Abstract

A theory is developed which relates quasi-elastic light scattering measurements to blood flow in tissue microvasculature. We assume that the tissue matrix surrounding the blood cells is a strong diffuser of light and that moving erythrocytes, therefore, are illuminated by a spatially distributed source. Because the surrounding tissue is considered to be stationary, Doppler shifts in the frequency of the scattered light arise only from photon interactions with the moving blood cells. The theory implies that the time decay of the photon autocorrelation function scales proportionally with cell size and inversely with mean translational speed. Analysis of multiple interactions of photons with moving cells indicates the manner in which spectral measurements additionally are sensitive to changes in blood volume. Predictions are verified by measurements of particle flow in model tissues.

© 1981 Optical Society of America

I. Introduction

Several research groups have been working to develop laser Doppler light scattering instrumentation to measure flow within blood vessels[1][4] or vascularized tissue.[5][11] Particularly promising for clinical applications are noninvasive schemes,[3],[5][11] where a tissue surface is illuminated and information about blood flow is obtained by analyzing spectral components of backscattered light. Our own interests are to develop techniques for examining local blood flow in tissues which are vascularized primarily by small blood vessels, namely, capillaries, venules, and arterioles.[12],[13] Examples are skin, gums, skeletal muscle, and the surfaces of organs and body extremities. These entities typically glow with a diffuse backscattered pattern when irradiated with a narrow incident beam. Because the scattering cross section for particles of the size of blood cells—predominantly erythrocytes (red blood cells)—is sharply peaked in the forward direction (mean scattering angle 5.4°), such radiation mostly contains photons which have been scattered at least once by surrounding tissue elements. Very little (<0.1%) of the observed backscattered luminescence derives from photons scattered only by the flowing blood cells.

The optical structure of the complex tissue matrix which surrounds the blood vessels generally cannot be characterized in detail. However, we shall show that, if the tissue scattering cross section is much greater than that of the moving blood cells, a rather general model can be devised which is applicable to a wide variety of human and animal tissues. This model can guide instrumental design and use, particularly of a specialized electronic processor for extracting flow parameters from detected fluctuations in photocurrent.

Photons generally suffer several collisions with somatic cells, connective tissue, blood vessel walls, etc. before interacting with a blood cell [Fig. 1(A)]. The effect of such interactions is to randomize the direction of the light which is incident upon the moving erythrocytes. These photons can be scattered into a small forward angle by a blood cell and then scattered back toward the detector by the tissue matrix [Fig. 1(B)]. More complicated scenarios can be envisioned, including sequential scattering by the moving particles [Fig. 1(C)]. Here we examine a simplified theoretical model which takes these features into account, particularly the observed diffuse tissue luminescence and expected small angle scattering of the erythrocytes (Secs. II and III). We also discuss the consequences of photons scattering from more than one moving cell before emerging from the tissue (Sec. IV).

The model leads to predictions of the way in which photon autocorrelations (Secs. III and IV) and photocurrent power spectra (Sec. V) depend on variables of the moving particles such as angular scattering cross section, size, number density, and speed distribution. These theoretical implications have been tested experimentally with a tissue analog in which particles flow through a fiber capillary bundle surrounded by a medium which is a diffuse scatterer of light (agarose impregnated with 0.055-μm radius polystyrene microspheres). The results of these studies are given in Sec. VI.

In particular, we show that the normalized first moment of the spectrum, 〈ω〉, is proportional to the rms speed of the moving particles 〈V21/2 as

ω=V21/2β(12ξ)1/2af(m¯),
where a is the radius of an average spherical scatterer, ξ is an empirical factor which is related to the shape of the cells [see Eq. (13′)], and β is an instrumental factor which is defined in Eq. (7). The variable m¯ is the average number of collisions which a detected photon makes with a moving cell; the function f(m¯) is linear with tissue blood volume for m¯1 (heterodyne) and varies as the square root of tissue blood volume for m¯1 [see Eq. (36)]. Here Eq. (1) is given prominence because 〈ω〉 has been taken as an indication of blood flow in instrumentation presently being developed for clinical applications.[11] An abbreviated version of certain aspects of this theory has been presented previously.[8]

II. Theoretical Model

Several investigators have undertaken mathematical descriptions of the propagation of light in whole blood. The intense scattering and strong absorption which occurs at normal hematocrit values implies that photon diffusion theory or angle dependent transport theory must be used for modeling in that instance (see, e.g., the papers cited in [Ref. 14]). The present situation, i.e., the transmission and scattering of light in the microvasculature, is somewhat simpler because the number of red cells in moderately perfused tissue (0.5–4% by volume of tissue) is much lower than in whole blood (40% v/v). However, scattering of photons by tissue elements surrounding the blood vessels must be taken into account. Although the manner in which light diffuses throughout that tissue matrix cannot be specified in detail, if one assumes the latter to be stationary (or moving very slowly), considerable progress can be made in understanding how the observed photon autocorrelation function (or spectrum) relates to the movement of the blood cells.

We shall now show that the time autocorrelation function of detected photons which undergo a multiple step diffusion in tissue (Fig. 1: A,A,…A,B,A,…A) can be expressed in terms of an intermediate scattering function I1(τ) given as

I1(τ)=ππS[Q(θ)]exp[iQ(θ)·ΔR(τ)]sinθdθππS[Q(θ)]sinθdθ,
where ΔR(τ) is the displacement of the center of mass of a moving cell during time τ (the expectation is taken over the velocity distribution of the cells), and S(Q) is the structure factor of an average scatterer [see Eqs. (5′) and (13)]. The Bragg scattering vector Q(θ) has the magnitude
Q(θ)=4πnλsin(θ/2)=2ksin(θ/2).
The normalized photon autocorrelation function decays as I12(t) if all the detected photons are scattered exactly one time by moving red cells before they leave the irradiated tissue. In general, however, multiple scattering must be taken into account and a complex dependency exists between measured data and I1(τ) [see Eq. (24) below].

Equation (2) can be derived from the following argument. We consider each element of the static tissue matrix to be a separate irradiator. If all scattering centers within the tissue were immobile, the relative phase, in time, of the electromagnetic field incident from the laser would be preserved at all locations within the tissue; i.e., although the intensity of these distributed sources would differ at each location and a spatial point-to-point phase relationship could not be specified, the time coherence of each source would be homologous with the incident field. (The scattering would be described by the sequence A,A,…A shown in Fig. 1.) Changes in temporal phase occur because the red cells move [Fig. 1(B)]. These phase changes in effect are detected by other static tissue elements which, if no subsequent scattering by a red cell occurs, act as direct links to the external detector (a series of steps from Fig. 1: A,A,…,A,B,A,…A); that is, static tissue elements scatter to other static tissue elements and, ultimately, to the detector, yet preserving relative temporal phase in the same way that phase information is preserved in an incident pathway. Of course, each time that a photon interacts with another moving red cell, an additional time-dependent phase shift occurs.

In a distributed network of microvessels, succeeding temporal phase shifts [(Q · vt)] will be totally independent of earlier ones. In contrast, when light diffuses in large blood vessels, e.g., retinal vessels[3] or femoral veins,[4] it might be scattered by several red blood cells whose velocities and orientations are strongly correlated (v1·v2v12). However, if the lack of correlation of the scattering vectors Q1 with Q2 (i.e., 〈Q1 · Q2〉 = 0) from sequential red blood cell scattering is sufficient to randomize the series Doppler shifts in such large vessels, results developed here for uncorrelated scattering by cells in different microvessels nonetheless could be applicable.

The wave front emanating from any given elemental tissue source may be approximated by a plane wave propagating along the vector directed from the illumination center to an erythrocyte. Deviations from planarity are not significant as long as the distance between the tissue source point and the moving cell is large compared with the displacement ΔR(τe) = |rj(τe) − rj(0)|, where τe is the phase correlation time of the field (time such that I1(τe) ∼ 0.3). For moving red blood cells, ΔR(τe) is ∼2 μm. Let E(ρ) be the magnitude of the field which derives from a static scattering center at location ρ within the tissue; also designate θ(ρ) as the angle between a line drawn from ρ to a moving red cell, and a line drawn from that cell to an arbitrary tissue element located at some point rd which, by the argument in the preceding paragraph, acts as the primary detector locus [see Fig. 1(B)]. If this tissue element is sufficiently far from the erythrocyte, the scattered field at the physical detector is[15]

escj(t)=E(ρ)exp(ω0t)A[Q(θρ)]×exp{i[Q(θρ)·rj(t)+φ(ρ)+φ(rd)]},
where rj, is the position of the center of mass of the (jth) blood cell, φ(ρ) is the phase shift (relative to an arbitrary reference) of the incident field at ρ, and φ(rd) similarly is the phase shift occurring as the scattered light diffuses through the tissue after being scattered by an immobile element at rd. We assume that φ(ρ) is statistically uncorrelated with the phase shift at other source points in the tissue matrix. A(Q) is a scattering function which is related to the optical structure of the scatterers.

Let us consider the erythrocytes to be spherically symmetric. (For our present calculations we only require that the directions of cell movement are uncorrelated with shape and that any significant changes in cell morphology occur slowly compared with τe.) In the Rayleigh-Gans limit,[16] A(Q) thus is given as

A(Q)=particlevolumeα(r)exp(iQ·r)d3r,
where α(r) is the distribution of excess polarizability (the difference between the polarizability within the scatterer and that of the surrounding medium) which, for a homogeneous particle, is proportional to the mass distribution. The structure factor S(Q) is defined in terms of A(Q) as
S(Q)=|A(Q)|2.

The field at the tissue point rd has components representing scattering from sources which are distributed throughout the tissue. Thus, an integration must be performed over the term eSC given in Eq (4), i.e.,

sc(rd,t)tissued3ρE(ρ)A[Q(θρ)]×expi[Q(θρ)·rj(t)+φ(ρ)].
It is impossible to specify what fraction of the light which is secondarily scattered by the tissue detector at rd will ultimately reach the external photodetector. The important point again is that each fixed diffusion pathway will impart a unique phase shift to the light as it travels between rd and the external detector. If no interactions with other moving cells occur, the field at the external detector, deriving from a scattering event with a red blood cell located at rj and summed over all the possible subsequent scattering paths, is given as
Esc(t)tissueD(rd)exp[iφ(rd)]sc(rd,t)d3rd,
where D(rd) is the fraction of the field amplitude at rd which ultimately is transmitted to the photodetector.

We measure the normalized photon autocorrelation function [〈n(t)n(t + τ)〉/〈n2] of photons scattered by moving red blood cells as well as photons which interact only with static tissue elements. The total intensity of the light falling upon the photodetector is designated as i0, and the intensity of that portion which arises from photons which have interacted with moving cells is designated as iSC. Since the detected light arises from a large number of essentially uncorrelated events, we can assume that the field statistics of the light scattered by the moving cells are Gaussian, in which case [〈n(t)n(t + τ)/〈n2] can be expressed as[17]

g(2)(τ)=n(t)n(t+τ)n2=1+β[(isci0)2|I(τ)|2+2isc(i0isc)i02I(τ)],
where 0 < β < 1 is a factor which primarily depends upon the optical coherence of the signal at the detector surface,[15],[17] and I(τ) is the intermediate scattering function of the Doppler shifted light
I(τ)Esc*(t)Esc(t+τ).
Thus, the contribution to I(τ) by events involving single scattering from a moving blood cell (A,A,…, A,B,A,…, A), which derives from the term given in Eqs. (6) and (6′), is
I1(τ)D(rd)d3rdE*(ρ)A*[Q(θρ)]×exp{i[Q(θρ)·rj(t)+φρ+φrd]}d3ρ·D(rd)d3rdE(ρ)A[Q(θρ)]×exp{i[Q(θρ)·rj(t+τ)+φρ+φrd]}d3ρ.
{We take Q to be defined with respect to the position of the target red cell at time t [see discussion appearing before Eq. (4)].}

The phase shifts [φ(ρ)] and [φ(rd)] are uncorrelated with any other terms, so the expectation in Eq. (9) factors into a product, two multiplicands of which are 〈exp[−(ρ′) exp[(ρ)]〉 and exp[iφ(rd)exp[iφ(rd)]; also, because the phase shifts at different points within the tissue are unrelated, these quantities factor as 〈exp[−(ρ′)]〉 〈exp[(ρ)]〉 and exp[iφ(rd)] 〈exp[(rd)]〉 unless ρ′ = ρ and rd=rd However, since all values of φ between 0 and 2π are equally probable, we find 〈exp()〉 = 0, from which we infer that the expectation in Eq. (9) can be expressed as [see Eq. (5′)]

I1(τ)|D(rd)|2d3rdi(ρ)S[Q(θρ)]×exp{iQ(θρ)·[rj(t+τ)rj(t)]}d3ρ.
The factor |D(rd)|2d3rd is a time invariant term which can be ignored.

Because the photon distribution arising from the diffusion of light through the static tissue is imprecisely known, we now assume that a cell is irradiated with equal intensity from all directions, i.e., that we have 4π illumination. Thus, the numerator in Eq. (2) is obtained upon transforming the integration of ρ in Eq. (10) to an integration over the scattering angle θ and taking account of the geometric solid angle which locates an annulus of tissue sources with respect to the detector [from which the sinθ term in Eq. (5) follows]. We note that scattering occurs from many erythrocytes but assume that the cells move independently and are statistically identical. We thus can associate the term 〈exp{iQ · [rj(t + τ) − rj(t)]}〉 with 〈exp[iQ · ΔR(τ)]〉, where ΔR(τ) is the center of mass translation of a (typical) cell over a period of time τ. Finally, the expression given for I1(τ) in Eq. (2) is obtained by normalizing such that limτ→0I1(τ) = 1.

III. Results for a Specific Speed Distribution and Particle Shape

Equation (2) has been derived by assuming that the scattering system consists of identical, spherically symmetric, particles (or particles whose orientation is uncorrelated with direction of movement), illuminated by a light source which has been totally randomized in direction. The particles are considered to move independently, and interparticle optical interference is neglected. We now determine the form of I1(τ), using specific particle structure factors S(Q) and speed distributions in Eq. (2).

To compute the expectation 〈exp[iQ · ΔR(τ)]〉, one must first choose a model of blood flow through the vascular bed. It is simplest to consider the blood to be percolating through a randomly directed network of capillaries, with trajectories which are undisturbed for times which are long compared with the relaxation of I1(τ). The latter assumption can be easily justified, as decay times τ1/e of photon autocorrelation functions measured for capillary flow typically are of the order of 10−4–10−3 sec (see Sec. VI and [Refs. 5][11]), whereas blood cells move steadily and without dramatic shape change for tenths of a second or longer.[18],[19] The expectation 〈exp[iQ · ΔR(τ)]〉 for spherically symmetric particles moving with constant velocities in random directions is[20]

exp[iQ·ΔR(τ)]=0j0(QVτ)P(V)dVj0(QVτ),
where P(V) is the speed distribution of the cells (center of mass translation), and j0(x) is a spherical Bessel function of the first kind of order zero. Because the shape of the autocorrelation function is almost independent of P(V) (which, therefore, cannot be extracted from data collected from tissue), we thus analyze I1(t) for a Gaussian speed distribution, namely,
P(V)=(2π)1/2(3V2)3/2V2exp(3V2/2V2),
for which one finds
j0(QVτ)=exp(Q2V2τ2/6),
where 〈V2〉 is the second moment of the distribution.

The structure factor S(Q) for erythrocytes has only been established experimentally for a limited number of scattering angles.[21] These data and a Rayleigh-Gans scattering approximation for randomly oriented red blood cells are well represented by a Rayleigh-Gans approximation for a sphere having 2.75-μm radius (see Fig. 2). We expect that, for the purposes of the present investigation (using red blood cells and polystyrene latex spheres), a suitable analytic expression is that derived for optically homogeneous spheres in the Rayleigh-Gans approximation, i.e.,[16]

S(Q)=S(Qa)={3(Qa)3[sin(Qa)Qacos(Qa)]}2,
where a is the apparent radius of the particle. As shown in Fig. 2, a good approximation to the expression given in Eq. (13) is
S(Q)exp[2ξ(Qa)2],
where ξ = 0.1. This expression represents Eq. (13) particularly well at the small and intermediate scattering angles (especially for red blood cells, see Fig. 2) which contribute most to the integrals in Eq. (2); also, it is a good approximation to a structure factor computed from the theory of anomalous diffraction[16] as well as to the limited empirical data for red blood cells (Fig. 2).

Substituting Eq. (11) into Eq. (2), one finds

I1(τ)=0πS[Q(θ)a]j0(QVτ)sinθdθ0πS[Q(θ)a]sinθdθ,
which, after the coordinate transformation z = [(1 − cosθ)/2]1/2, can be rewritten as
I1(τ)=01S(2kaz)j0(2kzVτ)zdz01S(2kz)zdz,
where k is defined in Eq. (3). Thus, using the expressions for 〈j0(QVt)〉 and S(Qa) given, respectively, by Eqs. (12) and (13′), one finds
I1(τ)=0Lexp(2ξz2)exp(T2z2)zdz0Lexp(2ξz2)zdz,
where the reduced variables L and T are defined as
L=2ka,T=V21/2τ/6a.
The integrals are easily performed, yielding
I1(τ)=2ξ2ξ+T2[1exp(2ξL2)exp(L2T2)][1exp(2ξL2)].

Numerical curves determined from Eq. (18) are shown in Fig. 3. We note from the equation that when T gets large, I1(τ) tends to zero as I1(τ) ∼ 1/τ2, whatever the value of L. In Fig. 4 we show the halfwidth of the decay T1/2 as a function of L. From Eq. (18) we also see that I1(τ) becomes a function only of T (independent of L) when 2ξL2 = 8ξk2a2 is large. One consequence is that the half-decay point of I1(τ) is then proportional to a/〈V21/2; from Eq. (17) and the asymptotic value T1/2 = 0.52 shown in Fig. 4, we find in this case that the half-decay point is given as

τ1/2=1.27aV21/2(kalarge).
Referring to Eq. (18) we see that, if 2ξL2 ≳ 4, the terms proportional to exp(−2ξL2) are <4% of the value of I1(τ) [i.e., exp(−2ξL2) < 0.04]. With the indicated value of ξ ∼ 0.1, this requirement is satisfied for a He–Ne laser if a > 0.15 μm. Thus, when considering red blood cells (a ∼ 2.8 μm), the correction to Eq. (19) arising from finite particle size can be ignored. In the asymptotic limit (ξL2 ≫ 1), the expression given in Eq. (18) becomes simply,
I1(τ)=2ξ2ξ+T2,
where T(τ) is defined in Eq. (17).

The value for τ1/2 given in Eq. (19) would pertain to the measured autocorrelation function for a heterodyne experiment where most of the photons which arrive at the photodetector are not scattered by moving cells. However, as shown below (see Fig. 7), scattering from the microvasculature is found to be nearly a homodyne process (i.e., the detected photons are predominantly Doppler shifted) and can be described by Eq. (7) with iSCi0 and β = 0.5 for a conventional spectrometer (see Sec. V) and β = 0.2 for a fiber optics instrument.[8],[11] The autocorrelation function in this case contains contributions representing the autocorrelation of photons which are multiply scattered from moving erythrocytes. In the next section we show how the intermediate scattering function I(τ) is related to I1(τ) when such multiple scattering is taken into account.

IV. Effects of Multiple Scattering

The model described in Secs. II and III is a single-shift model, in that we presume that a photon interacts with a moving cell only once while migrating within the tissue; however, under normal circumstances there is a strong likelihood that a photon will be scattered by more than one moving red cell before it leaves the tissue. If an appreciable number of such events occurs, the measured photon autocorrelation function will include components relating to higher-order scattering processes, i.e., those which involve multiply convoluted spectral shifts. Thus, the normalized intermediate scattering function I(τ) in general must be expressed as

I(τ)=m=1PmIm(τ)/(1P0),
where I1 is the autocorrelation function for photons which experience one collision with a moving cell, I2 is the autocorrelation function for photons which experience two collisions, etc., and Pm is the probability that a photon will experience m collisions with moving red cells before leaving the tissue.

Sorenson et al.[22] show that I2(t), which is the autocorrelation of field components I2(τ)=E2*(t)E2(t+τ) [where E2(t) represents an electric field of unit amplitude which has been scattered two times], can be written as the product I2(τ)=I12(τ) if there is no correlation between the positions of any two particles. This is a consequence of taking an ensemble average of unrelated phase shifts; similarly, one can show that terms of the form Ei*(t)Ej(t) are zero when i is not j, because the phase of Ej(t) is completely independent of the phase of Ei(t) (see footnote 8 of [Ref. 22]). In accordance with the arguments given in Sec. III, the time dependence of the fluctuating field associated with an n step photon diffusion process, of which m steps are collisions with moving cells (Fig. 1: A,A,…, A,B,A,…, A,B,A,…, A), can be written as ESC(t) = ESC1(t)ESC2(t) … ESCm(t). Because these m phase shifts are independent, the resultant term of the intermediate scattering function is

Im(τ)=Esc1*(t)Esc1(t+τ)Esc2*(t)Esc2(t+τ)Escm*(t)Escm(t+τ)=|I1(τ)|m,
and Eq. (21) thus can be written as
I(τ)=m=1Pm[I1(τ)]m/(1P0).

We next note that Pm can be written as Pm = ΣrP(m|r) · P(r), where P(m|r) is the probability that a photon makes m collisions with the moving erythrocytes before emerging from the tissue, given that it makes r collisions in total [i.e., (rm) collisions with static elements]. But, P(m|r) is given by the binomial distribution, i.e., the probability of realizing m successes (collisions with moving cells) out of r tries (total collisions), where the probability of success is proportional to the scattering cross section of the blood relative to the total scattering cross section of the tissue, p = σblood/(σblood + σtissue). If the blood volume is but a small fraction of total tissue volume, p simply will be proportional to blood volume (assuming that blood volume changes will not affect the gross scattering structure of the tissue and the mean path length of photon diffusion). In the limit that r is large and p is much less than 1, the binomial distribution can be represented by the Poisson distribution P(m|r)exp(m¯)(m¯)j/(j!), independent of r, where m is the average number of collisions with a moving cell. (By the preceding argument m is proportional to red blood cell number density and, consequently, to blood volume.) Thus, if an incident photon experiences many collisions with static elements before leaving the tissue, Eq. (22) can be rewritten as

I(τ)=m=1exp(m¯)[m¯I1(τ)]mm!/[1exp(m¯)]={exp[m¯(I1(τ)1)]exp(m¯)}/[1exp(m¯)]

The normalized autocorrelation function g(2)(τ) can now be obtained from Eqs. (7) and (23), namely,

g(2)(τ)=1+β(exp{2m¯[I1(τ)1]}exp(2m¯)).
Note that when m¯ is small, g(2)(τ) − 1 does not tend asymptotically to β for small τ; rather, it goes to a value β[1exp(2m¯)]. This amplitude diminution of the time varying portion of the normalized autocorrelation function occurs because a portion of the incident field experiences no scattering from red blood cells.

In Fig. 5 we show g(2)(τ) − 1 evaluated with I1 as given by Eq. (20). When m¯ is <1, the amplitude associated with the time varying portion of the autocorrelation function decreases approximately linearly with decreasing m¯ (nearly heterodyne), with only minor change in shape or decay rate. For large m¯, the amplitude of the decaying portion approaches the maximal value β and the shape of the curves becomes Gaussian with a decay rate proportional to (m¯)1/2. This Gaussian form is obtained for a sufficiently high degree of multiple scattering, regardless of the velocity distribution P(V). Consequently, laser Doppler velocimetry cannot provide information concerning details of the velocity distribution but is sensitive only to the mean speed through the scale factor m1/2V2〉/a.

Several simplifying assumptions have been used to derive Eqs. (2), (14), (20), and (23), which were necessitated by our uncertainty about the optical structure of the vascularized tissues which would be examined by laser Doppler scattering techniques. In Sec. VI we present experimental results which nonetheless indicate that many important features of the scattering process indeed are faithfully represented by these calculations.

V. Spectral Analysis

A spectrum analyzer also can be used to resolve the fluctuating intensity of the light which is scattered to the detector. The resulting signal is given as[15]

P(ω)=i02δ(ω)+ei0π+i02S(ω),
where e is a constant, i0 is the mean detected intensity, and the spectrum S(ω) is related to the photon autocorrelation function through the intermediate scattering function I(t) as[15]
S(ω)=1π0cosωt[g(2)(τ)1]dt.
When heterodyne conditions are realized so that i0iSC in Eq. (7), the intermediate scattering function I(t) is given approximately as I(t) ≈ I1(t) [see Eq. (23) as m¯0]. In this case, when I1 is given as in Eq. (20), the spectrum associated with the time varying part is
S1(ω)=βπ2isci00cosωtI1(t)dtexp[W(ω)],
where the function W(ω) (the reduced angular frequency) is defined as
W(ω)=(12ξ)1/2ωaV21/2.
From Eqs. (23) and (24) one can see that S(ω) in general is related to Eq. (20) as a function only of this reduced variable W. Thus, parameters such as ω1/2 [the value of ω where S(ω) is half-maximal] can be scaled in terms of particle speeds and dimensions [Eqs. (28) and (19)].

The spectrum S(ω) which pertains to the model for multiple scattering as described in the last section can be computed from the following expansion, obtained from Eqs. (26) and (24):

S(ω)=βexp(2m¯)j=1(2m¯)jSj(ω)j!,
where Sj(ω) is defined in terms of I1 as
Sj(ω)=1π0cosWt[I1(t)]jdt,
with W given in Eq. (28). When I1 is given as in Eq. (20), the first few terms of Sj(ω) are[23] (see the Appendix)
S1(ω)=exp(W);S2(ω)=¼(1+W)exp(W);S3(ω)=1/16(W2+3W+3)exp(W).
Typical curves for S(W) computed for different values of m¯ are shown in Fig. 6. We note that, in accordance with the behavior seen in Fig. 5, the curves change from exponential to Gaussian and become broader as m¯ increases.

There is a general analytic relationship between m¯, blood cell size and speeds, and the width of the spectrum. The latter is characterized by various moments of S(ω). For example, let us define the first moment 〈ω〉 as[11]

ω=|ω|S(ω)dω.
From Eqs. (24) and (26) we thus find
ω=2βπ0ω(0cosωt{exp2m¯[I1(t)1]exp(2m¯)}dt)dω.
But as seen from Eq. (20), I1(t) can be written as a function of Z(2ξ)1/2T=V21/2t/12ξa. Therefore, by successive changes of variables in the integrals, the expression for 〈ω〉 can be rewritten as
ω=V21/2(12ξ)1/2aβf(m¯),
where f(m¯), which depends only on m¯, is defined as
f(m¯)=2π0W(0cosWZexp{2m¯[I1(Z)1]}dZ)dW,
with I1(Z) = (1 + Z2)−1.

The function in Eq. (35) can be evaluated from the expressions given in Eqs. (29), (30), and (20). As shown in the Appendix, we find that 〈ω〉 is given as

ω=V21/2(12ξ)1/2aβ[2π1/2exp(2m¯)j=1(2m¯)jΓ(j+½)Γ(j+1)Γ(j)].
The expression in the brackets, which indicates the dependence on m¯, has been evaluated and is shown in Fig. 10. For m¯<1, i.e., average number of collisions between photons and moving erythrocytes less than one, we find that 〈ω〉 increases in an essentially linear fashion with changes in blood volume. In this case the first moment of the spectrum varies directly with relative blood flow, that is, as the product of blood cell concentration and mean speed. For higher blood volumes, 〈ω〉 still is sensitive to changes in flow but varies approximately as the square root of m¯, although still linearly with 〈V21/2. (However, sufficiently small changes in blood volume give rise to relative changes in 〈ω〉 which are linear.) Results of measurements taken on one of our model tissue analogs, in which 〈ω〉 was determined as a function of increasing particle concentration, are also shown in Fig. 10. We find it gratifying that the curves have similar characteristics (see Sec. VI).

VI. Experimental Models

In Fig. 7 we show some typical measured photon autocorrelation functions taken with a spectrometer constructed of a Spectra-Physics model 125A He–Ne laser, a fiber optics delivery system,[11] an ITT F-4085 photon counting tube, and a clipped autocorrelator.[24] Figure 7(a) shows the autocorrelation function of photons backscattered from the surface of a warm (∼37°) finger. Figure 7(b) is the photon autocorrelation function obtained from studies on a model system (to be described below) which we used to test predictions of the theory presented in the preceding section.

Several similar tissue models were used. Each essentially consisted of a bundle of hollow fibers surrounded by a strongly scattering medium. The data presented here were obtained from a bundle of seventeen 180-μm diam silicone fibers (BioRad Laboratories, Biofiber 5) set in a 5.0%-w/v agarose gel impregnated with 2.5-mg/ml, 0.055-μm radius latex polystyrene spheres. The latter material acted as an effective diffuser of the light incident from the laser, resulting in a glow which was similar in distributed intensity to that from living tissue. The fittings to the fiber bundle were connected to a precision infusion pump (Harvard Apparatus Co.). Various homogeneous particle suspensions, composed of either polystyrene spheres diluted in water or heparinized whole human blood diluted with 10% dextran in saline, were passed through the bundle at predetermined flow rates. When filled with diluted blood (0.5–5% RBC v/v) the 180-μm diam tubes have a scattering cross section equal to that of tissue microvessels (5–30 μm). The mean speeds of the flowing particles are reproducible. (The speed distributions probably are uniform, as for Newtonian flow in a cylinder.) The results from the model system, therefore, should deviate only slightly from those predicted using the distribution described by Eq. (12) (compare curves A and B in Figs. 5(c) and (d)].

These models were created to provide a system similar to living tissue, in which photons diffusing among static elements occasionally would encounter moving particles having known scattering properties. By varying particle size, concentration, and velocity, the appropriateness of our theory could then be determined. Specifically, we sought to verify the premise that discerned Doppler shifts arise from forward scattering by red blood cells, even when backscattered light from the tissue is analyzed. We also wished to test our conclusions concerning multiple Doppler scattering by moving particles.

As predicted by our theory, power spectra or autocorrelations of light scattered from the model system, as well as from human and animal tissues, were found to be independent of external scattering angle. This is in marked contrast to the dependence on the (difficult to determine) angle between q and v vectors observed when Doppler ultrasound measurements are made on large arteries[25] where tissue and red blood cell scattering cross sections are ∼10−5 those of light.

Using the model system and the Doppler apparatus described in [Ref. (11)], we could demonstrate that Doppler shifts do in fact vary linearly with the mean velocity of the flowing cells (Fig. 8). A more critical test concerned the relationship between spectral shifts and the scattering cross section (structure factor) of the particles. The number concentration of different sized particles was adjusted to the same total scattering cross section (to eliminate effects of a variable amount of multiple Doppler scattering; see next paragraph), and the resulting suspensions were then passed through the model at identical flow speeds. In accordance with Eq. (36), the observed mean spectral shift varied as the inverse of particle size (Fig. 9).

Our theoretical analysis of multiple scattering was tested by varying the concentration of red blood cells flowing in the model system. At each concentration, the mean spectral shift was determined for several values of mean velocity (0.2–2.0 mm/sec). The degree of multiple scattering m¯ was also evaluated. The latter was accomplished by determining the amplitude of the normalized autocorrelation decay [g(2)(0) − 1]. As indicated in Eq. (24), this quantity is approximately (2βm¯) for small m¯ and approaches β (here, β = 0.17) for m¯>2. As shown in Fig. 10, as the concentration of suspended red blood cells flowing in the model system was increased (while keeping the flow velocity constant), the mean frequency 〈ω〉 of the Doppler shift increased almost linearly at low concentrations (m¯<1); at higher particle concentrations 〈ω〉 tended toward a square root dependence on concentration, as predicted by Eq. (36).

The lower curve in Fig. 10 represents values computed from Eq. (36) using best estimates for 〈V21/2 and a. The values of the fitted upper curve are everywhere 140% of those of the lower curve. The 40% discrepancy in the coefficient V21/2/(12ξa) could arise from errors in determining the rms velocity of the red blood cells from the bulk flow through the model system and in estimating m¯, or from the theoretical approximations used for the speed distribution [Eq. (12)] and structure factor [Eq. (13′)]. Additionally, the model system contains oriented 180-μm diam tubes (much larger than tissue microvessels) in which multiple Doppler scattering of the type indicated in Fig. 1(C) may occur.

Our theory thus provides a good representation of the behavior of flow models which have been constructed to mimic laser Doppler scattering from the microvasculature of living tissues. Those model systems allow systematic variation of particle radius, concentration, and velocity, as well as external scattering geometry, enabling us to test the quantitative predictions of our theory in a manner not possible in living tissue. The ability of the theory to rationalize accurately the data acquired with the model systems suggests: (1) laser Doppler scattering can be used to quantitate instantaneous microvascular blood flow in small volumes (∼1 mm3) of optically accessible tissue; (2) 〈ω〉 varies linearly with blood flow in tissues containing small relative blood volumes; and (3) 〈ω〉 varies linearly with small changes in blood volume associated with the cardiac cycle.

The theory predicts, with reasonable accuracy, measurements of blood volume flow in a model system where flow speed 〈V21/2 and collision number m¯ can be determined. However, amplitudes of actual physiological flow extend over 2 orders of magnitude, with large variations in both 〈V21/2 and m¯. Estimates of m¯ are difficult to obtain for tissues for which the mean number of scattering events from moving cells is >2 (i.e., tissue whose vascular volume is >0.06-ml blood per gram of tissue, given the existing probe design). Therefore, in order to determine an absolute value of blood flow from any given tissue, the laser Doppler flowmeter generally would first have to be calibrated by another quantitative technique. However, this instrument is uniquely able to monitor microvascular flow continuously and noninvasively in many animal and human tissues, with a temporal resolution of ∼100 msec and spatial resolution of 1 mm.

Appendix: Derivation of Eq. (36)

Note that Eqs. (29), (30), and (20) can be used to write Eq. (35) as

f(m¯)=2exp(2m¯)j=1(2m¯)jfjj!
where fj is
fj1π0W[0cosWZ(11+Z2)jdZ]dW.
The integral in the brackets can be expressed as[23]
0cosWZ(11+Z2)jdZ=(W2)j1/2π1/2Γ(j)Kj1/2(W),
where Kj is a modified Bessel function, and Γ(j) is a gamma function. The integral over W then is[26]
0W(W2)j1/2Kj1/2(W)dW=Γ(j+½).
Thus, the term fj is given as
fj=Γ(j+½)π1/2Γ(j),
and, by Eq. (A1), the expression for f(m¯) becomes
f(m¯)=2exp(2m¯)π1/2j=1(2m¯)jΓ(j+½)Γ(j+1)Γ(j).
Using this expression, one obtains from Eq. (34) the relationship given in Eq. (36).

The authors thank John Bechhoefer, Pat Bowen, and Doug McGuire for their cheerful and competent assistance with several technical details relating to the accomplishment of this work.

Figures

 

Fig. 1 Diffusion of photons within the tissue can be represented as a series of scattering steps of types A, B, or C. Step A represents scattering from a static tissue element which does not impart any Doppler shift (the phase shift of this step, φA, is constant). The distance between static scattering centers |ρrj| will depend on the tissue structure but typically is ∼100 μm. Step B represents small-angle Doppler scattering from a moving red blood cell with a phase shift φβ(t), which varies as Q · vt. The probability of step B scattering increases with local blood cell concentration. Step C represents sequential scattering from two moving red blood cells and occurs within larger vessels (>50-μm diam). In this case the velocity vectors of the two cells are highly correlated, |v1·v2|υ12, although the sign of the phase terms Q1 · r1(t) and Q2 · r2(t) are random.

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Fig. 2 Angular scattering structure factor S(Qa) vs Qa. Shown are (a) the approximations given by Eq. (13), which is used in our subsequent analysis, and (b) a curve based on empirical data and a Rayleigh-Gans computation of light scattered by randomly oriented biconcave disk red blood cells.[21] Also shown are curves for (c) spherical red blood cells of 2.75-μm radius and polystyrene latex spheres of (d) 0.55- and (e) 0.26-μm radius, derived from Mie theory for particles in water.

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Fig. 3 Field autocorrelation function given by Eq. (18), I1(T), for light which is singly scattered by moving particles, plotted as a function of the reduced time variable T for different values of the particle size variable L (L = 1,2,4,15). For large particles with L > 4 (a > 0.15 μm), I1(T) has the asymptotic form given by Eq. (20).

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Fig. 4 Half time, T1/2, of I1(T) determined from Eq. (18) and plotted as a function of the size parameter L = 2ka. For large particles with highly anisotropic forward scattering (L > 4), the reduced time variable is given by Eq. (19).

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Fig. 5 Normalized intensity autocorrelation function g(2)(T) plotted as a function of the reduced time variable T [i.e., scaled by the rms speed divided by the particle radius, see Eq. (17)] for different degrees of multiple scattering and different speed distributions. Parts (a) and (b) show g(2)(T) for the Gaussian speed distribution [Eq. (12)] when m¯ varies from 0.1 to 4. (a) For small values of m¯, the predominant effect on g(2)(T) is an increase in amplitude with increasing m¯. (b) For larger m¯ the amplitude of g(2)(0) approaches a maximum, 1 + β, determined by the spatial coherence at the detector, while the decay rate increases and approaches m¯1/2 scaling. In (c) and (d) the curves labeled A, B, C, and D represent g(2)(T) for different speed distributions: A, Gaussian [Eq. (12)]; B, uniform (Newtonian flow in a cylinder); C, single speed; D, two speed ( 1/3υ¯ and 5/3υ¯). Differences in these curves are noticeable at large T if m¯1 but are insignificant for large m¯ (where all curves become Gaussian). The relative amplitude of the slow decay (corresponding to low frequency components) is seen to increase with the variance of the speed distribution.

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Fig. 6 Power spectrum S(W) of the detected photocurrent computed as a function of Wm¯1/2 for m¯=0.5,1,1.5,2,3,4,6,8,and10 (the curves from left to right, respectively). For m¯<1, the normalized first moment of these spectra increases more rapidly than m¯1/2, whereas for large m¯ the spectra scale with Wm¯1/2.

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Fig. 7 (A) Normalized photon autocorrelation function g(2)(τ) − 1 of light backscattered from the fingertip of a normal volunteer. The decay amplitude is 90% of the empirical value of β obtained from homodyne scattering from a solution of polystyrene spheres using the same fiber optic probe and corresponds [see Eq. (24)] to m¯1.2. (B) Autocorrelation functions obtained from light backscattered from the tissue model using diluted human blood with mean velocity of 1 mm/sec and hematocrits H = 0.05, 0.025, 0.006, and 0.003 (a, b, c, and d, respectively). Increased red blood cell number density (and, therefore, m¯) results in increased decay rates and amplitudes of g(2)(τ) − 1. These experimental curves are similar to the theoretical curves shown in Fig. 5. For clarity, smoothed curves have been drawn through the data points, which are only shown for H = 0.025.

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Fig. 8 Mean Doppler shift 〈ω〉, as defined in Eq. (32), plotted vs mean velocity for a variety of particle sizes and volume fractions of fluid moving through the hollow fibers of the model system. In the 0–2-mm/sec range, the mean frequency 〈ω〉 increases linearly with mean particle velocity. A monotonically increasing dependence on particle density and decreasing dependence on particle radius are also observed, ag, human blood diluted to hematocrits of 0.003, 0.006, 0.012, 0.012, 0.025, 0.035, 0.050, and 0.12, respectively; hi, 0.55-μm radius PSL spheres at concentrations of 0.5 and 1.0 % v/v; j, 0.264-μm radius PSL spheres, 1 % v/v.

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Fig. 9 Mean Doppler frequency normalized by f(m¯) and rms particle velocity ν¯/f(m¯)=ω/f(m¯)V21/2 vs the reciprocal of particle radius. Shown are data obtained when red blood cells or PSL spheres were passed through the tissue flow model. Mean frequencies have been normalized to m¯ and mean particle speed. The solid line drawn through the data points represents values of ν¯/f(m¯) predicted by Eq. (36). [For red blood cells, m¯ equals 1.43 times the vol % RBC in our model system (see insert in Fig. 10). Corresponding values of m¯ for PSL solutions were determined from literature values[14],[16] of particle scattering cross sections and from particle volume ratios. From the values of m¯ so obtained, f(m¯) was calculated by Eq. (A4).]

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Fig. 10 Mean Doppler shift normalized by the rms particle velocity plotted as a function of the hematocrit of the fluid flowing through the tissue model (H = 0.3–12% v/v RBC). The average degree of multiple scattering m¯ was related to RBC concentration by determining the decay amplitudes (see insert) and fitting according to Eq. (24). Empirical values of ν¯=ω/V21/2 are plotted (circles) for rms velocities in the 0.2–2-mm/sec range. The lower solid line is the theoretical curve given by Eq. (36) using an effective RBC radius of 2.8 μm and measured values of 〈V21/2 and m¯. The upper curve is obtained from the lower merely by increasing the ordinate scale [〈V21/2/(12ξ)1/2a] by 40%. The shape of the curve depends only on f(m¯) [see Eq. (A4)].

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References

1. S. Morikawa, O. Lanz, and C. C. Johnson, IEEE Trans. Biomed. Eng. BME-18, 416 (1971) [CrossRef]  .

2. C. Riva, B. Ross, and G. B. Benedek, Invest. Ophthalmol. 11, 936 (1972) [PubMed]  .

3. T. Tanaka, C. Riva, and I. Ben-Sira, Science 186, 830 (1974) [CrossRef]   [PubMed]  .

4. T. Tanaka and G. B. Benedek, Appl. Opt. 14, 189 (1975) [PubMed]  .

5. M. Stern, Nature (London) 254, 56 (1975) [CrossRef]  .

6. M. D. Stern, D. L. Lappe, P. D. Bowen, J. E. Chimosky, G. A. Holloway Jr., H. R. Keiser, and R. L. Bowman, Am. J. Physiol. 232, H441 (1977) [PubMed]  .

7. M. D. Stern, P. D. Bowen, R. Parma, R. W. Osgood, R. L. Bowman, and J. H. Stein, Am. J. Physiol. 236, F80 (1979) [PubMed]  .

8. R. F. Bonner, P. Bowen, R. L. Bowman, and R. Nossal, in Proceedings, Electrooptics/Laser &'78 Conference (Industrial and Scientific Conference Management, Inc., Chicago, III., 1978), p. 539.

9. D. Watkins and G. A. Holloway Jr., IEEE Trans. Biomed. Eng. BME-25, 28 (1978) [CrossRef]  .

10. R. W. Wunderlich, R. L. Folger, B. R. Ware, and D. B. Giddon, Rev. Sci Instrum. 51, 1258 (1980) [CrossRef]  .

11. R. Bonner, T. R. Clem, P. D. Bowen, and R. L. Bowman, in Scattering Techniques Applied to Supramolecular and Non-equilibrium Systems, S-H. Chen, B. Chu, and R. Nossal, Eds. (Plenum, New York, in press).

12. D. I. Abramson, Circulation in the Extremities (Academic, New York, 1967).

13. P. C. Johnson, Ed., Peripheral Circulation (Wiley, New York, 1978).

14. L. Reynolds, C. Johnson, and A. Ishimaru, Appl. Opt. 15, 2059 (1976) [CrossRef]   [PubMed]  .

15. H. Z. Cummins and H. L. Swinney, Prog. Opt. 8, 133 (1970) [CrossRef]  .

16. H. C. van de Hulst, Light Scattering by Small Particles (Wiley, New York, 1957).

17. E. Jakeman, in Photon Correlation and Light Beating Spectroscopy, H. Z. Cummins and E. R. Pike, Eds., (Plenum, New York, 1974), p. 91.

18. H. L. Goldsmith and S. G. Mason, Bibl. Anat. 10, 1 (1969).

19. P. Butti, M. Intaglietta, H. Riemann, C. Holliger, A. Bollinger, and M. Anliker, Microvasc. Res. 10, 220 (1975) [CrossRef]   [PubMed]  .

20. R. Nossal, S. H. Chen, and C. C. Lai, Opt. Commun. 4, 35 (1971) [CrossRef]  .

21. R. S. Chadwick and I. Chang, J. Colloid Interface Sci. 42, 516 (1973) [CrossRef]  .

22. C. M. Sorensen, R. C. Mockler, and W. J. O'Sullivan, Phys. Rev. A: 14, 1520 (1976) [CrossRef]  .

23. A. Erdelyi, Ed., Tables of Integral Transforms, (McGraw-Hill, New York, 1954).

24. S. H. Chen, W. B. Veldkamp, and C. C. Lai, Rev. Sci. Instrum. 46, 1356 (1975) [CrossRef]  .

25. J. Lubbers, P. J. L. M. Bernink, G. J. Barendsen, and J. W. van den Berg, Pfluegers Arch. 382, 241 (1979) [CrossRef]  .

26. M. Abramowitz and I. A. Stegun, Eds., Handbook of Mathematical Functions (Dover, New York, 1965).

References

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  • |

  1. S. Morikawa, O. Lanz, C. C. Johnson, IEEE Trans. Biomed. Eng. BME-18, 416 (1971).
    [Crossref]
  2. C. Riva, B. Ross, G. B. Benedek, Invest. Ophthalmol. 11, 936 (1972).
    [PubMed]
  3. T. Tanaka, C. Riva, I. Ben-Sira, Science 186, 830 (1974).
    [Crossref] [PubMed]
  4. T. Tanaka, G. B. Benedek, Appl. Opt. 14, 189 (1975).
    [PubMed]
  5. M. Stern, Nature (London) 254, 56 (1975).
    [Crossref]
  6. M. D. Stern, D. L. Lappe, P. D. Bowen, J. E. Chimosky, G. A. Holloway, H. R. Keiser, R. L. Bowman, Am. J. Physiol. 232, H441 (1977).
    [PubMed]
  7. M. D. Stern, P. D. Bowen, R. Parma, R. W. Osgood, R. L. Bowman, J. H. Stein, Am. J. Physiol. 236, F80 (1979).
    [PubMed]
  8. R. F. Bonner, P. Bowen, R. L. Bowman, R. Nossal, in Proceedings, Electrooptics/Laser &'78 Conference (Industrial and Scientific Conference Management, Inc., Chicago, III., 1978), p. 539.
  9. D. Watkins, G. A. Holloway, IEEE Trans. Biomed. Eng. BME-25, 28 (1978).
    [Crossref]
  10. R. W. Wunderlich, R. L. Folger, B. R. Ware, D. B. Giddon, Rev. Sci Instrum. 51, 1258 (1980).
    [Crossref]
  11. R. Bonner, T. R. Clem, P. D. Bowen, R. L. Bowman, in Scattering Techniques Applied to Supramolecular and Non-equilibrium Systems, S-H. Chen, B. Chu, R. Nossal, Eds. (Plenum, New York, in press).
  12. D. I. Abramson, Circulation in the Extremities (Academic, New York, 1967).
  13. P. C. Johnson, Ed., Peripheral Circulation (Wiley, New York, 1978).
  14. L. Reynolds, C. Johnson, A. Ishimaru, Appl. Opt. 15, 2059 (1976).
    [Crossref] [PubMed]
  15. H. Z. Cummins, H. L. Swinney, Prog. Opt. 8, 133 (1970).
    [Crossref]
  16. H. C. van de Hulst, Light Scattering by Small Particles (Wiley, New York, 1957).
  17. E. Jakeman, in Photon Correlation and Light Beating Spectroscopy, H. Z. Cummins, E. R. Pike, Eds., (Plenum, New York, 1974), p. 91.
  18. H. L. Goldsmith, S. G. Mason, Bibl. Anat. 10, 1 (1969).
  19. P. Butti, M. Intaglietta, H. Riemann, C. Holliger, A. Bollinger, M. Anliker, Microvasc. Res. 10, 220 (1975).
    [Crossref] [PubMed]
  20. R. Nossal, S. H. Chen, C. C. Lai, Opt. Commun. 4, 35 (1971).
    [Crossref]
  21. R. S. Chadwick, I. Chang, J. Colloid Interface Sci. 42, 516 (1973).
    [Crossref]
  22. C. M. Sorensen, R. C. Mockler, W. J. O'Sullivan, Phys. Rev. A: 14, 1520 (1976).
    [Crossref]
  23. A. Erdelyi, Ed., Tables of Integral Transforms, (McGraw-Hill, New York, 1954).
  24. S. H. Chen, W. B. Veldkamp, C. C. Lai, Rev. Sci. Instrum. 46, 1356 (1975).
    [Crossref]
  25. J. Lubbers, P. J. L. M. Bernink, G. J. Barendsen, J. W. van den Berg, Pfluegers Arch. 382, 241 (1979).
    [Crossref]
  26. M. Abramowitz, I. A. Stegun, Eds., Handbook of Mathematical Functions (Dover, New York, 1965).

1980 (1)

R. W. Wunderlich, R. L. Folger, B. R. Ware, D. B. Giddon, Rev. Sci Instrum. 51, 1258 (1980).
[Crossref]

1979 (2)

M. D. Stern, P. D. Bowen, R. Parma, R. W. Osgood, R. L. Bowman, J. H. Stein, Am. J. Physiol. 236, F80 (1979).
[PubMed]

J. Lubbers, P. J. L. M. Bernink, G. J. Barendsen, J. W. van den Berg, Pfluegers Arch. 382, 241 (1979).
[Crossref]

1978 (1)

D. Watkins, G. A. Holloway, IEEE Trans. Biomed. Eng. BME-25, 28 (1978).
[Crossref]

1977 (1)

M. D. Stern, D. L. Lappe, P. D. Bowen, J. E. Chimosky, G. A. Holloway, H. R. Keiser, R. L. Bowman, Am. J. Physiol. 232, H441 (1977).
[PubMed]

1976 (2)

L. Reynolds, C. Johnson, A. Ishimaru, Appl. Opt. 15, 2059 (1976).
[Crossref] [PubMed]

C. M. Sorensen, R. C. Mockler, W. J. O'Sullivan, Phys. Rev. A: 14, 1520 (1976).
[Crossref]

1975 (4)

S. H. Chen, W. B. Veldkamp, C. C. Lai, Rev. Sci. Instrum. 46, 1356 (1975).
[Crossref]

P. Butti, M. Intaglietta, H. Riemann, C. Holliger, A. Bollinger, M. Anliker, Microvasc. Res. 10, 220 (1975).
[Crossref] [PubMed]

T. Tanaka, G. B. Benedek, Appl. Opt. 14, 189 (1975).
[PubMed]

M. Stern, Nature (London) 254, 56 (1975).
[Crossref]

1974 (1)

T. Tanaka, C. Riva, I. Ben-Sira, Science 186, 830 (1974).
[Crossref] [PubMed]

1973 (1)

R. S. Chadwick, I. Chang, J. Colloid Interface Sci. 42, 516 (1973).
[Crossref]

1972 (1)

C. Riva, B. Ross, G. B. Benedek, Invest. Ophthalmol. 11, 936 (1972).
[PubMed]

1971 (2)

S. Morikawa, O. Lanz, C. C. Johnson, IEEE Trans. Biomed. Eng. BME-18, 416 (1971).
[Crossref]

R. Nossal, S. H. Chen, C. C. Lai, Opt. Commun. 4, 35 (1971).
[Crossref]

1970 (1)

H. Z. Cummins, H. L. Swinney, Prog. Opt. 8, 133 (1970).
[Crossref]

1969 (1)

H. L. Goldsmith, S. G. Mason, Bibl. Anat. 10, 1 (1969).

Abramson, D. I.

D. I. Abramson, Circulation in the Extremities (Academic, New York, 1967).

Anliker, M.

P. Butti, M. Intaglietta, H. Riemann, C. Holliger, A. Bollinger, M. Anliker, Microvasc. Res. 10, 220 (1975).
[Crossref] [PubMed]

Barendsen, G. J.

J. Lubbers, P. J. L. M. Bernink, G. J. Barendsen, J. W. van den Berg, Pfluegers Arch. 382, 241 (1979).
[Crossref]

Benedek, G. B.

T. Tanaka, G. B. Benedek, Appl. Opt. 14, 189 (1975).
[PubMed]

C. Riva, B. Ross, G. B. Benedek, Invest. Ophthalmol. 11, 936 (1972).
[PubMed]

Ben-Sira, I.

T. Tanaka, C. Riva, I. Ben-Sira, Science 186, 830 (1974).
[Crossref] [PubMed]

Bernink, P. J. L. M.

J. Lubbers, P. J. L. M. Bernink, G. J. Barendsen, J. W. van den Berg, Pfluegers Arch. 382, 241 (1979).
[Crossref]

Bollinger, A.

P. Butti, M. Intaglietta, H. Riemann, C. Holliger, A. Bollinger, M. Anliker, Microvasc. Res. 10, 220 (1975).
[Crossref] [PubMed]

Bonner, R.

R. Bonner, T. R. Clem, P. D. Bowen, R. L. Bowman, in Scattering Techniques Applied to Supramolecular and Non-equilibrium Systems, S-H. Chen, B. Chu, R. Nossal, Eds. (Plenum, New York, in press).

Bonner, R. F.

R. F. Bonner, P. Bowen, R. L. Bowman, R. Nossal, in Proceedings, Electrooptics/Laser &'78 Conference (Industrial and Scientific Conference Management, Inc., Chicago, III., 1978), p. 539.

Bowen, P.

R. F. Bonner, P. Bowen, R. L. Bowman, R. Nossal, in Proceedings, Electrooptics/Laser &'78 Conference (Industrial and Scientific Conference Management, Inc., Chicago, III., 1978), p. 539.

Bowen, P. D.

M. D. Stern, P. D. Bowen, R. Parma, R. W. Osgood, R. L. Bowman, J. H. Stein, Am. J. Physiol. 236, F80 (1979).
[PubMed]

M. D. Stern, D. L. Lappe, P. D. Bowen, J. E. Chimosky, G. A. Holloway, H. R. Keiser, R. L. Bowman, Am. J. Physiol. 232, H441 (1977).
[PubMed]

R. Bonner, T. R. Clem, P. D. Bowen, R. L. Bowman, in Scattering Techniques Applied to Supramolecular and Non-equilibrium Systems, S-H. Chen, B. Chu, R. Nossal, Eds. (Plenum, New York, in press).

Bowman, R. L.

M. D. Stern, P. D. Bowen, R. Parma, R. W. Osgood, R. L. Bowman, J. H. Stein, Am. J. Physiol. 236, F80 (1979).
[PubMed]

M. D. Stern, D. L. Lappe, P. D. Bowen, J. E. Chimosky, G. A. Holloway, H. R. Keiser, R. L. Bowman, Am. J. Physiol. 232, H441 (1977).
[PubMed]

R. F. Bonner, P. Bowen, R. L. Bowman, R. Nossal, in Proceedings, Electrooptics/Laser &'78 Conference (Industrial and Scientific Conference Management, Inc., Chicago, III., 1978), p. 539.

R. Bonner, T. R. Clem, P. D. Bowen, R. L. Bowman, in Scattering Techniques Applied to Supramolecular and Non-equilibrium Systems, S-H. Chen, B. Chu, R. Nossal, Eds. (Plenum, New York, in press).

Butti, P.

P. Butti, M. Intaglietta, H. Riemann, C. Holliger, A. Bollinger, M. Anliker, Microvasc. Res. 10, 220 (1975).
[Crossref] [PubMed]

Chadwick, R. S.

R. S. Chadwick, I. Chang, J. Colloid Interface Sci. 42, 516 (1973).
[Crossref]

Chang, I.

R. S. Chadwick, I. Chang, J. Colloid Interface Sci. 42, 516 (1973).
[Crossref]

Chen, S. H.

S. H. Chen, W. B. Veldkamp, C. C. Lai, Rev. Sci. Instrum. 46, 1356 (1975).
[Crossref]

R. Nossal, S. H. Chen, C. C. Lai, Opt. Commun. 4, 35 (1971).
[Crossref]

Chimosky, J. E.

M. D. Stern, D. L. Lappe, P. D. Bowen, J. E. Chimosky, G. A. Holloway, H. R. Keiser, R. L. Bowman, Am. J. Physiol. 232, H441 (1977).
[PubMed]

Clem, T. R.

R. Bonner, T. R. Clem, P. D. Bowen, R. L. Bowman, in Scattering Techniques Applied to Supramolecular and Non-equilibrium Systems, S-H. Chen, B. Chu, R. Nossal, Eds. (Plenum, New York, in press).

Cummins, H. Z.

H. Z. Cummins, H. L. Swinney, Prog. Opt. 8, 133 (1970).
[Crossref]

Folger, R. L.

R. W. Wunderlich, R. L. Folger, B. R. Ware, D. B. Giddon, Rev. Sci Instrum. 51, 1258 (1980).
[Crossref]

Giddon, D. B.

R. W. Wunderlich, R. L. Folger, B. R. Ware, D. B. Giddon, Rev. Sci Instrum. 51, 1258 (1980).
[Crossref]

Goldsmith, H. L.

H. L. Goldsmith, S. G. Mason, Bibl. Anat. 10, 1 (1969).

Holliger, C.

P. Butti, M. Intaglietta, H. Riemann, C. Holliger, A. Bollinger, M. Anliker, Microvasc. Res. 10, 220 (1975).
[Crossref] [PubMed]

Holloway, G. A.

D. Watkins, G. A. Holloway, IEEE Trans. Biomed. Eng. BME-25, 28 (1978).
[Crossref]

M. D. Stern, D. L. Lappe, P. D. Bowen, J. E. Chimosky, G. A. Holloway, H. R. Keiser, R. L. Bowman, Am. J. Physiol. 232, H441 (1977).
[PubMed]

Intaglietta, M.

P. Butti, M. Intaglietta, H. Riemann, C. Holliger, A. Bollinger, M. Anliker, Microvasc. Res. 10, 220 (1975).
[Crossref] [PubMed]

Ishimaru, A.

Jakeman, E.

E. Jakeman, in Photon Correlation and Light Beating Spectroscopy, H. Z. Cummins, E. R. Pike, Eds., (Plenum, New York, 1974), p. 91.

Johnson, C.

Johnson, C. C.

S. Morikawa, O. Lanz, C. C. Johnson, IEEE Trans. Biomed. Eng. BME-18, 416 (1971).
[Crossref]

Keiser, H. R.

M. D. Stern, D. L. Lappe, P. D. Bowen, J. E. Chimosky, G. A. Holloway, H. R. Keiser, R. L. Bowman, Am. J. Physiol. 232, H441 (1977).
[PubMed]

Lai, C. C.

S. H. Chen, W. B. Veldkamp, C. C. Lai, Rev. Sci. Instrum. 46, 1356 (1975).
[Crossref]

R. Nossal, S. H. Chen, C. C. Lai, Opt. Commun. 4, 35 (1971).
[Crossref]

Lanz, O.

S. Morikawa, O. Lanz, C. C. Johnson, IEEE Trans. Biomed. Eng. BME-18, 416 (1971).
[Crossref]

Lappe, D. L.

M. D. Stern, D. L. Lappe, P. D. Bowen, J. E. Chimosky, G. A. Holloway, H. R. Keiser, R. L. Bowman, Am. J. Physiol. 232, H441 (1977).
[PubMed]

Lubbers, J.

J. Lubbers, P. J. L. M. Bernink, G. J. Barendsen, J. W. van den Berg, Pfluegers Arch. 382, 241 (1979).
[Crossref]

Mason, S. G.

H. L. Goldsmith, S. G. Mason, Bibl. Anat. 10, 1 (1969).

Mockler, R. C.

C. M. Sorensen, R. C. Mockler, W. J. O'Sullivan, Phys. Rev. A: 14, 1520 (1976).
[Crossref]

Morikawa, S.

S. Morikawa, O. Lanz, C. C. Johnson, IEEE Trans. Biomed. Eng. BME-18, 416 (1971).
[Crossref]

Nossal, R.

R. Nossal, S. H. Chen, C. C. Lai, Opt. Commun. 4, 35 (1971).
[Crossref]

R. F. Bonner, P. Bowen, R. L. Bowman, R. Nossal, in Proceedings, Electrooptics/Laser &'78 Conference (Industrial and Scientific Conference Management, Inc., Chicago, III., 1978), p. 539.

Osgood, R. W.

M. D. Stern, P. D. Bowen, R. Parma, R. W. Osgood, R. L. Bowman, J. H. Stein, Am. J. Physiol. 236, F80 (1979).
[PubMed]

O'Sullivan, W. J.

C. M. Sorensen, R. C. Mockler, W. J. O'Sullivan, Phys. Rev. A: 14, 1520 (1976).
[Crossref]

Parma, R.

M. D. Stern, P. D. Bowen, R. Parma, R. W. Osgood, R. L. Bowman, J. H. Stein, Am. J. Physiol. 236, F80 (1979).
[PubMed]

Reynolds, L.

Riemann, H.

P. Butti, M. Intaglietta, H. Riemann, C. Holliger, A. Bollinger, M. Anliker, Microvasc. Res. 10, 220 (1975).
[Crossref] [PubMed]

Riva, C.

T. Tanaka, C. Riva, I. Ben-Sira, Science 186, 830 (1974).
[Crossref] [PubMed]

C. Riva, B. Ross, G. B. Benedek, Invest. Ophthalmol. 11, 936 (1972).
[PubMed]

Ross, B.

C. Riva, B. Ross, G. B. Benedek, Invest. Ophthalmol. 11, 936 (1972).
[PubMed]

Sorensen, C. M.

C. M. Sorensen, R. C. Mockler, W. J. O'Sullivan, Phys. Rev. A: 14, 1520 (1976).
[Crossref]

Stein, J. H.

M. D. Stern, P. D. Bowen, R. Parma, R. W. Osgood, R. L. Bowman, J. H. Stein, Am. J. Physiol. 236, F80 (1979).
[PubMed]

Stern, M.

M. Stern, Nature (London) 254, 56 (1975).
[Crossref]

Stern, M. D.

M. D. Stern, P. D. Bowen, R. Parma, R. W. Osgood, R. L. Bowman, J. H. Stein, Am. J. Physiol. 236, F80 (1979).
[PubMed]

M. D. Stern, D. L. Lappe, P. D. Bowen, J. E. Chimosky, G. A. Holloway, H. R. Keiser, R. L. Bowman, Am. J. Physiol. 232, H441 (1977).
[PubMed]

Swinney, H. L.

H. Z. Cummins, H. L. Swinney, Prog. Opt. 8, 133 (1970).
[Crossref]

Tanaka, T.

T. Tanaka, G. B. Benedek, Appl. Opt. 14, 189 (1975).
[PubMed]

T. Tanaka, C. Riva, I. Ben-Sira, Science 186, 830 (1974).
[Crossref] [PubMed]

van de Hulst, H. C.

H. C. van de Hulst, Light Scattering by Small Particles (Wiley, New York, 1957).

van den Berg, J. W.

J. Lubbers, P. J. L. M. Bernink, G. J. Barendsen, J. W. van den Berg, Pfluegers Arch. 382, 241 (1979).
[Crossref]

Veldkamp, W. B.

S. H. Chen, W. B. Veldkamp, C. C. Lai, Rev. Sci. Instrum. 46, 1356 (1975).
[Crossref]

Ware, B. R.

R. W. Wunderlich, R. L. Folger, B. R. Ware, D. B. Giddon, Rev. Sci Instrum. 51, 1258 (1980).
[Crossref]

Watkins, D.

D. Watkins, G. A. Holloway, IEEE Trans. Biomed. Eng. BME-25, 28 (1978).
[Crossref]

Wunderlich, R. W.

R. W. Wunderlich, R. L. Folger, B. R. Ware, D. B. Giddon, Rev. Sci Instrum. 51, 1258 (1980).
[Crossref]

Am. J. Physiol. (2)

M. D. Stern, D. L. Lappe, P. D. Bowen, J. E. Chimosky, G. A. Holloway, H. R. Keiser, R. L. Bowman, Am. J. Physiol. 232, H441 (1977).
[PubMed]

M. D. Stern, P. D. Bowen, R. Parma, R. W. Osgood, R. L. Bowman, J. H. Stein, Am. J. Physiol. 236, F80 (1979).
[PubMed]

Appl. Opt. (2)

Bibl. Anat. (1)

H. L. Goldsmith, S. G. Mason, Bibl. Anat. 10, 1 (1969).

IEEE Trans. Biomed. Eng. (2)

S. Morikawa, O. Lanz, C. C. Johnson, IEEE Trans. Biomed. Eng. BME-18, 416 (1971).
[Crossref]

D. Watkins, G. A. Holloway, IEEE Trans. Biomed. Eng. BME-25, 28 (1978).
[Crossref]

Invest. Ophthalmol. (1)

C. Riva, B. Ross, G. B. Benedek, Invest. Ophthalmol. 11, 936 (1972).
[PubMed]

J. Colloid Interface Sci. (1)

R. S. Chadwick, I. Chang, J. Colloid Interface Sci. 42, 516 (1973).
[Crossref]

Microvasc. Res. (1)

P. Butti, M. Intaglietta, H. Riemann, C. Holliger, A. Bollinger, M. Anliker, Microvasc. Res. 10, 220 (1975).
[Crossref] [PubMed]

Nature (London) (1)

M. Stern, Nature (London) 254, 56 (1975).
[Crossref]

Opt. Commun. (1)

R. Nossal, S. H. Chen, C. C. Lai, Opt. Commun. 4, 35 (1971).
[Crossref]

Pfluegers Arch. (1)

J. Lubbers, P. J. L. M. Bernink, G. J. Barendsen, J. W. van den Berg, Pfluegers Arch. 382, 241 (1979).
[Crossref]

Phys. Rev. A: (1)

C. M. Sorensen, R. C. Mockler, W. J. O'Sullivan, Phys. Rev. A: 14, 1520 (1976).
[Crossref]

Prog. Opt. (1)

H. Z. Cummins, H. L. Swinney, Prog. Opt. 8, 133 (1970).
[Crossref]

Rev. Sci Instrum. (1)

R. W. Wunderlich, R. L. Folger, B. R. Ware, D. B. Giddon, Rev. Sci Instrum. 51, 1258 (1980).
[Crossref]

Rev. Sci. Instrum. (1)

S. H. Chen, W. B. Veldkamp, C. C. Lai, Rev. Sci. Instrum. 46, 1356 (1975).
[Crossref]

Science (1)

T. Tanaka, C. Riva, I. Ben-Sira, Science 186, 830 (1974).
[Crossref] [PubMed]

Other (8)

R. Bonner, T. R. Clem, P. D. Bowen, R. L. Bowman, in Scattering Techniques Applied to Supramolecular and Non-equilibrium Systems, S-H. Chen, B. Chu, R. Nossal, Eds. (Plenum, New York, in press).

D. I. Abramson, Circulation in the Extremities (Academic, New York, 1967).

P. C. Johnson, Ed., Peripheral Circulation (Wiley, New York, 1978).

H. C. van de Hulst, Light Scattering by Small Particles (Wiley, New York, 1957).

E. Jakeman, in Photon Correlation and Light Beating Spectroscopy, H. Z. Cummins, E. R. Pike, Eds., (Plenum, New York, 1974), p. 91.

R. F. Bonner, P. Bowen, R. L. Bowman, R. Nossal, in Proceedings, Electrooptics/Laser &'78 Conference (Industrial and Scientific Conference Management, Inc., Chicago, III., 1978), p. 539.

A. Erdelyi, Ed., Tables of Integral Transforms, (McGraw-Hill, New York, 1954).

M. Abramowitz, I. A. Stegun, Eds., Handbook of Mathematical Functions (Dover, New York, 1965).

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Figures (10)

Fig. 1
Fig. 1 Diffusion of photons within the tissue can be represented as a series of scattering steps of types A, B, or C. Step A represents scattering from a static tissue element which does not impart any Doppler shift (the phase shift of this step, φA, is constant). The distance between static scattering centers |ρrj| will depend on the tissue structure but typically is ∼100 μm. Step B represents small-angle Doppler scattering from a moving red blood cell with a phase shift φβ(t), which varies as Q · vt. The probability of step B scattering increases with local blood cell concentration. Step C represents sequential scattering from two moving red blood cells and occurs within larger vessels (>50-μm diam). In this case the velocity vectors of the two cells are highly correlated, | v 1 · v 2 | υ 1 2, although the sign of the phase terms Q1 · r1(t) and Q2 · r2(t) are random.
Fig. 2
Fig. 2 Angular scattering structure factor S ( Q a ) vs Qa. Shown are (a) the approximations given by Eq. (13), which is used in our subsequent analysis, and (b) a curve based on empirical data and a Rayleigh-Gans computation of light scattered by randomly oriented biconcave disk red blood cells.21 Also shown are curves for (c) spherical red blood cells of 2.75-μm radius and polystyrene latex spheres of (d) 0.55- and (e) 0.26-μm radius, derived from Mie theory for particles in water.
Fig. 3
Fig. 3 Field autocorrelation function given by Eq. (18), I1(T), for light which is singly scattered by moving particles, plotted as a function of the reduced time variable T for different values of the particle size variable L (L = 1,2,4,15). For large particles with L > 4 (a > 0.15 μm), I1(T) has the asymptotic form given by Eq. (20).
Fig. 4
Fig. 4 Half time, T1/2, of I1(T) determined from Eq. (18) and plotted as a function of the size parameter L = 2ka. For large particles with highly anisotropic forward scattering (L > 4), the reduced time variable is given by Eq. (19).
Fig. 5
Fig. 5 Normalized intensity autocorrelation function g(2)(T) plotted as a function of the reduced time variable T [i.e., scaled by the rms speed divided by the particle radius, see Eq. (17)] for different degrees of multiple scattering and different speed distributions. Parts (a) and (b) show g(2)(T) for the Gaussian speed distribution [Eq. (12)] when m ¯ varies from 0.1 to 4. (a) For small values of m ¯, the predominant effect on g(2)(T) is an increase in amplitude with increasing m ¯. (b) For larger m ¯ the amplitude of g(2)(0) approaches a maximum, 1 + β, determined by the spatial coherence at the detector, while the decay rate increases and approaches m ¯ 1 / 2 scaling. In (c) and (d) the curves labeled A, B, C, and D represent g(2)(T) for different speed distributions: A, Gaussian [Eq. (12)]; B, uniform (Newtonian flow in a cylinder); C, single speed; D, two speed ( 1 / 3 υ ¯ and 5 / 3 υ ¯). Differences in these curves are noticeable at large T if m ¯ 1 but are insignificant for large m ¯ (where all curves become Gaussian). The relative amplitude of the slow decay (corresponding to low frequency components) is seen to increase with the variance of the speed distribution.
Fig. 6
Fig. 6 Power spectrum S(W) of the detected photocurrent computed as a function of W m ¯ 1 / 2 for m ¯ = 0.5 , 1 , 1.5 , 2 , 3 , 4 , 6 , 8 , and 10 (the curves from left to right, respectively). For m ¯ < 1, the normalized first moment of these spectra increases more rapidly than m ¯ 1 / 2, whereas for large m ¯ the spectra scale with W m ¯ 1 / 2.
Fig. 7
Fig. 7 (A) Normalized photon autocorrelation function g(2)(τ) − 1 of light backscattered from the fingertip of a normal volunteer. The decay amplitude is 90% of the empirical value of β obtained from homodyne scattering from a solution of polystyrene spheres using the same fiber optic probe and corresponds [see Eq. (24)] to m ¯ 1.2. (B) Autocorrelation functions obtained from light backscattered from the tissue model using diluted human blood with mean velocity of 1 mm/sec and hematocrits H = 0.05, 0.025, 0.006, and 0.003 (a, b, c, and d, respectively). Increased red blood cell number density (and, therefore, m ¯) results in increased decay rates and amplitudes of g(2)(τ) − 1. These experimental curves are similar to the theoretical curves shown in Fig. 5. For clarity, smoothed curves have been drawn through the data points, which are only shown for H = 0.025.
Fig. 8
Fig. 8 Mean Doppler shift 〈ω〉, as defined in Eq. (32), plotted vs mean velocity for a variety of particle sizes and volume fractions of fluid moving through the hollow fibers of the model system. In the 0–2-mm/sec range, the mean frequency 〈ω〉 increases linearly with mean particle velocity. A monotonically increasing dependence on particle density and decreasing dependence on particle radius are also observed, ag, human blood diluted to hematocrits of 0.003, 0.006, 0.012, 0.012, 0.025, 0.035, 0.050, and 0.12, respectively; hi, 0.55-μm radius PSL spheres at concentrations of 0.5 and 1.0 % v/v; j, 0.264-μm radius PSL spheres, 1 % v/v.
Fig. 9
Fig. 9 Mean Doppler frequency normalized by f ( m ¯ ) and rms particle velocity ν ¯ / f ( m ¯ ) = ω / f ( m ¯ ) V 2 1 / 2 vs the reciprocal of particle radius. Shown are data obtained when red blood cells or PSL spheres were passed through the tissue flow model. Mean frequencies have been normalized to m ¯ and mean particle speed. The solid line drawn through the data points represents values of ν ¯ / f ( m ¯ ) predicted by Eq. (36). [For red blood cells, m ¯ equals 1.43 times the vol % RBC in our model system (see insert in Fig. 10). Corresponding values of m ¯ for PSL solutions were determined from literature values14,16 of particle scattering cross sections and from particle volume ratios. From the values of m ¯ so obtained, f ( m ¯ ) was calculated by Eq. (A4).]
Fig. 10
Fig. 10 Mean Doppler shift normalized by the rms particle velocity plotted as a function of the hematocrit of the fluid flowing through the tissue model (H = 0.3–12% v/v RBC). The average degree of multiple scattering m ¯ was related to RBC concentration by determining the decay amplitudes (see insert) and fitting according to Eq. (24). Empirical values of ν ¯ = ω / V 2 1 / 2 are plotted (circles) for rms velocities in the 0.2–2-mm/sec range. The lower solid line is the theoretical curve given by Eq. (36) using an effective RBC radius of 2.8 μm and measured values of 〈V21/2 and m ¯. The upper curve is obtained from the lower merely by increasing the ordinate scale [〈V21/2/(12ξ)1/2a] by 40%. The shape of the curve depends only on f ( m ¯ ) [see Eq. (A4)].

Equations (47)

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ω = V 2 1 / 2 β ( 12 ξ ) 1 / 2 a f ( m ¯ ) ,
I 1 ( τ ) = π π S [ Q ( θ ) ] exp [ i Q ( θ ) · Δ R ( τ ) ] sin θ d θ π π S [ Q ( θ ) ] sin θ d θ ,
Q ( θ ) = 4 π n λ sin ( θ / 2 ) = 2 k sin ( θ / 2 ) .
e sc j ( t ) = E ( ρ ) exp ( ω 0 t ) A [ Q ( θ ρ ) ] × exp { i [ Q ( θ ρ ) · r j ( t ) + φ ( ρ ) + φ ( r d ) ] } ,
A ( Q ) = particle volume α ( r ) exp ( i Q · r ) d 3 r ,
S ( Q ) = | A ( Q ) | 2 .
sc ( r d , t ) tissue d 3 ρ E ( ρ ) A [ Q ( θ ρ ) ] × exp i [ Q ( θ ρ ) · r j ( t ) + φ ( ρ ) ] .
E sc ( t ) tissue D ( r d ) exp [ i φ ( r d ) ] sc ( r d , t ) d 3 r d ,
g ( 2 ) ( τ ) = n ( t ) n ( t + τ ) n 2 = 1 + β [ ( i sc i 0 ) 2 | I ( τ ) | 2 + 2 i sc ( i 0 i sc ) i 0 2 I ( τ ) ] ,
I ( τ ) E sc * ( t ) E sc ( t + τ ) .
I 1 ( τ ) D ( r d ) d 3 r d E * ( ρ ) A * [ Q ( θ ρ ) ] × exp { i [ Q ( θ ρ ) · r j ( t ) + φ ρ + φ r d ] } d 3 ρ · D ( r d ) d 3 r d E ( ρ ) A [ Q ( θ ρ ) ] × exp { i [ Q ( θ ρ ) · r j ( t + τ ) + φ ρ + φ r d ] } d 3 ρ .
I 1 ( τ ) | D ( r d ) | 2 d 3 r d i ( ρ ) S [ Q ( θ ρ ) ] × exp { i Q ( θ ρ ) · [ r j ( t + τ ) r j ( t ) ] } d 3 ρ .
exp [ i Q · Δ R ( τ ) ] = 0 j 0 ( Q V τ ) P ( V ) d V j 0 ( Q V τ ) ,
P ( V ) = ( 2 π ) 1 / 2 ( 3 V 2 ) 3 / 2 V 2 exp ( 3 V 2 / 2 V 2 ) ,
j 0 ( Q V τ ) = exp ( Q 2 V 2 τ 2 / 6 ) ,
S ( Q ) = S ( Q a ) = { 3 ( Q a ) 3 [ sin ( Q a ) Q a cos ( Q a ) ] } 2 ,
S ( Q ) exp [ 2 ξ ( Q a ) 2 ] ,
I 1 ( τ ) = 0 π S [ Q ( θ ) a ] j 0 ( Q V τ ) sin θ d θ 0 π S [ Q ( θ ) a ] sin θ d θ ,
I 1 ( τ ) = 0 1 S ( 2 kaz ) j 0 ( 2 kz V τ ) zdz 0 1 S ( 2 kz ) zdz ,
I 1 ( τ ) = 0 L exp ( 2 ξ z 2 ) exp ( T 2 z 2 ) zdz 0 L exp ( 2 ξ z 2 ) zdz ,
L = 2 ka , T = V 2 1 / 2 τ / 6 a .
I 1 ( τ ) = 2 ξ 2 ξ + T 2 [ 1 exp ( 2 ξ L 2 ) exp ( L 2 T 2 ) ] [ 1 exp ( 2 ξ L 2 ) ] .
τ 1 / 2 = 1.27 a V 2 1 / 2 ( ka large ) .
I 1 ( τ ) = 2 ξ 2 ξ + T 2 ,
I ( τ ) = m = 1 P m I m ( τ ) / ( 1 P 0 ) ,
I m ( τ ) = E sc 1 * ( t ) E sc 1 ( t + τ ) E sc 2 * ( t ) E sc 2 ( t + τ ) E sc m * ( t ) E sc m ( t + τ ) = | I 1 ( τ ) | m ,
I ( τ ) = m = 1 P m [ I 1 ( τ ) ] m / ( 1 P 0 ) .
I ( τ ) = m = 1 exp ( m ¯ ) [ m ¯ I 1 ( τ ) ] m m ! / [ 1 exp ( m ¯ ) ] = { exp [ m ¯ ( I 1 ( τ ) 1 ) ] exp ( m ¯ ) } / [ 1 exp ( m ¯ ) ]
g ( 2 ) ( τ ) = 1 + β ( exp { 2 m ¯ [ I 1 ( τ ) 1 ] } exp ( 2 m ¯ ) ) .
P ( ω ) = i 0 2 δ ( ω ) + e i 0 π + i 0 2 S ( ω ) ,
S ( ω ) = 1 π 0 cos ω t [ g ( 2 ) ( τ ) 1 ] dt .
S 1 ( ω ) = β π 2 i sc i 0 0 cos ω t I 1 ( t ) dt exp [ W ( ω ) ] ,
W ( ω ) = ( 12 ξ ) 1 / 2 ω a V 2 1 / 2 .
S ( ω ) = β exp ( 2 m ¯ ) j = 1 ( 2 m ¯ ) j S j ( ω ) j ! ,
S j ( ω ) = 1 π 0 cos Wt [ I 1 ( t ) ] j dt ,
S 1 ( ω ) = exp ( W ) ; S 2 ( ω ) = ¼ ( 1 + W ) exp ( W ) ; S 3 ( ω ) = 1 / 16 ( W 2 + 3 W + 3 ) exp ( W ) .
ω = | ω | S ( ω ) d ω .
ω = 2 β π 0 ω ( 0 cos ω t { exp 2 m ¯ [ I 1 ( t ) 1 ] exp ( 2 m ¯ ) } dt ) d ω .
ω = V 2 1 / 2 ( 12 ξ ) 1 / 2 a β f ( m ¯ ) ,
f ( m ¯ ) = 2 π 0 W ( 0 cos WZ exp { 2 m ¯ [ I 1 ( Z ) 1 ] } dZ ) dW ,
ω = V 2 1 / 2 ( 12 ξ ) 1 / 2 a β [ 2 π 1 / 2 exp ( 2 m ¯ ) j = 1 ( 2 m ¯ ) j Γ ( j + ½ ) Γ ( j + 1 ) Γ ( j ) ] .
f ( m ¯ ) = 2 exp ( 2 m ¯ ) j = 1 ( 2 m ¯ ) j f j j !
f j 1 π 0 W [ 0 cos WZ ( 1 1 + Z 2 ) j dZ ] dW .
0 cos WZ ( 1 1 + Z 2 ) j dZ = ( W 2 ) j 1 / 2 π 1 / 2 Γ ( j ) K j 1 / 2 ( W ) ,
0 W ( W 2 ) j 1 / 2 K j 1 / 2 ( W ) dW = Γ ( j + ½ ) .
f j = Γ ( j + ½ ) π 1 / 2 Γ ( j ) ,
f ( m ¯ ) = 2 exp ( 2 m ¯ ) π 1 / 2 j = 1 ( 2 m ¯ ) j Γ ( j + ½ ) Γ ( j + 1 ) Γ ( j ) .

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