IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 60, NO. 5, MAY 2013 1259

Blood Perfusion Values of Laser Speckle ContrastImaging and Laser Doppler Flowmetry:

Is a Direct Comparison Possible?Tiziano Binzoni∗, Anne Humeau–Heurtier, Pierre Abraham, and Guillaume Mahe

Abstract—Laser Doppler flowmetry (LDF) and laser specklecontrast imaging (LSCI) allow the monitoring of microvascularblood perfusion. The relationship between the measurements ob-tained by these two techniques remains unclear. In the presentcontribution, we demonstrate, experimentally and theoretically,that skin blood flow measurements obtained by LDF and LSCItechniques cannot be compared directly even after “classical” nor-malization procedure. This technical problem is generated by thenonlinear relationship existing between LDF and LSCI flow data.The experiments have been performed on five healthy voluntarysubjects (forearm) by using repeated ischemia/reperfusion cyclesto induce the necessary skin blood flow changes. LDF and LSCIdata were simultaneously acquired on the same region of interest.Considering the importance of this problem from the clinical pointof view, it is concluded that the definition of new corrected algo-rithms for LSCI is probably a mandatory step that must be takeninto account if LDF and LSCI blood flow have to be compared.

Index Terms—Human, laser Doppler flowmetry (LDF), laserspeckle contrast imaging (LSCI), skin blood perfusion.

I. INTRODUCTION

LASER speckle contrast imaging (LSCI), in its various im-plementations, is a real-time noninvasive and noncontact

technique allowing the assessment of tissue blood perfusion1

(ΦLSCI(t)) at high time (t) resolution [1]–[5]. LSCI has beensuccessfully used to assess ΦLSCI(t), e.g., in human retina, skin,and brain [4]–[6].

Manuscript received September 5, 2012; revised October 22, 2012; acceptedDecember 1, 2012. Date of publication December 11, 2012; date of currentversion April 15, 2013. This work was supported by the University Hospital ofAngers. Asterisk indicates corresponding author.

∗T. Binzoni is with the Departement de Neurosciences Fondamentales, Uni-versity of Geneva, 1211 Geneva, Switzerland, and also with the Departementde l’Imagerie et des Sciences de l’Information Medicale, University Hospital,1211 Geneva, Switzerland (e-mail: tiziano.binzoni@unige.ch).

A. Humeau–Heurtier is with LUNAM Universite, Universite d’Angers, LISA- Laboratoire d’Ingenierie des Systemes Automatises, 62 Avenue Notre Damedu Lac, 49000 Angers, France (e-mail: anne.humeau@univ-angers.fr).

G. Mahe and P. Abraham are with LUNAM Universite, Angers France, Uni-versite d’Angers, CHU Angers, Laboratoire de Physiologie et d’ExplorationsVasculaires, UMR CNRS 6214-INSERM 1083, 49933 Angers cedex 09, France(e-mail: gumahe@chu-angers.fr; piabraham@chu-angers.fr).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TBME.2012.22322941The word “perfusion” should be the exact term to use; however, for simp-

licity, in this paper, the word “flow” is also used indistinctly with the samemeaning.

For the clinician, one weak point of LSCI is that ΦLSCI(t)is expressed in arbitrary units (a.u.). This technical feature mayrepresent a serious limitation on the diagnostic power of theinstrument, because it does not allow us to define absoluteΦLSCI(t) values or to easily compare different patients. Thisis why the definition of effective procedures allowing to “nor-malize” the data is a fundamental point in this domain. In thisregard, considered that classical laser Doppler flowmetry (LDF)techniques [7] and LSCI have many common points [8], [9],well-known normalization procedures usually applied to LDFflow (ΦLDF(t)) measurements may also be applied to LSCI.For example, it is possible to normalize ΦLSCI(t)-data by astandardized maximum blood flow value induced, e.g., by lo-cal heating [10], or to normalize ΦLSCI(t) by a baseline valueobtained in controlled conditions (i.e., controlled ambient tem-perature, humidity, patient position, etc.) [10], [11].

Other phenomena, not directly linked to the a.u., may also in-fluence the reliability of the interpretation of the ΦLSCI(t) data.In fact, it is well known that, in vivo, ΦLSCI(t) does not go com-pletely to zero when the “real” tissue blood flow is null. Thisnon-null baseline, mainly studied in the past with LDF instru-mentation, is called biological zero [12]–[18] and it is probablygenerated by residual movements of cells, macromolecules [18],or other phenomena related to the function of the autonomicnervous system [19]. Thus, a “shifted” zero-flux line may alsoinduce errors in the clinical interpretation of ΦLSCI(t) signalsand a suitable correction is in principle needed. In this case, thesimplest solution is to subtract the biological zero baseline fromthe raw ΦLDF(t) or ΦLSCI(t) data [10].

It immediately appears from aforementioned examples thatthe necessary condition that allows us to apply these normal-ization/correction procedures is linked to the fact that the LSCIinstrumentation must display a linear behavior between the mea-sured ΦLSCI(t) and the “real” tissue blood perfusion values. Itis well known that this is true for ΦLDF(t) [20]–[22] but, un-fortunately, a rigorous theory linking the measured ΦLSCI(t) to“real” tissue blood perfusion values does not yet exist [23]. Inpractice, this means that it is not sure that the same normaliza-tion procedures applied to LDF can actually give comparableresults if applied to LSCI.

By using LDF as a reference technique [23], the aim ofthe present contribution is to test if normalized ΦLDF(t) andΦLSCI(t) data, simultaneously recorded on the same skin area,will allow us to obtain comparable values. This work shouldhelp us to improve the interpretation of ΦLSCI(t)-data in theclinical environment.

0018-9294/$31.00 © 2012 IEEE

1260 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 60, NO. 5, MAY 2013

II. MATERIALS AND METHODS

Skin blood perfusion (human forearm) has been acquired,noninvasively, simultaneously by two methods: LSCI and LDF.The necessary skin blood flow changes were induced by arterialocclusion and subsequent reperfusion.

A. Laser Speckle Contrast Imager

LSCI recordings were performed using a 70 mW system(PeriCam PSI System; Perimed, Jarfalla, Sweden) with a laserwavelength of 785 nm. The mean sampling frequency was18 Hz. Time sequences of intensity I(t) and variance σ(t)2 ofblurred speckle images were stored on the LSCI computer harddisk together with the exact time scale. I(t) and σ(t)2 imageswere computed in real time by the LSCI instrument applying thelaser speckle contrast analysis [24], [25] method. The integra-tion time for the charge-coupled device camera was 6 ms, andI(t) and variance σ(t)2 values at a given t were computed with asliding matrix of 3× 3 pixels [3]. The distance between the laserhead and the skin was set at 15.3 ± 0.2 cm [26]. The measuredregion of interest was 9.9 ± 1.1 × 4.7 ± 0.5 cm2 and the result-ing pixel resolution for images was 0.43 ± 0.0 mm [3], [27].Subsequently, I(t) and σ(t)2 were analyzed offline, with a soft-ware (Perimed, Jarfalla, Sweden) written in MATLAB language(The Mathworks Inc., Natick, MA) to obtain ΦLSCI(t) in a.u.The algorithm that allows us to obtain ΦLSCI(t), and suppliedwith the LSCI system, was

ΦLSCI(t) = kLSCI

(I(t)

βLSCIσ(t)− 1

)(1)

where kLSCI is a calibration constant and βLSCI is a constant fac-tor that ensures ΦLSCI(t) = 0 for static objects. The parameterskLSCI and βLSCI are set during the standard calibration proce-dure of the instrument. It is clear that (1) must be interpretedhere as a pixel-by-pixel calculation.

B. Laser Doppler Flowmeter

Cutaneous blood flow was continuously recorded using twoLDF instruments and five probes (Probe 408 and 455 & Per-iflux System 5010; Perimed).2 The interoptode distance was0.25 mm, the laser wavelength 780 nm, and the sampling fre-quency for ΦLDF(t) equals 20 Hz. ΦLDF(t) was computed inreal time (and sent to five analog outputs) by the LDF instru-ments using the classical algorithm [20]

ΦLDF(t) =kLDF

i2o

(∫ ω2

ω1

ωP (ω)dω − βLDF(io))

(2)

where ω = 2πν and ν is the frequency in Hertz. Boundary val-ues were set to ω1 = 2π50 rad/s and ω2 = 2π15 × 103 rad/s.The function P (ω) is the power density spectrum of the mea-sured photo-electric current and io is the mean value of the latter.The parameter kLDF is a calibration constant and βLDF(io) is anio-dependent factor that ensures ΦLDF(t) = 0 for static objects.The parameters kLDF and βLDF(io) are set during the standard

2The scanning laser Doppler perfusion imaging technique was too slow to beused in the present protocol.

Fig. 1. Schematic drawing (right forearm) representing 2 × 5 different po-sitions where skin blood perfusion, ΦLDF (t) and ΦLSCI (t), for LDF (bluecircles) and for LSCI (red squares), respectively, have been obtained as a func-tion of time (t). Blue circles also represent the position of the LDF opticalprobes. The large rectangle (real dimensions 9.9 ± 1.1 × 4.7 ± 0.5 cm2 ) corre-sponds to the monitored LSCI region of interest (see Fig. 3 for an example oft-tracing obtained on one blue circle and one red square.)

calibration procedure of the instrument. The five ΦLDF(t) chan-nels were acquired in real time from the analog output of theLDF instruments by using a data acquisition system (BiopacSystem, Santa Barbara, CA) and stored in the hard disk of aseparate PC.

C. Subjects

The study was performed on five healthy nonsmoking Cau-casian volunteers (two males, three females), aged 29.6 ± 2.9years, body mass 69.0 ± 10.9 kg, and stature 168.8 ± 5.7 cm.The subjects gave their informed consent to participate in thestudy. The protocol was approved by the local Ethical Commit-tee (Centre Hospitalier Universitaire d’Angers) and was con-form to the Declaration of Helsinki and registered to the Amer-ican National Institutes of Health database under reference No:NCT01169974.

D. Experimental Protocol

The measurements were performed on the volar surface ofthe right forearm (glabrous skin) with the subject resting supinein a temperature-controlled room (23 ± 1oC) [11]. The forearmwas laying securely on a dedicated cast to prevent unwantedmovements [28]. All subjects had an acclimatization period ofat least 15 min before the beginning of the LDF and LSCIacquisitions. A pressure cuff was placed on the right upper armof each subject. The five LDF probes were distributed aroundthe measured LSCI region of interest, avoiding visible veins (seeFig. 1).

The acquisition with the LDF and LSCI instruments wasstarted simultaneously. The protocol consisted in 2 min baselineand then six ischemia/reperfusion periods (3 min of ischemiafollowed by 3 min of reperfusion) [29]. Ischemic conditionswere obtained by inflating the cuff at suprasystolic pressure (220mmHg). The first baseline/ischemia period was used to stabilize

BINZONI et al.: BLOOD PERFUSION VALUES OF LASER SPECKLE CONTRAST IMAGING AND LASER DOPPLER FLOWMETRY 1261

(a)

0 0.5 1

(b)

0 0.5 1

Fig. 2. Contrast images during (a) arterial occlusion and (b) postocclusivehyperemia for a typical subject.

0 5 10 15 20 25 30 350

50

100

ΦL

DF(t

)(a

.u.)

0 5 10 15 20 25 30 350

100

200

300

t (min)

ΦL

SCI (t

)(a

.u.)

Fig. 3. Skin blood perfusion in a.u. for a typical subject, as a function of time(t), assessed by LDF (ΦLDF

free (t)) and LSCI (ΦLSCIfree (t)). Each ΦLDF

free (t) andΦLSCI

free (t) curve represents one different measurement point randomly chosenon the skin surface. Red and green regions define the steady-state basal-flowbaselines and zero-flow baselines, respectively.

the physiological system and was not used for all calculations(see Section III).

This protocol was performed at day 0 (J0) and seven daysafter (J7) on each subject.

III. RESULTS

Fig. 2 shows two contrast images (computed as K =σ(t)/I(t)) during arterial occlusion and postocclusive hyper-emia. The parameter K varies by definition in the interval [0,1].

Before performing any analysis, five random regions (avoid-ing visible veins) of 5 × 5 pixels were chosen inside the mon-itored LSCI region of interest (see Fig. 1, red squares), andfive mean t-dependent ΦLSCI(t)-values were obtained by ap-plying (1) to I(t) and σ(t)2 images. Five ΦLDF(t)-values (seeFig. 1, blue circles) were directly obtained from the LDF mea-surements. Fig. 3 shows an example of ΦLSCI(t) and ΦLDF(t)raw data, for a typical subject. The five peaks, visible for bothΦLDF(t) and ΦLSCI(t), correspond to the postocclusive reactivehyperemic reaction occurring after release of the cuff. The fivered and green regions correspond to the basal-flow baselines (nocuff) and to the biological zero-flow baselines (arterial occlu-sion), respectively. These regions were used for the calculationspresented in the following paragraphs. In total, 5 (subject) ×5 (measurements points) × 2 (LDF and LSCI) = 50 measure-

ΦLDFmax

ΦLDFbaseline ΦLDF

zero

ΦL

DF(t

)

t

Fig. 4. Schematic drawing explaining the meaning of some mathematicalsymbols utilized in this paper. The parameter ΦLDF

zero is the blood flow in thepresence of an arterial occlusion (mean value of the green region) and is called“biological zero.” The parameter ΦLDF

baseline is the free blood flow baseline value(mean value of the red region). The parameter ΦLDF

m ax is the maximum valuereached by ΦLDF (t) after opening of the arterial cuff (red circle). The definitionfor ΦLSCI

zero , ΦLSCIbaseline , and ΦLSCI

m ax is similar to LDF. Horizontal thick black linescorrespond to the ischemic periods (arterial cuff inflated).

ments, similar to that shown in Fig. 3, have been performed (forJ0 or J7). Possible spikes (artifact) appearing on ΦLSCI(t) orΦLDF(t) tracings were eliminated from the raw data by apply-ing a Savitzky–Golay filter [30].

To test the possibility to compare LDF and LSCI data af-ter the different normalization procedures (see Section I), the“biological zero”-to-“basal” flow ratios (in percent), ZLDF andZLSCI , were computed for ΦLDF(t) and ΦLSCI(t), respectively.The parameters ZLDF and ZLSCI were obtained as

ZLDF =ΦLDF

zero

ΦLDFbaseline

100 (3)

and

ZLSCI =ΦLSCI

zero

ΦLSCIbaseline

100 (4)

where ΦLDFzero , ΦLDF

baseline , ΦLSCIzero , and ΦLSCI

baseline are defined inFig. 4. For each subject, it was possible to compute 5 (ischemia–reperfusion repetitions) × 5 (temporal tracings) = 25 ZLDF (orZLSCI) values (for J0 or J7).

In Fig. 5(a)–(e), the mean (over 25 values) ZLDF and ZLSCIvalues are shown, for each single subject, at J0 and J7. The ver-tical bars are the 95% confidence intervals of the sample meanprobability distribution. The confidence intervals have been es-timated by bootstrap using the bias corrected and acceleratedpercentile method [31]. It clearly appears that for a given exper-imental session (J0 or J7) and subject, ZLSCI is always signifi-cantly larger than ZLDF (the computed confidence intervals bybootstrap method do not superpose [32]). To estimate the mag-nitude of this difference, Fig. 5(f) shows the 100 ZLSCIZ

−1LDF

ratios for the grouped subjects, at J0 and J7. The 95% confi-dence intervals of the sample mean probability distribution arecalculated as previously. The two values, 162.4 [140.2, 186,2]

1262 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 60, NO. 5, MAY 2013

20

40

60Z

LD

F,Z

LSC

I(%

) (a)

20

40

60 (b)

20

40

60

ZL

DF,Z

LSC

I(%

) (c)

J0L J0S J7L J7S

20

40

60 (d)

J0L J0S J7L J7S

20

40

60

ZL

DF,Z

LSC

I(%

) (e)

J0 J70

100

200

300

ZL

SCI/

ZL

DF*

100

(%) (f)

Fig. 5. (a)–(e) Ratio (in percent) of the biological zero divided by the baselineblood flow [see (3) and (4)], ZLDF and ZLSCI , for LDF and LSCI, respectively.The horizontal (vertical) scales are the same. J0L: from LDF measurements, J0(black circles). J7L: from LDF measurements, J7 (black circles). J0S: fromLSCI measurements, J0 (red circles). J7S: from LSCI measurements, J7 (redcircles). Each panel from (a)–(e) represents a different subject. (f) Mean over allthe subjects appearing in (a)–(e). For all the panels, the vertical bars representthe 95% confidence intervals of the sample mean probability distribution.

and 210.0 [147.8, 263.8] (square parenthesis are the confidenceintervals), are not significantly different (i.e., independent of themeasurement day) and show that ZLSCI goes from one and ahalf to the double of the ZLDF magnitude.

To evaluate the effect of the normalization by a maximum tis-sue blood flow on the ΦLDF(t) and ΦLSCI(t) data, the followingratios have also been computed:

PLDF =ΦLDF

baseline

ΦLDFmax

100 (5)

and

PLSCI =ΦLSCI

baseline

ΦLSCImax

100 (6)

where ΦLDFmax and ΦLSCI

max are defined in Fig. 4. In Fig. 6(a)–(e),the mean (over 25 values) PLDF and PLSCI values are shown,for each subject, at J0 and J7. The vertical bars are the 95% con-fidence intervals of the sample mean probability distribution.As previously, the confidence intervals have been estimated bybootstrap using the bias corrected and accelerated percentilemethod. For a given experimental session (J0 or J7) and subject,PLSCI is always significantly larger than PLDF . In Fig. 6(f), the100 PLSCIP

−1LDF ratios for the grouped subjects are shown, at J0

and J7. The 95% confidence intervals of the sample mean prob-ability distribution are calculated as previously. The two values,193.4 [168.1, 235.7] and 213.3 [174.8, 261.2], are not signifi-

10

20

30

40

50

PLD

F,P

LSC

I(%

)

(a)

10

20

30

40

50 (b)

10

20

30

40

50

PLD

F,P

LSC

I(%

)

(c)

J0L J0S J7L J7S

10

20

30

40

50 (d)

J0L J0S J7L J7S

10

20

30

40

50

PLD

F,P

LSC

I(%

)

(e)

J0 J70

100

200

300

PLSC

I/P

LD

F*

100

(%) (f)

Fig. 6. (a)–(e) Ratio (in percent) of the baseline blood flow divided by themaximum flow reached after opening of the arterial cuff [see (5) and (6)],PLDF and PLSCI , for LDF and LSCI, respectively. The horizontal (vertical)scales are the same. J0L: from LDF measurements, J0 (black circles). J7L:from LDF measurements, J7 (black circles). J0S: from LSCI measurements, J0(red circles). J7S: from LSCI measurements, J7 (red circles). Each panel from(a)–(e) represents a different subject. (f) Mean over all the subjects appearingin (a)–(e). For all the panels, the vertical bars represent the 95% confidenceintervals of the sample mean probability distribution.

cantly different (i.e., independent of the measurement day) andshows that PLSCI is about the double of the PLDF magnitude.

In order to understand the reason of the difference in mag-nitude existing between ZLDF and ZLSCI or PLDF and PLSCI(see also Section IV), Fig. 7(a) shows the “calibration curve” ofΦLSCI as a function of ΦLDF for a typical subject. In this case,the ΦLDF values have been grouped by bins of 3 a.u. widthand the corresponding ΦLSCI values chosen accordingly (thisis why the variable t disappeared from the symbols). Then, themean value inside each bin (for ΦLDF and the correspondingΦLSCI points) has been computed and reported on the figure.Considering that ΦLDF(t) and ΦLSCI(t) did not have the samesampling frequency, to perform this operation, it has been nec-essary to resample the ΦLSCI(t) signal to the LDF frequency bylinear interpolation. Other types of interpolation method (e.g.,nearest neighbor, cubic spline) did not change the present find-ings (data not shown). Again, the 95% confidence intervals ofthe sample mean probability distribution have been obtainedas previously. We can clearly observe from Fig. 7(a) that the“calibration curve” is nonlinear.

Concerning the “calibration curve,” it is important to notethat a rigorous model linking ΦLSCI and ΦLDF [see Fig. 7(a)]does not yet exist [23]; however, a potential mathematical modelmust satisfy at fortiori some basic and obvious constraints: 1) themodel describing the experimental points appearing in Fig. 7(a)

BINZONI et al.: BLOOD PERFUSION VALUES OF LASER SPECKLE CONTRAST IMAGING AND LASER DOPPLER FLOWMETRY 1263

0 20 40 60 80 1000

50

100

150

ΦLSC

I(a

.u.)

(a)

0 20 40 60 80 1000

50

100

150

ΦLSC

I(a

.u.)

ΦLDF (a.u.)

(b)

J0J7

Fig. 7. (a) Mean skin perfusion values (ΦLSCI ) assessed by LSCI technique,for a typical subject, as a function of the same variable but assessed by LDF,ΦLDF . Vertical and horizontal bars are the 95% confidence intervals of thesample mean probability distribution. The blue line represents the model fittingthe experimental data (see the paper). (b) Same variables as in (a), but whereonly the models computed for each subject independently, at J0 (black) and J7(red), are shown. In this paper, we will call these curves “calibration curves.”The blue line in (a) and the longest black line in (b) are the same curves. Thevertical dashed lines define the interval where the mean biological zero values(i.e., for all subjects) are situated.

must be monotonically increasing due to the properties of LDFand LSCI; 2) due to the calibration procedures and the physicalproperties of LDF and LSCI instrumentation, the first point mustbe at the origin (the instruments are build to give ΦLSCI = 0 andΦLDF = 0 with a “real” zero perfusion value, e.g., on a staticobject), and 3) the model must become linear for increasing per-fusion values. For this reason, to be independent of any specificmodel, but with the aim to satisfy the aforementioned constraint,a shape prescriptive modeling (fitting) approach [33] has beenapplied to the “calibration curve” experimental data. This mod-eling procedure is based on least squares cubic Hermite splinesfitting with constraints. The constraints were the ones listed pre-viously. Fig. 7(a) shows the resulting model (in blue) appliedto the experimental data for the typical subject. Fig. 7(b) showsthe fitted models, independently, for each subject at J0 and J7(for the sake of clarity, in this figure, the experimental datahave not been reported). It is clear that all the resulting curvesare very similar. In Section IV, we will discuss the practicalconsequences of these results.

IV. DISCUSSION AND CONCLUSION

In the present contribution, we have shown that ZLDF <ZLSCI and PLDF < PLSCI for all subjects and experimentalsessions (see Figs. 5 and 6). In fact, it is easy to demonstratethat for any “calibration curve” having a shape similar to theones presented in Fig. 7, the aforementioned inequalities arealways satisfied.

Proof: Fig. 8 is aimed to explain this point in a graphicaland easy manner. The black line represents any model sub-mitted to the constraints considered in Section III and similarto Fig. 7(b). The function f describes this general curve, i.e.,

00

ΦLDF

ΦL

SCI=

f(Φ

LD

F)

(Φzero,f (Φ )

Φ Φzero)

(Φzero, f (Φzero)) (Φbaseline , f (Φbaseline))

Fig. 8. Drawing representing a general calibration curve linking skin bloodflow measured by LDF (ΦLDF ) and by LSCI (ΦLSCI ). The parenthesis nearthe colored circles are the coordinates.

ΦLSCI = f(ΦLDF). The red circles represent possible valuesfor the biological zero (Φzero) and the baseline flow (Φbaseline),respectively. Now, thanks to (3) and to the law of similarity fortriangles (see Fig. 8)

ZLDF = [Φzero ]1

Φbaseline100

=[f(Φbaseline)

ΦbaselineΦzero

]1

f(Φbaseline)100 (7)

(note that the ratio f(Φbaseline)Φ−1baseline is the slope of the dotted

line shown in Fig. 8). It is clear from Fig. 8 that

f(Φbaseline)Φbaseline

Φzero < f(Φbaseline) (8)

and thus from (4), (7), and (8)

ZLSCI = [f(Φzero)]1

f(Φbaseline)100 > ZLDF . (9)

�It is important to note that this demonstration holds for any

couple of “red” points shown on the black curve in Fig. 8, andthus also for

PLSCI > PLDF . (10)

This conclusion remains also valid independently of the calibra-tion value of kLDF or kLSCI . In practice, this means that, for thesame experimental protocol, classical normalization proceduresor baseline corrections applied to LDF signals cannot give thesame results if applied to LSCI.

To give a more intuitive representation of the practical con-sequences of the present results, in Fig. 9(a), a general ΦLDF(t)curve (black line) is reported. Fig. 9(b) shows a typical “calibra-tion curve” [data from Fig. 7(a)]. With the “calibration curve”and ΦLDF(t), we can now compute the corresponding theo-retical ΦLSCI(t). The computed ΦLSCI(t) is shown in Fig. 9(b)(red curve). Both ΦLDF(t) and ΦLSCI(t) data have been normal-ized to 100% by the corresponding ΦLDF

max and ΦLSCImax maximum

1264 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 60, NO. 5, MAY 2013

0 1 2 3 4 5 6 70

50

100Φ

LD

F(t

),Φ

LSC

I (t)

(%)

t (min)

(a)ΦLDF(t)ΦLSCI(t)

0 20 40 60 80 1000

50

100

150 (b)

ΦL

SCI

(a.u

.)

ΦLDF (a.u.)

Fig. 9. (a) Typical skin blood flow data assessed by LDF (ΦLDF (t), blackline) during an ischemia/reperfusion/ischemia sequence. LSCI flow (ΦLSCI (t),red line) computed from ΦLDF (t) by using the “calibration curve” shown in(b). (b) The colored surface defines the values spanned by ΦLDF in (a).

values. Thus, from Fig. 9(a), one can observe that normalizedΦLDF(t) and ΦLSCI(t) data are not comparable. Even in thecase where we previously subtract the biological-zero baselinebefore normalization, ΦLDF(t) and ΦLSCI(t) remain fundamen-tally different.

From this study, and in particular from Fig. 7(b), it appearsthat to obtain comparable LDF and LSCI data, it would be nec-essary 1) to measure a “calibration curve” for each subject andexperimental protocol, 2) linearize ΦLSCI(t) with the “calibra-tion curve,” and 3) apply a suitable normalization procedure.However, this is probably a too heavy and long procedure to beapplied in a clinical environment. In this case, the best solutionwould perhaps be to redefine (1) with another algorithm. In fact,LSCI and LDF techniques are strongly correlated because theyare based on the same physical phenomenon [34]. Intuitively,LSCI behaves as LDF where the power density spectrum is “fil-tered” with a low-pass filter before computing flow values [35].The positive effect of this “filter” (depending on the integra-tion time) is that it allows the detection of blood flow changeswith LSCI. The drawback is that it may create a nonlinear re-lationship with the real flow values of the tissue, and a lowerdynamic range for LSCI compared to LDF. Algorithms takinginto account this phenomenon may be considered. An investi-gation in this sense, based on the original Fercher’ and Briers’algorithm [36], has been realized by Dunn et al. [37]. With thisalgorithm, the authors found a linear correlation between LSCIand LDF on rat brain measurements. However, more sophisti-cated algorithms mean longer calculation times, and this maydrastically slow down real-time LSCI. These considerations willcertainly be matter for future investigations.

Another factor that might influence the LSCI nonlinearitycompared to LDF is the different penetration depth due to dif-ferent light sources used (“large spot” versus “point source”).In fact, it has been shown with Monte Carlo simulations, usingthe same light source configurations as LSCI and LDF, that the

penetration depths may differ more than one half [38], [39]. Toinvestigate this problem, a new experimental approach wouldbe useful. Full-field laser Doppler perfusion imaging (FFLDPI)[40], [41] is an emerging technique that might be better com-pared with LSCI than LDF, due to the same method of illumina-tion (large region). However, for the moment, the simultaneoususe of LSCI and FFLDPI is prevented by the presence of the twolasers. The problem of the synchronization of this techniquesdeserves future attention.

ACKNOWLEDGMENT

The authors would like to thank Perimed AB, Jarfalla, Swe-den, for the detailed technical and theoretical explanations onLDF and LSCI instrumentation.

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