectively.

In Fig. 4(b) we plot the graphed vascular network identifying the arterioles (red), capillaries (green), and venules (blue). We see that the surface pial arteriole branches twice. The left-hand branch propagates along the surface to the lower left of the image and then descends 450 μm to the bottom of the image without branching. The right-hand branch descends into the tissue at the lower right. This descending arteriole quickly branches a few times leading to a capillary network that connects with an ascending venule on the upper left of the image. The velocity of blood along the vascular network is shown in Fig. 4(c). The velocity has a maximum of 10 mm/s in the pial arteriole and reaches a minimum of ~1 mm/s in the capillaries and draining venules. The partial pressure of oxygen along the network is graphed in Fig. 4(d), showing the expected decrease from feeding arteriole to draining venule.

The median velocity, blood pressure, and partial pressure of oxygen versus vessel diameter are shown in Fig. 5. Our pressure distribution compares well with a survey of experimental data [33]. Our velocity and partial pressure of oxygen, however, drop below experimental data [33, 34] in the capillaries and venules. This is likely a result of our incomplete vascular graph not accounting for the surrounding microvessels that are providing more blood into the draining vein and delivering more oxygen to the tissue. We will correct this in the future by considering a vascular graph over a larger volume of tissue in order to obtain a more completely connected graph in the central regions.

Fig. 5

The (a) median velocity, (b) blood pressure and (c) partial pressure of oxygen versus vessel diameter compared with experimental data from the literature. The experimental data in panel (a-b) were derived from [33] and (c) from [34].

The partial pressure of oxygen within the tissue is shown in Fig. 6 in slices at different depths and in a movie of the volumetric data. From these data, we see the high tissue PO₂ around the arterioles dropping off rapidly with distance, while the lower PO₂ around the capillaries is much more spatially uniform. In Fig. 7 we plot the drop in PO₂ versus distance from selected arterioles, capillaries, and venules. These results qualitatively agree with experimental in-vivo measurements [34].

Fig. 6

The 3D rendering (left, Media 3, View 2) and cross-sections (right) of the PO₂ distributions in the vessel and tissue.

Fig. 7

The tissue PO₂ versus distance from a vessel wall of a few selected (a) arterioles, (b) capillaries and (c) venules. Different colors represent different vessels selected for this test.

4. Summary

We have described a hybrid numerical algorithm and a general work-flow to model the 3D time-varying oxygen advection diffusion process in an anatomical microvascular network. This framework provides a quantitative and computationally feasible approach for modeling the dynamic processes in the microvessels in the tissue, which is becoming increasingly important in studies of cerebro-vascular physiology and tumor physiology. The proposed algorithm is structurally generic and readily applicable to vessel networks with varying degrees of complexity. The graph representations of the vessel network and finite-element modeling also provide a flexible spatial discretization scheme, which is important to balance computational expense and accuracy. Solving the 3D dynamic diffusion-advection process not only makes this framework useful for modeling tissue oxygen delivery and consumption, but is also extendable to include other molecules such as NO and CO₂, or to model the pharmacokinetic processes in drug delivery. Future efforts for this framework include automatic graphing of the vessel network from 3D microscopic images and an in-depth understanding of the stability condition. We will also apply this method to full-sized vessel networks to characterize the microscopic details of oxygen delivery during brain activation and to validate macroscopic NIRS and functional magnetic resonance imaging (fMRI) measurements of oxygen delivery.

Acknowledgments

The authors would like to acknowledge funding support from NIH R01NS057476, P01NS055104 and R01NS051188, and from the Air Force Office of Scientific Research, Medical Free Electron Laser Program Contract FA9550-07-1-0101. The authors would also like to acknowledge the assistance of Shuai Yuan, Mark Shalinsky, and Weicheng Wu for in vivo experiments, and the help from Wes Turner (Kitware Inc.) and Scott Dineen (OSA) for preparing the interactive datasets.

Footnotes

OCIS codes: (180.6900) Three-dimensional microscopy.

References and links

1. Cope M, Delpy DT. System for long-term measurement of cerebral blood flow and tissue oxygenation on newborn infants by infra-red transillumination. Med Biol Eng Comput. 1988;26:289–294. [PubMed]

2. Villringer A, Chance B. Non-invasive optical spectroscopy and imaging of human brain function. Trends Neurosci. 1997;20:435–442. [PubMed]

3. Gibson AP, Hebden JC, Arridge SR. Recent advances in diffuse optical imaging. Phys Med Biol. 2005;50:R1–43. [PubMed]

4. Grinvald A, Lieke E, Frostig RD, Gilbert CD, Wiesel TN. Functional architecture of cortex revealed by optical imaging of intrinsic signals. Nature. 1986;324:361–364. [PubMed]

5. Briers JD. Laser Doppler, speckle and related techniques for blood perfusion mapping and imaging. Physiol Meas. 2001;22:R35–66. [PubMed]

6. Dunn AK, Bolay H, Moskowitz MA, Boas DA. Dynamic imaging of cerebral blood flow using laser speckle. J Cereb Blood Flow Metab. 2001;21:195–201. [PubMed]

7. Villringer A, Them A, Lindauer U, Einhaupl K, Dirnagl U. Capillary perfusion of the rat brain cortex. Circ Res. 1994;75:55–62. [PubMed]

8. Kleinfeld D, Mitra PP, Helmchen F, Denk W. Fluctuations and stimulus-induced changes in blood flow observed in individual capillaries in layers 2 through 4 of rat neocortex. Proc Natl Acad Sci U S A. 1998;95:15741–15746. [PubMed]

9. Plant RL, Burns DH. Quantitative, depth-resolved imaging of oxygen concentration by phosphorescence lifetime measurement. Applied Spectroscopy. 1993;47:1594–1599.

10. Itzkan I, Qiu L, Fang H, Zaman MM, Vitkin E, Ghiran IC, Salahuddin S, Modell M, Andersson C, Kimerer LM, Cipolloni PB, Lim KH, Freedman SD, Bigio I, Sachs BP, Hanlon EB, Perelman LT. Confocal light absorption and scattering spectroscopic microscopy monitors organelles in live cells with no exogenous labels. Proc Natl Acad Sci U S A. 2007;104:17255–17260. [PubMed]

11. Estrada AD, Ponticorvo A, Ford TN, Dunn AK. Microvascular oxygen quantification using two-photon microscopy. Opt Lett. 2008;33:1038–1040. [PubMed]

12. Wang Y, Bower BA, Izatt JA, Tan O, Huang D. In vivo total retinal blood flow measurement by Fourier domain Doppler optical coherence tomography. J Biomed Opt. 2007;12:041215. [PubMed]

13. Cense B, Chen TC, Nassif N, Pierce MC, Yun SH, Park BH, Bouma BE, Tearney GJ, Boer deJF. Ultra-high speed and ultra-high resolution spectral-domain optical coherence tomography and optical Doppler tomography in ophthalmology. Bull Soc Belge Ophtalmol. 2006:123–132. [PubMed]

14. Wang RK, Jacques S, Ma Z, Hurst S, Hanson S, Gruber A. Three dimensional optical angiography. Optics Express. 2007;15:4083–4097. [PubMed]

15. Orbach HS, Cohen LB, Grinvald A. Optical mapping of electrical activity in rat somatosensory and visual cortex. J Neurosci. 1985;5:1886–1895. [PubMed]

16. Baker BJ, Kosmidis EK, Vucinic D, Falk CX, Cohen LB, Djurisic M, Zecevic D. Imaging brain activity with voltage- and calcium-sensitive dyes. Cell Mol Neurobiol. 2005;25:245–282. [PubMed]

17. Chance B, Graham N, Mayer D. A time sharing fluorometer for the readout of intracellular oxidation-reduction states of NADH and flavoprotein. Rev Sci Instrum. 1971;42:951–957. [PubMed]

18. Kasischke KA, Vishwasrao HD, Fisher PJ, Zipfel WR, Webb WW. Neural activity triggers neuronal oxidative metabolism followed by astrocytic glycolysis. Science. 2004;305:99–103. [PubMed]

19. Elwell CE, Henty JR, Leung TS, Austin T, Meek JH, Delpy DT, Wyatt JS. Measurement of CMRO2 in neonates undergoing intensive care using near infrared spectroscopy. Adv Exp Med Biol. 2005;566:263–268. [PubMed]

20. Zheng Y, Johnston D, Berwick J, Chen D, Billings S, Mayhew J. A three-compartment model of the hemodynamic response and oxygen delivery to brain. Neuroimage. 2005;28:925–939. [PubMed]

21. Huppert TJ, Allen MS, Benav H, Jones PB, Boas DA. A multicompartment vascular model for inferring baseline and functional changes in cerebral oxygen metabolism and arterial dilation. J Cereb Blood Flow Metab. 2007;27:1262–1279. [PMC free article] [PubMed]

22. Huppert TJ, Allen MS, Diamond GS, Boas DA. Inferring cerebral oxygen metabolism from fMRI with a dynamic multi-compartment Windkessel model. Human Brain Mapping. 2008 in press.

23. Franceschini MA, Thaker S, Themelis G, Krishnamoorthy KK, Bortfeld H, Diamond SG, Boas DA, Arvin K, Grant PE. Assessment of infant brain development with frequency-domain near-infrared spectroscopy. Pediatr Res. 2007;61:546–551. [PMC free article] [PubMed]

24. Beard DA, Bassingthwaighte JB. Modeling advection and diffusion of oxygen in complex vascular networks. Ann Biomed Eng. 2001;29(4):298–310. [PubMed]

25. Lobdell DD. An invertible simple equation for computation of blood O2 dissociation relations. J Appl Physiol. 1981;50(5):971–973. [PubMed]

26. Lynch DR. Computational Partial Differential Equations for Environmental Scientists and Engineers – A First Practical Course. Springer; 2005.

27. Godsil C, Royle G. Algebraic Graph Theory. Springer-Verlag; 2001.

28. Popel AS, Pittman RN, Ellsworth ML. Rate of oxygen loss from arterioles is an order of magnitude higher than expected. Am J Physiol Heart Circ Physiol. 1989;256(3 Pt 2):H921–4. [PubMed]

29. Haidekker MA, Tsai AG, Brady T, Stevens HY, Frangos JA, Theodorakis E, Intaglietta M. A novel approach to blood plasma viscosity measurement using fluorescent molecular rotors. Am J Physiol Heart Circ Physiol. 2002;282:H1609–H1614. [PubMed]

30. Boas DA, Jones SR, Devor A, Huppert TJ, Dale AM. A vascular anatomical network model of the spatio-temporal response to brain activation. Neuroimage. 2008;40(3):1116–29. [PMC free article] [PubMed]

31. Herman P, Trubel HK, Hyder F. A multiparametric assessment of oxygen efflux from the brain. J Cereb Blood Flow Metab. 2006;26:79–91. [PubMed]

32. Arfken G. Mathematical Methods for Physicists. 3rd. Orlando, FL: Academic Press; 1985.

33. Lipowsky HH. Microvascular rheology and hemodynamics. Microcirculation. 2005;12:5–15. [PubMed]

34. Vovenko E. Distribution of oxygen tension on the surface of arterioles, capillaries and venules of brain cortex and in tissue in normoxia: an experimental study on rats. Pflugers Arch. 1999;437:617–623. [PubMed]