three-dimensional volumetric measurement of red blood cell motion using digital holographic...

Three-dimensional volumetric measurementof red blood cell motion using digital

holographic microscopy

Yong-Seok Choi and Sang-Joon Lee*Center for Bio-fluid and Biomimetics Research, Department of Mechanical Engineering,

Pohang University of Science and Technology, Pohang 790-784, Korea

*Corresponding author: [email protected]

Received 4 March 2009; revised 27 April 2009; accepted 29 April 2009;posted 29 April 2009 (Doc. ID 108194); published 20 May 2009

Measurement of blood flow with high spatial and temporal resolutions in a three-dimensional (3D)volume is a challenge in biomedical research fields. In this study, digital holographic microscopy is usedto measure the 3D motion of human red blood cells (RBCs) in a microscale volume. The cinematographicholography technique, which uses a high-speed camera, enabled the continuous tracking of individualRBCs in a microtube flow. Several autofocus functions that quantify the sharpness of reconstructed RBCimages are evaluated to locate the accurate depthwise position of RBCs. In this study, the squaredLaplacian function yields the smallest depth of focus and locates the depthwise positions of RBCs witha root mean square error of 2:3 μm. By applying this method, we demonstrate the measurement of four-dimensional (space and time) trajectories as well as 3D velocity profiles of RBCs. The measurementuncertainties of the present method are also discussed. © 2009 Optical Society of America

OCIS codes: 170.3880, 180.6900, 090.1995.

1. Introduction

Recently, the hemodynamic information of blood flowhas been receiving much attention due to the rapidincrease in the occurrence of circulatory vascular dis-eases. It is important to obtain detailed informationon blood flow because a disordered blood flow can sig-nificantly influence the progression of congenital oracquired circulatory diseases. For example, a skewedvelocity profile in a blood vessel can create a dead-flow pocket in which the value of wall shear stressis very small.Themajority of previous studies on blood flowwere

carried out from a clinical point of view. However,they did not provide detailed hemodynamic informa-tion of blood flow due to the technological limitationsof conventional measurement techniques, such asangiography or ultrasound Doppler analysis, whichgive information on vessel shape or blood flow speed

with poor spatial resolutions. Nowadays, variousparticle image velocimetry (PIV) velocity field mea-surement techniques have been applied to numerousin vivo/in vitro hemorheological studies [1–3].However, conventional PIV methods inherentlyprovide two-dimensional (2D) planar informationconfined in a thin depth of field.

In the field of ocular hemodynamic research,optical coherence tomography (OCT) [4] has beenused as a noninvasive 3D imaging method to observethe microstructure of biological tissues with highspatial resolution. With the improved imaging speedof Fourier domain OCT (FD-OCT), it is possible to de-tect a Doppler shift of the reflected light that pro-vides the flow velocity information. However, theflow information is based on the 3D vessel geometryand the detected Doppler shift [5]. Therefore, it is yetdifficult for this technique to observe the dynamicmotion of individual blood cells in a blood flow.

Holography is capable of recording 3D volumetricfield information on a single hologram. The recentdevelopment of digital holography enables the

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1 June 2009 / Vol. 48, No. 16 / APPLIED OPTICS 2983

volumetric measurement of particle fields withoutthe use of any chemical or physical processes [6,7].In this technique, a digital hologram of the particlesdistributed in a flow is directly recorded on a digitalmedium. The 3D flow information can be subse-quently obtained through the numerical reconstruc-tion and the particle tracking procedure. However, asthe pixel size of most commercial digital media iscomparably larger than the wavelength of the micro-scopic interference fringes, the poor accuracy pro-blem in the depthwise direction arises. Xu et al.[8], Satake et al. [9], and Sheng et al. [10] combineddigital holography and optical microscopy to over-come the pixel size limitation. The basic concept ofdigital holographic microscopy (DHM) is to magnifythe hologram image by adopting an optical lens sys-tem so that the microscopic fringes can be resolved.Satake et al. [9,11] and Kim and Lee [12] employedthe DHM technique with tracer particles to measure3D flows in a microchannel and a microtube. As a re-cent biological application of DHM, Sheng et al.[13]analyzed changes in the swimming behavior of dino-flagellates due to the existence of prey animals.In the present study, we applied the DHM techni-

que to measure the 3D motion of human red bloodcells (RBCs) moving in a microtube flow. Comparedto the OCT technique, DHM requires only a pair ofparticle hologram images to get complete 3D flow in-formation and is of great advantage in motion ana-lysis of individual blood cells. The feasibility anduncertainty of the established DHM system in thedetection of 3D RBC position were evaluated by aplanar test target. The depthwise position of aRBC was located by applying focus functions thatquantify the sharpness of its reconstructed image[14]. Five focus functions were evaluated to findthe suitable function that provides minimum uncer-tainty. Finally, the sample trajectories as well as the3D velocity profiles of RBCs inside the microtubeflow are presented and the measurement uncertain-ties are discussed. As far as we surveyed, this is thefirst trial to apply the DHM technique in the field ofhemodynamic research.

2. Methodology

A. Sample Preparation

Human blood from a healthy donor was used as thetest sample. It was first heparinized to prevent coa-gulation. RBCs were separated from the blood sam-ple using centrifugation and aspiration of the plasmaand the buffy coat. Then the RBCs were mixed withplasma in a hematocrit of 0.05%. The hematocrit wasmaintained at a very low value to reduce the over-lapping of RBCs in a hologram image. The numberconcentration of RBCs in the test solution was3:2 × 103 cells=mm3. The mean diameter of the RBCstested in this study was about 7 μm.

B. Optical Setup

In this study, a single beam in-line holography setupwas used. This simple optical setup utilizes only asingle reference wave for light illumination. If a par-ticle exists in the pathway of the reference wave, dif-fraction occurs by the particle. The superposition ofthe unaffected reference wave and the diffractedwave creates fringe patterns on the image plane.

Figure 1 shows the experimental setup for measur-ing RBC motion in a circular microtube flow. It con-sists of a He–Ne laser (λ ¼ 633nm, 15mW), a spatialfilter, a water immersion objective lens (Nikon,20 ×W, NA ¼ 0:50), and a high-speed digital camera(PCO, 1200hs). The objective lens is attached in frontof the camera with a proper tube length to get a mag-nified hologram at the imaging plane. The camerahas a CMOS array of 1280 × 1024 pixel resolutionwith a pixel size of 12 μm × 12 μm. Hence, the pixelresolution of the measurement system is about0:6 μm (pixel size/magnification power). The actualresolution of the optical system can be calculatedfrom the resolving power defined as the power to re-cognize the two points:

R ¼ 0:61λ

NA: ð1Þ

The actual resolution R of the present experimentalsetup is about 0:77 μm.

The RBC sample solution is supplied to the fluori-nated ethylene propylene (FEP) microtube, which isimmersed in water. Refractive image distortions arenot observed since the refractive indices of the sam-ple solution (plasma, n ¼ 1:346); FEP, n ¼ 1:338; andwater, n ¼ 1:333) are nearly identical. The wallthickness and the inner diameter of the tube are100 and 350 μm, respectively. The Reynolds numberbased on the inner diameter and the mean flowvelocity (3:0mm=s) is nearly unity. The focal planeof the objective lens is adjusted to a position justabove the tube. Digital holograms are sequentially

Fig. 1. (Color online) Experimental setup for measuring red bloodcell motion in a circular microtube flow.

2984 APPLIED OPTICS / Vol. 48, No. 16 / 1 June 2009

captured by the high-speed camera. The time inter-val between consecutive frames is 2:0ms and theexposure time of each frame is 10 μs.Figure 2 represents the optical setup employed in

the uncertainty analysis for position measurement ofRBCs. The RBC sample solution is placed betweentwo optically flat slide glasses to make a planar testtarget. The target is placed on an axially moving tra-verse of 1 μm precision to control the position of thetarget from the focal plane.

C. Numerical Reconstruction

The digital holograms are numerically reconstructedby employing the angular spectrum method [15].This method has a high accuracy because it doesnot require any simplifications, such as the Fresnelapproximation [6,16]. The reconstruction procedureconsists of convolution of the hologram functionhðx; yÞ and diffraction kernel gðξ − x; η − yÞ at each re-construction plane. It can be efficiently calculated byemploying fast Fourier transforms (FFTs):

Γðξ; ηÞ ¼ I−1½Ifhðx; yÞgIfgðξ − x; η − yÞg�; ð2Þ

where If g and I−1f g denote the FFTand the inverseFFT, respectively; x and y represent the spatial coor-dinates in the hologram plane, while ξ and η belong tothe reconstruction plane. The intensity field of thereconstructed images can be obtained from jΓðξ; ηÞj.The angular spectrum analysis introduced byGoodman [17] can eliminate the FFT routine forthe diffraction kernel:

Ifgðξ; ηÞg ¼ Gðf ξ; f ηÞ

¼ exp�ikd

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1 − ðλf ξÞ2 − ðλf ηÞ2

q �: ð3Þ

In this equation, f ξ and f η represent the spectral co-ordinates, λ is the laser wavelength, k ¼ 2π=λ, and dis the distance between the hologram plane and thereconstruction plane. As the algorithm preserves aconstant scale in the reconstructed image, it is par-ticularly suitable for the volumetric flow measure-ment. A detailed description of DHM can be foundin [8,10].

3. Measurement Accuracy for RBC Position

A. Image Sharpness Quantification

Langehanenberg et al. [14] tested several numericalfocusing methods for digital holographic phase-contrast microscopy to find the optimal focus planefrom a series of reconstructed live cell images. Thefocus functions based on the quantification of imagesharpness successfully located the position of thefocus plane where the cell is actually placed. Inaddition, some functions provide a significantly nar-rower depth of focus. This suggests that the accuracyof the depthwise position measurement may be en-hanced by adopting those focus functions. In this pa-per, several advanced numerical approaches areadopted. In the following equations, Iðx; y; zÞ is thediscrete intensity distribution of a reconstructed im-age segmented in a section enclosing a single bloodcell, and z is the depthwise position of the reconstruc-tion plane:

GRAðzÞ ¼Xx;y

j∇Iðx; y; zÞj ð4Þ

performs summation of intensity differencesbetween neighboring pixels. The value of GRA in-creases as long as the image sharpness increases[18].

LAPðzÞ ¼Xx;y

f∇2Iðx; y; zÞg2 ð5Þ

accounts for the variation in the intensity gradient.This parameter can be used to measure the high-frequency edges in the image by neglecting the partsof constant intensity gradient [18].

VARðzÞ ¼ 1NxNy

Xx;y

fIðx; y; zÞ − �IðzÞg2 ð6Þ

quantifies the image contrast, which is an indicatorof image sharpness [19]. Nx and Ny represent theimage dimensions.

SPECðzÞ ¼Xf x;f y

log½1þ jIfIðx; y; zÞ − �IðzÞgj� ð7Þ

indicates the high spatial-frequency component con-tained in the image with a logarithmic weighting[19]. The mean intensity value is subtracted tosuppress the DC component of the image.Fig. 2. (Color online) Optical setup for uncertainty analysis.


For comparison, the intensity function defined asbelow is also evaluated:

INTENSITYðzÞ ¼�Iðx; y; zÞ − IcenterðzÞ

�Iðx; y; zminÞ − IcenterðzminÞ: ð8Þ

In this equation, zmin is the plane of minimum inten-sity and the subscript “center” refers to the center ofthe segmented cell image [10].

B. Uncertainty Analysis Using a Planar Test Target

The positioning accuracy of the established measure-ment technique is estimated using a planar test tar-get, as shown in Fig. 2. Starting from the focal plane,digital hologram images are captured by traversingthe test target to a distance of 100, 200, and 300 μm.Typical hologram images obtained at four differenttarget positions are shown in Fig. 3. Except for theimage obtained at the focal plane, fringe patternsare generated by the superposition of a wave dif-fracted from the RBCs and an unaffected referencewave. The large fringe pattern in the background re-sults from an optical misalignment. It can be re-moved by using a background removal process.The background is obtained by performing a boxfiltering on the hologram [6].Figure 4 shows typical examples of ahologram, a re-

construction, an image projection, and a segmenta-tion. Figure 4(a) is a 350 × 350 pixel (208 μm×208 μm) section of the original hologram (1024×1024 pixel; 610 μm × 610 μm) obtained at a distanceof 100 μm from the focal plane. Numerical reconstruc-tionof thehologramiscarriedoutusingEqs. (2)and (3)

at 200 reconstruction slices with 1 μm intervals in thedepth direction. Figures 4(b)–4(d) show typical recon-struction images at three different depths. At the cor-rect focal depth (Δz ¼ 0 μm), RBCs appear as darkspots with sharp boundaries. The images recon-structed at some depth positions from the focal planeshowblurred edges. Figure 4(e) is a bright-field imageofRBCs in the test target positionedat the focal plane.ThereconstructedimagesofthesameRBCsareshownatFigs.4(f)–4(h).The imagereconstructedat theexactfocal depth is comparable with the bright-field image.Since the tilt of diffracted reference wave is not largeenough to preserve the diffraction-limited resolutionin the holographic reconstruction, the reconstructedimage shows a similar resolutionwith the bright-fieldimage.As shown inFig. 5, themaximum tilt angle θ atwhich the wavelength of the fringe can beresolvable is calculated by

θ < sin−1 λ2R

: ð9Þ

Here, λ is the wavelength of illuminated light sourceand R is the actual resolution of the optical setup.The calculated maximum angle θ is about 24°. Thisis a much improved value compared to the tilt angleof 2°–3° for the in-line holography setup, which doesnot utilize a lens system.

Fig. 3. (Color online) Digital hologram obtained from a planarRBC target positioned at (a) 0 μm, (b) 100 μm, (c) 200 μm, and(d) 300 μm from the focal plane.

Fig. 4. (Color online) (a) Typical digital hologram of a planarRBC target positioned at 100 μm from the focal plane. (b), (c),(d) Reconstructed images at three selected depth planes.(e) Microscopic image of RBCs positioned at the focal plane. (f),(g), (h) Reconstructions of the hologram of RBCs of (e) at selecteddepth planes. (i) Projection of reconstructed RBCs of (a).(j) Segmentation of RBCs after bandpass filtering of (i).


For each hologram, one projection image is gener-ated by scanning the reconstructed images and copy-ing the minimum intensity value for each pixelposition. Since the light intensity of a focused RBChas a local minimum value in the reconstructed im-age, the projection image contains the information ofthe in-plane ðx; yÞ position of all the focused RBCs.Figure 4(i) is a projection image showing the in-planeposition of all the RBCs originally distributed in 3Dðx; y; zÞ space. Before entering into the segmentationprocess, a bandpass filter and a peak-searching algo-rithm are applied to the projection image to locatethe in-plane center position of each RBC. The in-plane position of an object can be detected in pixelresolution since we use a digital camera. For the op-tical system used in this study, the in-plane measure-ment accuracy is �0:3 μm in the x and y directions.The segmentation process makes a list of rectangu-

lar sections enclosing each RBC. The rectangularshape is selected for the simplification of the imagesharpness calculation. Figure 4(j) shows the segmen-ted sections centered at each RBC. The size of eachsection is determined by using the threshold pixel in-tensity, which is 80% of the RBC center. The depth-wise position of each RBC is determined by scanningthe series of segmented reconstruction images. Byapplying a focus function, which repeatedly performsthe image sharpness quantification to the segmentedimages, a series of focus values is generated for eachRBC. The depthwise position of a RBC is determinedat the position where the maximum focus valueappears.Figure 6(a) represents the depthwise profiles of the

focus value of the segmented RBC images for severalimage sharpness quantification methods. The pro-files are ensemble-averaged for 300 RBCs in the testtarget. All the profiles have a distinct peak at the re-construction depth where the target is actuallyplaced. The oscillations in the profiles are attributedto the interference patterns that alternatively ap-peared in the reconstruction images. The interfer-ence is induced by light scattering from the RBCsand the reference light. However, the oscillation levelis reduced in all the image sharpness quantificationmethods, compared to the intensity-based method(INTENSITY). Among the tested methods, the

LAP profile shows the minimum level of oscillation.From these results, we can see that the LAP methodis less influenced by the unfocused fringes in the re-construction procedure due to the intrinsic feature ofneglecting constant intensity gradients. In addition,when the depth of focus is defined as the regionwhere the focus value is larger than 80% of the peakvalue, the LAP profile shows the narrowest depthof focus.

In Table 1, the mean depth of focus, RMS position-ing errors, and computation time for eachmethod aresummarized. Compared to the GRA method, thedepth of focus is decreased about 50% for the LAPmethod. The depthwise RMS positioning error is also

Fig. 5. Schematics of tilt angle between the reference wave anddiffracted wave.

Fig. 6. (Color online) (a) Comparison of ensemble-averaged focusvalue profiles for several image sharpness quantification methods.(b) Spatial distribution of RBC positions obtained by the LAPmethod.

Table 1. Comparison of Mean Depth of Focus, RMS Position Error,and Normalized Computation Time of Five Focus Functions

FocusFunction

Mean Depthof Focus (μm)

RMSPosition

Error (μm)

NormalizedComputation

Time

INTENSITY 12.4 2.9 0.61GRA 14.3 3.5 0.98SPEC 10.8 2.9 0.71VAR 11.4 3.2 0.58LAP 7.4 2.3 1


decreased as the depth of focus decreases. As theactual RBCs are located in the same plane, theRMS error can be interpreted as a measurement un-certainty in the depthwise direction. Compared tothe in-plane measurement uncertainty value of�0:3 μm, uncertainty in the depth direction is about8 times larger. In the consideration of computationtime, the calculation time for each method is normal-ized with the time used for the LAP method.Although the LAP method takes the longest compu-tation time, it is nearly of the same order of magni-tude as all the other methods. Figure 6(b) shows the3D spatial distribution of RBC positions in the testtarget obtained using the LAP method.The effect of reconstruction depth (z) on the focus

value is analyzed from the holograms obtained bytraversing the test target to distances of 100, 200,and 300 μm from the focal plane. Figure 7(a) showsthe ensemble-averaged LAP profiles of the recon-structed images. If the propagation distance of thelight diffracted from an object increases, the regionoccupied by a fringe pattern also increases. Hence,loss of fringe information occurs outside the recon-struction medium. The increase of oscillation levelin the profile results from the decrease of the sig-nal-to-noise ratio (SNR) in the reconstruction, intro-duced by the loss of fringe information out of thehologram plane. The value of the secondary peakin the oscillation shows a linear increase. This im-

plies that the oscillation level is increased as the re-construction depth increases. If the peak continues toincrease linearly, then its value reaches 80% of theprimary peak around z ¼ 1mm. This will be the lim-iting boundary for the measurement volume in thedepth direction.

Figure 7(b) represents the measured depth posi-tion of the RBCs. They are obviously positioned ad-joining each measurement plane. Probability densityfunctions (PDFs) of the depthwise position measure-ment errors (z − zmean) are shown in Figs. 7(c)–7(e).As the reconstruction depth increases, the standarddeviation (σ) of the position measurement errors isincreased.

4. Application: Three-Dimensional Tracking of RBCsin a Microtube Flow

The 3D motions of RBCs in a circular microtube floware analyzed from 1000 hologram images capturedconsecutively. For each hologram, 200 slices arenumerically reconstructed with 2 μm spacing. Onaverage, 185 RBCs are observed in a hologramoccupying the measurement volume of 600 μm×350 μm × 350 μm. Figure 8 shows a typical hologramimage of RBCs and reconstructed images at threedepth planes. Figure 8(a) represents a 512 × 256pixel section of an original 1024 × 1024 pixel holo-gram. The numerically reconstructed images of thesection at three selected depth planes are shownin Figs. 8(b)–8(d). In each reconstruction image, someRBCs are clearly observed in good focus. Near thebottom wall the number of focused RBCs increases.This seems to be caused by the slowly creeping RBCson the bottom wall due to the density difference be-tween RBCs and plasma. The average density ofRBCs (ρ ¼ 1:095) is slightly bigger than blood plasma(ρ ¼ 1:027). Once RBCs settle on the bottom surface,they cannot rise directly into the central region of theflow because the flow is laminar. In Fig. 8(e), two sets

Fig. 7. (Color online) (a) Ensemble-averaged LAP profiles atthree reconstruction depths. (b) Depth positions of RBCs at threemeasurement planes. (c) Probability density functions of thedepthwise position measurement errors (z − zmin).

Fig. 8. (Color online) (a) Typical RBC hologram obtained in acircular microtube flow. (b), (c), (d) Reconstructed images at threeselected depth planes. Each focused RBC is shown by an arrow.(e) 3D spatial distribution of RBCsmeasured from two consecutiveholograms.


of RBC positions obtained from a pair of consecutiveholograms are depicted together. The displacementvector of each RBC can be traced unambiguouslyin 3D space by adopting a particle tracking velocime-try (PTV) algorithm [20]. On average, 89 velocityvectors are tracked from a pair of holograms.Figure 9 shows an instantaneously obtained 3D ve-

locity field. The z-directional velocity componentshows large fluctuations. This may result from thelow measurement accuracy in the depth direction,as described in Subsection 3.B. The spatial resolu-tion, statistical velocity fluctuations, ratio of velocityfluctuations to the mean streamwise velocity compo-nent, andmeasurement uncertainty are summarizedin Table 2. All instantaneous vector fields are ensem-ble averaged to obtain time-averaged flow statistics.The time-averaged streamwise velocity component(U) profiles are shown in Fig. 10. These velocity pro-files are in a good agreement with the theoreticalprofile of a Hagen–Poiseuille flow. Maximum andmean velocities calculated from the flow rate are5.96 and 2:98mm=s, respectively. The RMS errorsare also calculated by multiplying the statistical ve-locity fluctuations and the time interval between twoconsecutive image frames, because the velocity fluc-tuations in a laminar flow are mainly introduced bymeasurement errors. Interestingly, measurement er-ror in the z-direction is smaller than the uncertaintylevel obtained from the uncertainty analysis using aplanar test target. This is caused by the thresholderror value used in the PTV algorithm. RBCs thatshow depthwise movement larger than the threshold

value are discarded as errors because twice the re-construction depth spacing is used as the thresholdvalue.

The four-dimensional (space and time) trajectoriesof two typical RBCs are shown in Fig. 11. The RBCpositions are tracked from 60 holograms capturedconsecutively. Linear movement of RBCs is clearlyseen in the microtube. Two RBCs located at differentradial positions have different traveling distancesduring the same time period. Even though some fluc-tuations are observed in the z-directional positions,the 3D motion of RBCs can be tracked accuratelywithin an acceptable degree of uncertainty.

5. Summary and Conclusion

DHM is applied to measure 3D motion of RBCs in amicrotube. In this study, the features of DHM,

Fig. 9. 3D vector field shown from different view angles.

Table 2. Comparison of Measurement Resolution, RMS VelocityFluctuation, and RMS Errors in Three Coordinate Axes

CoordinateMeasurementAccuracy (μm)

RMS VelocityFluctuation(mm=s) a

RMS Errors(μm)

xðuÞ �0:3 0.305 (10.2%) 0.63yðvÞ �0:3 0.253 (8.5%) 0.52zðwÞ �1:0 0.996 (33.4%) 2.05aRatio of RMS velocity fluctuation to the mean u-velocity

component (2:98mm=s).

Fig. 10. Mean streamwise velocity profiles and RMS errors in the(a) x–y plane and the (b) x–z plane.

Fig. 11. (Color online) Trajectories of two RBCs tracked from 60cinematographic holograms.


volumetric recording, and numerical reconstructionare demonstrated with RBC samples. Measurementaccuracy of the established DHM in the directionparallel to the hologram plane and in the depthwisedirection are �0:3 and �1 μm, respectively. The mea-surement errors in the depthwise direction arechecked by traversing a planar test target axially.By adopting an image sharpness quantificationmethod, the measurement error in the depthwise di-rection is decreased. The squared Laplacian method,which shows the minimum depth of focus, providesthe minimum depthwise error. Measurement uncer-tainty of the RBC position in the depth direction isabout 8 times larger than that in the in-plane direc-tion. In addition, the uncertainty is increased as thedistance from the focal plane increases.Furthermore, we perform a 3D volumetric velocity

field measurement of RBCs in a circular microtubewith the established DHM system. The volume mea-sured in this study is 600 μm × 350 μm × 350 μm, withthe latter measurement being the depth. Consecu-tive hologram images are reconstructed and tempor-al variation of 3D positions of RBCs is obtained. Byadopting a PTV algorithm, the 3D velocity field ofRBCs is measured. In the uncertainty analysis, mea-surement uncertainty of the depthwise direction ve-locity component is about 4 times larger than that ofin-plane velocity components. The decrease of mea-surement uncertainty in the depthwise velocity com-ponent is caused by the discarded vectors that showlarge velocity fluctuations in the PTV procedure. Inaddition, the trajectories of RBCs are obtained infour-dimensional (space and time) coordinates by uti-lizing the cinematographic feature of a high-speedcamera. The adoption of a high-speed camera inDHM has strong potential in analyzing the 3D mo-tion of RBCs. This technique can be used to investi-gate 3D behavior of RBCs in various microchannelsas a fundamental of hemodynamic research.

This work was supported by Creative Research In-itiatives (Diagnosis of Biofluid Flow Phenomena andBiomimetics Research) of the Ministry of Education,Science, and Technology (MEST)/Korea Science andEngineering Foundation (KOSEF).

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