Quantitative Analysis of Peripheral Tissue Perfusion
Using Spatiotemporal Molecular Dynamics
Yujung Kang1., Myunghwan Choi1., Jungsul Lee1, Gou Young Koh2,3,4, Kihwan Kwon5, Chulhee
Choi1,3,4*
1 Department of Bio and Brain Engineering, KAIST, Daejeon, Korea, 2 Department of Biological Sciences, KAIST, Daejeon, Korea, 3 Graduate School of Medical Science andEngineering, KAIST, Daejeon, Korea, 4 KI for the BioCentury, KAIST, Daejeon, Korea, 5 Department of Cardiology, School of Medicine, Ewha Womans University, Seoul,
Korea

Abstract
Background: Accurate measurement of peripheral tissue perfusion is challenging but necessary to diagnose peripheral
vascular insufficiency. Because near infrared (NIR) radiation can penetrate relatively deep into tissue, significant attention
has been given to intravital NIR fluorescence imaging.
Methodology/Principal Findings: We developed a new optical imaging-based strategy for quantitative measurement of
peripheral tissue perfusion by time-series analysis of local pharmacokinetics of the NIR fluorophore, indocyanine green (ICG).
Time-series NIR fluorescence images were obtained after injecting ICG intravenously in a murine hindlimb ischemia model.
Mathematical modeling and computational simulations were used for translating time-series ICG images into quantitative
pixel perfusion rates and a perfusion map. We could successfully predict the prognosis of ischemic hindlimbs based on theperfusion profiles obtained immediately after surgery, which were dependent on the preexisting collaterals. This method
also reflected increases in perfusion and improvements in prognosis of ischemic hindlimbs induced by treatment with
vascular endothelial growth factor and COMP-angiopoietin-1.
Conclusions/Significance: We propose that this novel NIR-imaging-based strategy is a powerful tool for biomedical studies
related to the evaluation of therapeutic interventions directed at stimulating angiogenesis.
Citation: Kang Y, Choi M, Lee J, Koh GY, Kwon K, et al. (2009) Quantitative Analysis of Peripheral Tissue Perfusion Using Spatiotemporal Molecular Dynamics. PLoS
ONE 4(1): e4275. doi:10.1371/journal.pone.0004275
Editor: Timothy Secomb, University of Arizona, United States of America
Received October 16, 2008; Accepted December 15, 2008; Published January 26, 2009
Copyright: ß 2009 Kang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by the Chung Moon Soul Center for BioInformation and BioElectronics, KAIST. The funders had no role in study design, datacollection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: cchoi@kaist.ac.kr. These authors contributed equally to this work.

tools is a primary concern for clinical applications. Several
methods have been used to quantify perfusion, including
scintigraphic and positron emission tomography (PET) imaging
and magnetic resonance imaging (MRI). However, these methods
are too expensive for use in the diagnosis of tissue perfusion,
especially for animal studies. The standard quantitative method
for measuring perfusion in animals is microsphere perfusion;
however, this method is suitable for ex vivo, rather than in vivo,
measurements. Optical imaging, especially near infrared (NIR)
fluorescence imaging, has proven effective for in vivo imaging of thevasculature and for estimation of functional perfusion [8].
For decades, indocyanine green (ICG) has been clinically used
as a NIR fluorophore for intravital imaging, a marker for liverfunction [9], and a sensitizer for photodynamic therapy [10,11].
For vascular imaging, ICG is FDA-approved, and NIR spectra
enable deep tissue imaging [12,13]. In addition, intravenouslyinjected ICG shows no extravasation except in abnormallypermeable vasculature because ICG binds to the major serum
protein, albumin [14]. Albumin-ICG complexes are segregated in
the liver with first-order pharmacokinetics and excreted via thehepatobiliary pathway [15,16]. Nevertheless, the rapid clearance

Introduction
Peripheral tissues are vulnerable to necrosis under conditions
that promote vascular insufficiency. Peripheral vascular insufficiencies are highly prevalent and usually result from diabetic
complications or systemic atherosclerosis [1–3]. Perfusion imaging
tools used to visualize the structure of the vasculature or functional
perfusion levels are useful in preclinical drug discovery researchand could have clinical applications [4,5]. For these purposes,
functional perfusion imaging is superior to structural vascularimaging because prognosis of vascular insufficiency is directly
coupled to the functional perfusion level rather than to thevascular structure [6]. For example, highly varied outcomes of theischemic hindlimbs might arise from differences in preexisting
collaterals [7]. Therefore, measurements of the functional tissue
perfusion through vessels including collaterals are required toevaluate vascular insufficiency and predict the prognosis. In
addition, quantitative measurements of peripheral tissue perfusion
are required to enable comparative studies, e.g., evaluating theeffects of drugs in vivo or making clinical decisions based on patient
information. Minimizing the invasiveness of perfusion imaging
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Imaging of Tissue Perfusionof ICG has limited its use as an angiographic contrast medium.
Recently, it was shown that the altered pharmacokinetics of ICG
can be useful for estimating cerebral oxygenation and hemodynamics [17] and for measuring cerebral blood flow (CBF) [18–20].
Even though mathematical analysis was used to extract CBF fromICG dynamics in these studies, a lack of quantitative information
limited the use of these techniques in the clinical setting [18]. The
main purpose of our study was to develop a new method forfunctional and quantitative measurement of peripheral tissue
perfusion and to demonstrate the feasibility of this method.

function of ICG excretion by the liver because liver dynamics
dominate ICG excretion [16]. Therefore, ICG fluorescence intensity
in normal tissues could be described by the following equations:tFInor ~Vd et

ð1Þ

and

t~

Methods

t12
ln2

,

ð2Þ

where FInor is the ICG fluorescence intensity in the VIF of normal
tissues, Vd, is the vascular density, t is time, t is the time constant forsystemic dynamics, and t1/2 is the ICG half-life in the trunk.
The half-life of ICG decay varies, ranging from 3 to 5 min,according to idiosyncratic variations in hepatic function [9],anesthetizing conditions, and hemodynamic status. To normalize
the variations in the excretion rate, we introduced ‘t’ for all equations
mentioned below.
The change in concentration of the ROI can be described using
Fick’s law:

Murine hindlimb ischemia model
BalB/cAnNCriBgi-nu nude male mice were obtained fromCharles River Japan Inc. (Yokohama, Japan). All mice were 7–8
weeks (15–20 g) of age at the time of study. Hindlimb ischemia
was induced by ligation and excision of the right femoral arteryand vein under ketamine–xylazine anesthesia. Animal care andexperimental procedures were performed under the approval of
the Animal Care Committees of KAIST.
For therapeutic angiogenesis studies, mice were divided into fourgroups after induction of ischemia for intramuscular injection withsaline solution (20 mL with 0.9% NaCl and 0.1% bovine serum
albumen (BSA)) alone as a control or saline solution containing
COMP-angiopoietin-1 (cAng1) (20 mL of 200 ng/mL), vascularendothelial growth factor (VEGF) (20 mL of 130 ng/mL), or bothcAng1 and VEGF (20 mL of 200 ng/mL cAng1 and 130 ng/mLVEGF). Serial ICG perfusion imaging was performed immediately
after surgery and on postoperative day (POD) 3 and 7.

fchanges in the concentrationg~finflowg{foutflowg
Therefore, the dynamics of ICG fluorescence intensity in theischemic hindlimbs, FIisc, are a function of time and are
represented by
dFIisc ðtÞ~P:FInor {P:FIisc ,dt

NIR fluorescence imaging

ð3Þ

where parameter ‘P’ represents the perfusion rate of the ischemic
hindlimbs. The FIisc is defined as follows using Equations 1 and 3:

We used two optical systems for NIR fluorescence imaging:
Image station 4000 MM (Eastman Kodak Co., Rochester, NY,
USA) and a customized system developed for this study. The
customized imaging system employed a CCD digital camera(PIXIS 1024; Princeton instruments, Princeton, NJ, USA) with acustom-made 830-nm band-pass filter (Asahi Spectra USA,
Torrance, CA, USA) and 760-nm light-emitting diode arrays(SMC760; Marubeni America, Sunnyvale, CA, USA).
For time-series ICG imaging, mice under ketamine–xylazine
anesthesia were injected with an intravenous bolus injection of
ICG (0.1 mL of 400 mmol/L; Sigma, St. Louis, MO, USA) into
the tail vein. ICG fluorescence images were obtained for 12 min in
20-s intervals (Kodak imaging system) or in 1-s intervals (custom
optical imaging system) immediately after injection. After the serial
imaging, a silhouette image of the mouse was taken under whitelight to obtain the region of interest (ROI) mask of the ischemic
and normal hindlimbs.

FIisc ~


PVd  {tt {e{Pt
e
P{ 1t

ð4Þ

Because the dynamics of ICG fluorescence intensity after tissue
injection showed a bell-shaped curve, the differential value of FIiscis assigned as zero at the time-to-peak (Tmax). Therefore, thedifferential value of equation 4 is zero at the Tmax.

dFIisc ðtÞ P:Vd
1 {Tmax
t zPe{PTmax ~0
~
e
{
dt
t
P{ 1t

{lnt{

Tmax
~lnP{PTmax
t

ð5Þ

ð6Þ

In silico modelingTo identify the underlying relationships between the spatiotemporal ICG profiles and the perfusion rates in peripheral tissue, we
performed an in silico study that included mathematical modeling andcomputational simulation analysis of ICG pharmacokinetics. To
quantitatively represent tissue perfusion, perfusion rate (%/min) was
defined as the fraction of blood exchanged per minute in the vascularvolume of the ROI. We assumed that blood volume through thevasculature is time invariant during the imaging; therefore,
volumetric inflow and outflow were thought to be the same (Figure
S1A). To describe the regional ICG pharmacokinetics in vivo, the rearhalf of a mouse was divided into three compartments: the trunk, thenormal hindlimb, and the ischemic hindlimb. Vascular input function(VIF) of ICG in normal tissues has been described as a uniexponential
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From equation 5, we could derive equation 6 and calculate theperfusion rate (P) of each pixel in the ROI using two parameters, theTmax of each pixel and ICG half-life in the trunk. The simulation for
the temporal ICG dynamics was run with different perfusion rate
values (Figure S1B), which was consistent with actual ICG dynamics(see Figure 1C). The relationships among the ICG half-life, the timeto-peak, and the perfusion rate are shown in Figure S1C and D.

Image analysis
ROI masks of ischemic and normal limbs were drawn on thesilhouette image to extract regions corresponding to limbs in theacquired time-series NIR fluorescence images. Likewise, the square
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Figure 1. Quantitative measurement of perfusion rate based on spatiotemporal ICG dynamics. (A) Contrast NIR fluorescenceangiography. (B) Temporal sequence of ICG fluorescence in the hindlimb ischemia model. The number indicates the time after ICG injection. (C)Temporal dynamics in the regions indicated with boxes in D are plotted. Relative fluorescence units (R.F.U.) were normalized to the maximal intensity.
(D) Perfusion map reconstructed based on the time-series ICG images shown in B. (E) Histogram of perfusion rates in each limb for the regions
indicated by the dotted box in D. The arrows indicate two perfusion rate peaks in the normal limb. (F) Photographic image of a normal hindlimb after
skin excision to expose femoral vessels (right). Perfusion map of the limb (left). (H) Histogram of perfusion rates of the total hindlimb is indicated by
the blue line, and the red line depicts the perfusion rates of the ROIs marked by the red line on femoral vessels.
doi:10.1371/journal.pone.0004275.g001

mask was also drawn on the trunk region to obtain parameter t forcalculating the systemic half-life of ICG. Image processing and data
analysis were performed using Visual C++ (version 6.0; Microsoft,
Redmond, WA, USA) and Matlab (MathWorks, Natick, MA, USA).
The half–life of ICG was measured in the square mask region
on the trunk and the time-to-peak of each pixel was determined toobtain the perfusion rate of a pixel in the ROI. The pixel numberof a ROI in a limb was typically around 1200–1500. The perfusion
rate in each pixel was then calculated using the equation derived
by modeling. The result was visualized as a pseudocolor-coded
perfusion map or a histogram.
To determine the probability of necrosis, necrotic regions in theischemic limbs were assessed on POD 7. Perfusion rates that were
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measured 4 h post-op were analyzed in both the necrotic and nonnecrotic regions. The correlation between the regional perfusion
rate (average perfusion rate of 262 pixels) and the necrosis of thisregion was calculated. The relationship between necrosis determined at POD 7 and the regional perfusion rate measured
immediately after surgery was obtained based on data from 20
mice. A necrosis map was generated by mapping the predictive
necrosis of individual regions as a color-coded picture.

Histological analysis
Vessel density within the calf muscles of the ischemic limbs wasquantified by histological analysis. The ischemic muscles were
perfused with 4% (w/v) paraformaldehyde (Sigma) and embedded
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Imaging of Tissue Perfusionin paraffin. Calf muscle sections (10 mm thickness) were stainedwith hematoxylin–eosin (H&E) and also double-stained with antiCD31 antibody (Chemicon, Temecula, CA, USA) and a
monoclonal anti-a-smooth muscle-actin (a-SMA) antibody (Sigma)
conjugated to fluorescein isothiocyanate (FITC). Proteins immunoreactive with the anti-CD31 antibody were stained with ahamster anti-mouse IgG antibody conjugated with rhodamine(Jackson Laboratories, West Grove, PA, USA). The stained
sections were visualized by confocal microscopy (Axiovert LSM
510 META; Zeiss, Oberkochen, Germany). Vessel density was
expressed as the number of a-SMA and CD31 double-positive
micro- (16–63 mm in diameter) and macrovessels (.63 mm indiameter) per high-power field (magnification6400) [21].

hindlimb (Figure 1C), suggesting spatial heterogeneity of perfusion in
the peripheral tissue. Based on the equation obtained in silico, wetranslated the temporal ICG dynamics of each pixel into theperfusion rate and constructed a perfusion map (Figure 1D). This
perfusion map showed the spatial distribution of perfusion rates withhigh spatial resolution. Histogram analysis showed a bimodal
distribution of perfusion rates in the normal limb, while only one
peak with a lower perfusion rate was observed in the ischemic
hindlimb (Figure 1E). The two peaks of perfusion in the normal
hindlimb appeared to correspond to macrovasculature regions withhigh perfusion rates and microvascular compartments with lower
perfusion rates. To confirm whether the right peak of the bimodal
distribution corresponded to macrovascular regions, we removed theskin of the calf to reveal the femoral artery and vein and performed
ICG perfusion imaging (Figure 1F). The exposed femoral artery andvein were distinguishable with higher perfusion rates compared to thesurrounding area in the perfusion map (Figure 1F, right panel).
Moreover, the perfusion rates of the ROI corresponding to the
exposed macrovascular regions matched the right peak of thebimodal distribution in the histogram of the normal limb (Figure 1G).
Therefore, the histogram analysis of perfusion clearly indicated thatthe induction of ischemia abolished macrovasculature perfusion andsignificantly reduced microvascular perfusion.

Laser Doppler imaging (LDI)
Mice were scanned using LDI (MLDI5063; Moor Instruments
Ltd., Devon, UK) after ICG perfusion imaging at POD 0. Three
consecutive scans were performed until blood flow measurements
were stable. The images were subjected to computer-assisted
quantification of blood flow.

Micro-CT angiography
Mice were placed in an induction chamber with 4% isofluoranein oxygen to induce anesthesia. After anesthetization, mice were
injected with PEG-conjugated gold nanoparticles (400 mL of
0.2 mol/L) intravenously and placed on a volumetric CT scanner(NFR-MXSCAN-G90; NanoFocusRay, Iksan, Korea) [22]. Whilescanning, the mice remained anesthetized under 1.5% isofluoranein oxygen. In total, 600 images were acquired at 65 kVp, 55 mA,
and 800 ms per frame. Images were reconstructed using the
Feldkamp cone-beam reconstruction algorithm. The reconstruction image size was 102461024 pixels, and 512 slices wereacquired. The final reconstructed data were converted to theDigital Imaging and Communications in Medicine (DICOM)
format to make three-dimensional (3-D)-rendered images using 3D-rendering software (Lucion; MeviSYS, Seoul, Korea).

Functional levels of perfusion by preexisting collaterals
determine the prognosis of hindlimbs after induction ofischemia
To validate ICG dynamic perfusion imaging analysis, weestimated the tissue perfusion immediately after induction ofischemia (Figure 2A). Normal hindlimbs demonstrated a perfusion
rate of 475619%/min, while the perfusion rate of ischemic
hindlimbs was 5666%/min (12.2% of the normal hindlimbs).
Considerable variability was observed in the outcome of theischemic hindlimbs, which might have arisen from differences inpreexisting collaterals [7]. To validate our method, we investigatedthe correlation between the initial perfusion rates of the ischemic
tissues and the prognosis, both of which are dependent on
preexisting collaterals. The animals were classified into threegroups according to the severity score of limb necrosis (score
0 = no necrosis; score 1 = toe necrosis; score 2 = foot necrosis; score
3 = ankle necrosis; score 4 = autoamputation of the entire leg.
Moderate necrosis includes scores 1 and 2 and severe necrosis
includes scores 4 and 5.), which was determined by visualinspection on POD 7. These groups included no necrosis (16%),
moderate necrosis (40%), and severe necrosis (46%; Figure S2).
We then compared the perfusion rates of these three groups fromthe ischemic limbs immediately after surgery (Figure 2B). In 16%of the animals with the best prognosis, the perfusion rate of theischemic hindlimbs was significantly higher compared to othergroups; the average perfusion rates of the groups with moderateand severe necrosis were 19.4 and 4.11%/min, respectively(Figure 2B). However, the representative data of the ischemic/
normal blood flow ratio using LDI did not provide enoughsensitivity to show correlations between the initial blood flow andthe necrosis level in the ischemic limbs. Since the preexistingvasculature appeared to contribute significantly to the prognosis,
we questioned whether the perfusion map obtained immediately
after surgery could precisely predict future necrosis. To address
this, we determined the relationship between the probability ofregional tissue necrosis on POD 7 and the given perfusion rate of
the corresponding region after operation (Figure 2C). This
relationship was expressed in an inverse sigmoidal function. By
mapping perfusion rates with necrosis probability, we generated acolor-coded necrosis map to display the spatial distribution of

Statistical analysis
Data are expressed as means6SEM. Statistical significance wasassessed by Student’s two-tailed t-test for two groups or by oneway ANOVA and Bonferroni post hoc test for three or more groups.

Results
Estimation of tissue perfusion based on time-series
analysis of ICG pharmacokinetics
The feasibility of NIR fluorescence imaging [8] was examined tomonitor peripheral tissue perfusion using albumin microspheresconjugated with the NIR fluorophore ICG (Method S1) [23].
Contrast NIR fluorescence angiography showed obvious differences
in maximal fluorescence intensity between normal and ischemic
hindlimbs (Figure 1A). Even though contrast optical imaging
distinguished well-perfused from hypoperfused tissues, it only
provided qualitative information. Because the local concentrationof ICG is dependent on regional tissue perfusion, we measured thespatiotemporal dynamics of ICG pharmacokinetics by obtaining
time-series images of free ICG fluorescence [17]. As expected, ICG
dynamics in normal hindlimbs showed a rapid time-to-peak ofaround 20 s after ICG injection and an exponential decay of ICG
fluorescence (Figure 1B, upper panel). In contrast, ischemic limbs
showed markedly altered dynamics of ICG fluorescence compared tocontrols and a remarkably delayed time-to-peak and clearance(Figure 1B, lower panel). We also observed considerable differences in
the ICG dynamics between different regions in the same ischemic
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Figure 2. Necrosis predictability of ICG dynamic perfusion imaging. (A) Average limb perfusion rates of the normal and ischemic hindlimbs
at POD 0 (postoperative day 0; Post-Op.). The gray lines indicate the mean of each group. The P value is shown (Student’s t-test, n = 24). (B)Correlation between average limb perfusion rates in the ischemic limbs at POD 0 (black boxes and whiskers) and limb necrosis levels at POD 7.
Bonferroni post hoc test applied to the significant effect of groups (ANOVA F2,44 = 58.358, P,0.001). *, P,0.001. **, P = 0.054. The y-axis is in log scale.
The gray boxes and whiskers show a relationship between the ischemic/normal laser Doppler blood flow ratio at POD 0 and limb necrosis levels at
POD 7. (ANOVA F2,16 = 0.465, P = 0.636). (C) Sigmoidal relationship between regional perfusion rates of the ischemic hindlimbs at POD 0 and necrosis
probability of the corresponding region at POD 7. Graphs were drawn from data of 70,714 regions from 20 mice (Boltzmann sigmoidal fit, R2 = 0.998,
P,0.001). The x-axis is in log scale. (D) Comparison of diagnostic predictability between LDI and ICG perfusion imaging. For three representative micewith different prognoses, LDI images, perfusion maps, and necrosis maps at POD 0 are shown along with pictures of the ischemic limbs at POD 7. The
region for the necrosis map from the corresponding area in the perfusion map is indicated by the white dotted boxes.
doi:10.1371/journal.pone.0004275.g002

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necrosis probability on POD 7. Comparisons between thepredicted levels of necrosis to the actual necrosis levels in ischemic
limbs verified the feasibility and accuracy of the necrosis map. The
perfusion maps of representative mice showed considerable
contrasts in ischemic limb perfusion rates, and the necrosis maps
showed a remarkably high correlation with the actual necrotic
regions that were determined on POD 7 (Figure 2D). To further
validate the sensitivity of our newly developed tool using ICG
dynamic imaging, we compared the perfusion map to conventional LDI, the current ‘‘gold standard’’ for in vivo blood flow
measurements (Figure 2D, left panel) [24]. The LDI images of thethree representative mice could only demonstrate overall qualitative decreases in the levels of perfusion in the ischemic limbs andcould not, on a more quantitative level, differentiate between levelsof perfusion (Figure S3). These results collectively indicate thesuperior sensitivity of ICG dynamic optical imaging, which issensitive enough to predict future necrosis of ischemic tissues.

histogram analysis of the ischemic limb also showed a gradualtime-dependent shift to the right; however, the histogram of
perfusion rates on POD 7 showed incomplete recovery whencompared to the pre-op level (Figure 4, upper right panel). In a
representative case in the combination treatment group, the postop limb perfusion rate was estimated to be 68%/min, and no
necrosis was predicted. The limb perfusion rate increased to161%/min by POD 3 and increased to 364%/min by POD 7(Figure 4, lower left panel). The histogram showed almost
complete recovery of perfusion by POD 7 when compared tothe initial level of perfusion before surgery; notably, there was arecovery of macrovascular conductance on POD 7 even though
the level was lower than that of the basal level, suggesting
regeneration of the macrovasculature by combined treatment of
VEGF and cAng1 (Figure 4, lower right panel). Likewise, thepostmortem histological study demonstrated that PECAM and aSMA double-positive vessels increased significantly in number andsize in the intermuscular septa and muscle fibers in the combined
treatment group compared to the control group (Figure 5A).
Quantitative measurements showed a 19.7-fold increase in
macrovascular density, while the combined treatment increased
the density of both the macro- and microvessels (Figure 5B).
Moreover, micro-CT scans depicted more than three macrovessels(.150 mm in diameter) in the calf of the treated animals on POD
7 (Figure 5C, left panel); there were only thin macrovessels (smaller
than 100 mm) in the calf of the control animals (Figure 5C, rightpanel). Collectively, these results suggest that combined treatment
with VEGF and cAng1 increases perfusion and improves theoutcome of the ischemic hindlimbs partly via induction ofarteriogenesis and microvascular angiogenesis.

Vascular endothelial growth factor and COMP-ang1
synergistically improve perfusion and subsequent
prognosis of ischemic hindlimbs
For therapeutic angiogenesis studies, 70 mice were divided into
four groups. The initial perfusion rates of the ischemic limbs were
not significantly different among groups (ANOVA, F3,33 = 0.667,
p = 0.578, Figure S4). After intramuscular injection of angiogenic
factors in the ischemic limbs, follow-up ICG perfusion imaging
was serially performed on POD 3 and 7. As expected, theprobability of necrosis in regions with low perfusion rates (2–60%/min) was significantly improved by treatment with VEGF, cAng1,
or both (Figure 3A). Intergroup comparisons of time-dependent
increments in perfusion rates clearly demonstrated synergistic
therapeutic effects of VEGF and cAng1, especially between POD
3 and 7 (Figure 3B and Figure S5); this was consistent with resultsfrom our previous study [25]. The representative comparison
shows that combined treatment with VEGF and cAng1 improved
perfusion and the subsequent prognosis of the ischemic hindlimbs(Figure 3C). In the control animals, the amount of necrotic tissue
accurately matched the necrosis predicted by postoperative (postop) perfusion measurements. Slightly diminished perfusion was
initially observed in the combined treatment animal, which waspredicted to have a poorer prognosis compared to the control;
however, the final outcome was much better than that of thecontrol, indicating a protective effect of combined treatment.
Histogram analysis showed that the unimodal peak shifted to theright after surgery and was almost completely restored to the levelof the normal hindlimb, suggesting a dominant effect ofproangiogenic factors on the microvasculature.

Discussion
In this paper, we propose a novel NIR imaging method for thequantitative measurement of perfusion in peripheral tissues using
planar reflectance imaging, which is simpler to implementcompared to the tomographic method, by introducing mathematical modeling and a computational simulation. The validity of thisnovel method was evaluated using a murine model of hindlimb
ischemia and therapeutic interventions directed at stimulation ofangiogenesis. We showed that time-series imaging of intravenously
injected ICG can provide quantitative information regarding
peripheral tissue perfusion precisely enough to predict the
prognosis of the ischemic tissues and to verify the therapeutic
effects of proangiogenic factors such as VEGF and cAng1. For
analysis of ICG dynamics, we designed a biologically meaningfulmathematical model.
Previous studies that used ICG dynamics to estimate blood
perfusion failed to provide quantitative information that was
required for interindividual comparison. In those studies, blood
perfusion was estimated based on the maximal fluorescenceintensities of injected ICG, which depend heavily on theheterogenous optical properties of regional tissues [17,18].
Moreover, static reflectance images from different animals orfrom the same animal at different time points are generally
insufficient for yielding quantitative insights, because the fluorescence intensity depends on the depth of the signal and the volume
and optical properties of the tissue. Thus, we performed timeseries NIR fluorescence imaging to obtain the spatiotemporal
dynamics of ICG and translated the time-series images intoquantitative perfusion maps. Furthermore, we compensated for
idiosyncratic variations in ICG pharmacokinetics to obtain
quantitative information on perfusion rates, because the idiosyncratic variation of ICG dynamics depends on the level ofArteriogenesis partly mediates improvement of tissue
perfusion
As previously indicated, some animals never developed tissue
necrosis even though identical surgical protocols to induce
ischemia were used. Therefore, uneven distribution of suchnonresponsive animals into the study groups can severely distort
the results [26]. Since our method can quantitatively determine
the efficacy of the treatment on the perfusion rate rather thangrading visible necrosis, we tested whether our method was also
useful for evaluating the efficacy of the proangiogenic factors inthis nonresponsive group (Figure 4). The perfusion rate of arepresentative case in the control group at post-op was measured
as 56%/min; hence, the ischemic limb was predicted to besalvaged. The limb perfusion rate increased to 183%/min on
POD 3 and 227%/min on POD 7 (Figure 4, upper left panel). The
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Figure 3. Synergistic proangiogenic effects of VEGF and cAng1. (A) Correlations between necrosis probability and regional perfusion rates
were determined. (B) Differences in perfusion rates according to the time period are indicated for each group. ANOVA and Bonferroni post hoc test
applied to the significant effect of groups on D (POD 3 - POD 7), (ANOVA F3,33 = 4.890, P = 0.006). {{, P = 0.004 vs. BSA control. Paired t-tests were
performed for each intragroup comparison. (C) Representative examples of BSA control and combined treatment groups.
doi:10.1371/journal.pone.0004275.g003

The necrosis profiles of the ischemic limbs were variable among
subjects even when the same surgery protocol and the same strainof animals were used [26]. Necrosis profiles of the ischemic
hindlimbs are considered to be determined by two main factors:

anesthesia, systemic hemodynamic properties, and variations inhepatic clearance that can affect the evaluation of perfusion rate.
This quantitative information enabled us to predict the necrotic
profile of ischemic tissues.
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Figure 4. Therapeutic effects of VEGF and cAng1 on tissue perfusion. Representative cases of BSA control and combined treatment groups.
doi:10.1371/journal.pone.0004275.g004

the preexisting collateral circulation [7] and vascular genesis[27,28]. Variability of necrosis profiles between subjects could becaused primarily by differences in the preexisting collateralcirculation, because the substrates of vascular genesis arepreexisting collateral arteries [29]. Therefore, we made two
predictions. First, if the perfusion rate measured at post-op
represents the preexisting collateral circulation, we predicted that
this would show a significant relationship with future necrosis of
the ischemic limb. As expected, the regional perfusion rate showeda significant inverse (sigmoidal shape) relationship with theprobability of necrosis of the region. Second, we predicted thataiding angiogenesis by treatment with proangiogenic factors would
alter the necrosis profile based on the preexisting collateralcirculation. Significantly, treatment with the proangiogenic factors
VEGF and cAng1 improved the necrosis profile.
Our method has several advantages for analyzing the perfusionand prediction of necrosis compared to conventional methods. First,
quantitative measurement of tissue perfusion can allow interindividual comparative analysis of tissue perfusion. Second, this highly
sensitive and quantitative measurement of perfusion can be used toequally distribute ischemic hindlimb model mice in control andexperimental groups, which would lessen data distortion anddecrease the variability of the experimental results. We haveobserved that the prognosis of ischemic tissues from animals
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subjected to identical surgical protocols varies considerably. This
results in intrinsic data heterogeneity, which could severely
misrepresent the outcome of drug interventions. Third, changes in
perfusion due to proangiogenic factors in ischemic tissues can beevaluated and arteriogenic processes can be differentiated fromangiogenesis because our method can classify conductance throughthe macrovasculature. We observed that combined treatment with
VEGF and cAng1 restored the bimodal histogram perfusion rate
pattern in the ischemic limbs, reflecting the emergence ofmacrovascular components. Histological and CT-angiogram analysis also showed that combined treatment with VEGF and cAng1
induced arteriogenesis along with angiogenesis. These results
suggest that ICG dynamic perfusion imaging might be applied todifferentiate macroangiopathy from microangiopathy.
Our method has several potential limitations as well. First,
induction of ischemia transiently changes vascular permeability
and dilatation, which play roles in functional recovery from acutehindlimb ischemia [30]. Our method might underestimate
perfusion rates in cases of increased vascular permeability becausedelayed washout kinetics of ICG as a result of leakage are typicallyinterpreted as decreased perfusion. Moreover, our method cannot
distinguish between perfusion changes due to blood flow versus
microvessel density. Therefore, it is important to control thephysiologic factors that can affect blood flow such as blood
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Figure 5. Arteriogenic effects of VEGF and cAng1. (A) Confocal micrographs of calf muscle sections from ischemic limbs treated with VEGF/
cAng1 and BSA. Magnification, 2006. Bar = 100 mm. (B) Macro- and microvessel densities were evaluated in the calf muscles. The number of vessels
was expressed as vessels/mm2. Data from six fields of the four samples were averaged. Student’s t-test was applied. (C) Micro-CT angiography. The
arrows indicate macrovessels in the calf.
doi:10.1371/journal.pone.0004275.g005

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Imaging of Tissue Perfusionpressure, pulse rate, and body temperature for consistentmeasurement of tissue perfusion. Second, even though NIR canpenetrate biological tissues deeply, the depth of NIR penetration
nonetheless restricts measurement of perfusion to peripheral
tissues. For analysis of deep-seated organs, endoscope-coupledtechnology might help to overcome this limitation. Finally, it istechnically difficult to estimate accurate perfusion rates for normal
tissues, especially in the case of normal tissues that have very fast
ICG kinetics. For accurate measurements of such fast kinetics, theimages with maximal intensity should be captured, whichcorrespond to images of the time-to-peak. Since the 20 s timeseries imaging of the Kodak imaging system is too long an interval
to capture the time-to-peak of normal limbs, we developed acustomized NIR fluorescence imaging system that can shorten theinterval to as little as 1 s. In this way, we were able to capture thepeak intensity successfully even in normal limbs. In addition we
confirmed that these two imaging methods provided basically thesame results when estimating perfusion rates in ischemic limbs,
which showed slower ICG kinetics.
In summary, our results indicate that ICG dynamic perfusion
imaging can quantitatively evaluate perfusion in peripheral tissues.
Moreover, this imaging technology could be used to predict future
necrosis profiles and evaluate angiogenesis. ICG perfusion imaging
can be a cost- and time-efficient tool for diagnosis of patients with
peripheral vascular insufficiencies. Although further studies
evaluating ICG imaging for clinical applications are needed, we
believe that this revolutionary imaging method may provide new
impulse to the multidisciplinary analysis of biomedical imaging
and may contribute to the development of clinical diagnostic
techniques for peripheral vascular disease.

ICG. (D) Two parameters that determine the decay phase in ICG
dynamics. The graph shows that both the half-life of ICG and theperfusion rate contribute to the time-to-peak of ICG dynamics.
Found at: doi:10.1371/journal.pone.0004275.s002 (1.37 MB TIF)
Figure S2 Natural course of ischemic limbs. Severity score ofischemic hindlimb necrosis was assessed using the following scale:score 0 = no necrosis; score 1 = toe necrosis; score 2 = foot necrosis;
score 3 = ankle necrosis; score 4 = autoamputation of the entire
leg. Moderate necrosis includes scores 1 and 2 and severe necrosis
includes scores 4 and 5. The prognosis of the ischemic hindlimbs
of each group was plotted with the severity score over time. Notethat tissue necrosis occurred mainly within 7 days after surgery.
Found at: doi:10.1371/journal.pone.0004275.s003 (0.36 MB TIF)
Figure S3 Comparison of the tissue perfusion from a collectionof ROIs between the ICG perfusion imaging and LDI. Note the
LDI cannot differentiate between the levels of perfusion especiallyin the low perfusion section, from 1% to 100%/min. In thissection, the broad range of the necrosis probability was distributed
from 0% to 90%.
Found at: doi:10.1371/journal.pone.0004275.s004 (0.16 MB TIF)
Figure S4 Post-operative perfusion rates in the ischemic limbs offour groups of mice that were distributed evenly for therapeutic
angiogenesis study. Note the non-significant difference in perfusion
rates at post-op among the groups [ANOVA, F(3,33) = 0.667,
p = 0.578, Bonferroni Post hoc: p = 1 among all groups].
Found at: doi:10.1371/journal.pone.0004275.s005 (0.16 MB TIF)
Figure S5 Each line indicates the time-dependent change of theaverage perfusion rate of ischemic hindlimbs from each mouse.
Found at: doi:10.1371/journal.pone.0004275.s006 (0.33 MB TIF)

Supporting Information

Acknowledgments

Contrast NIR fluorescence angiography
Found at: doi:10.1371/journal.pone.0004275.s001 (0.02 MB
DOC)
Method S1

We thank Prof. C. Park (Department of Anatomy and Neurobiology,
Kyunghee University) and Y. Koh (Graduate School of Medical Scienceand Engineering, KAIST) for help with the immunohistochemical studies
and Prof. K. Yoon (Department of Radiology, Wonkwang University) for
help with the micro-CT angiograhy.

Figure S1 (A) Schematic model diagram for in silico analysis:
simplified model for local tissue perfusion including volumetricinflow (vi), volumetric outflow (vo), and blood volume of thevasculature (Vv). (B) Simulated results for temporal ICG dynamics
with different perfusion rates. Maximal intensity has beennormalized. R.F.U., relative fluorescence units. (C) Simulated
ICG dynamics with different perfusion rate values and half-lives of

Author Contributions
Conceived and designed the experiments: GYK KK CC. Performed theexperiments: YK MC. Analyzed the data: YK MC. Wrote the paper: YK
MC CC. In silico study: MC JL. Contributed proangiogenic factors: GYK.

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