26 INTERNATIONNAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.6, NO.1 2013

Development of High Speed 3D TomographicMicroscope for Non-invasive Monitoring of

Biological Samples

P. Meemon 1,2 ,

ABSTRACT

System design and implementation of a FourierDomain Optical Coherence Tomography (FD-OCT)for microscopic flow monitoring is presented. The sys-tem is capable of capturing flow characteristics under-neath the surface of biological samples at micrometerresolution. The high speed imaging capability allowsfor in vivo 3D mapping of micro-structure of biolog-ical tissues as well as their microvasculature system.An image resolution of 10 microns over 1 mm depthfrom the sample surface and across a 10 mm × 10mm lateral field-of-view is possible. The capability ofthe developed system for monitoring of flow activitywithin the heart of an African frog tadpole is demon-strate. In addition, a progress in development of ahigh speed FD-OCT based on our custom built highspeed spectrometer is presented.

Keywords: Carotid pulse, Push pull effect, Piezo-electric sensor, Tilt table, Biological Engineering

1. INTRODUCTION

To date, optical imaging technology plays an im-portant role in medical diagnostics and treatments.It also has applications in guiding the biopsy andsurgery. The main advantages of optical imaging areits high-resolution high-speed and noninvasive capa-bility. A non-invasive, reliable and affordable costoptical imaging system with the capability of detect-ing early stage of pathology would be a valuable toolto use for screening or detecting pathology. Opticalcoherence tomography (OCT)[1] is an emerging tech-nology that is capable of noninvasive high-speed high-resolution cross-sectional imaging of biological tissues[2]. OCT is based on low-coherence interferometry(LCI) that takes advantage of the short coherencelength of broadband light sources, which is in the or-der of microns, to achieve precise depth sectioning inscattering media. Analogous to ultrasound imaging,OCT illuminates biological sample with broadbandnear infrared light beam and measured the ampli-tude and depth location of the backscattered lightand uses it to construct a cross-sectional image that

Manuscript received on May 28, 2013 ; revised on November10, 2013.1 School of Laser Technology and Photonics, Institute of

Science, Suranaree University of Technology, Thailand, email:panomsak@sut.ac.th2 The Institute of Optics, University of Rochester, Rochester,

NY 14627, USA

reveals structure beneath the sample surface [3]. Todate, OCT has been proven and recognized by physi-cians as a potential tool for medical diagnostics andresearch. Particularly in the field of ophthalmology,OCT has been established for early detection of manyretinal pathologies such as glaucoma, diabetes, andage related macular degeneration [4-6].

Since the invention of the OCT, there are vari-ous implementations of OCT techniques. One tech-nique in particular that push forward the advance-ment of OCT is the Fourier-domain optical coher-ence tomography (FD-OCT) [7]. The fundamentalprinciple of FD-OCT is based on coherence theory inthe frequency domain [8]. FD-OCT captures spectralinterference at the output of an interferometer, e.g.Michelson interferometer, and then Fourier transformto obtain depth-resolved reflectivity profile along theincident beam path beneath the surface of the sam-ple under test. Sequentially, performing 2D scan-ning of the laser beam across the sample’s surface al-lows nondestructive 3D reconstruction of sample mi-crostructure. The main advantage over the time do-main counterpart is that FD-OCT obtained the wholedepth profile at once without scanning of the opticalpath length of the reference beam. Hence its imagingspeed is dramatically improved.

Besides structural imaging, OCT is also capableof functional imaging such as bidirectional flow ve-locity mapping. Analogous to the flow measurementtechnique in Doppler ultrasonography, Doppler OCT(DOCT) is capable of in vivo detection of flow ac-tivity embedded beneath the surface of a fairly thickbiological sample in high resolution and wide velocitydynamic range. DOCT allows visualization of tissuestructure and blood flow activity that provides impor-tant information for clinical diagnostics. For exam-ple, vessel flow property is an early indicator of manyretinal pathologies such as glaucoma, diabetes, andage related macular degeneration [6]. Moreover, de-tailed knowledge of in vivo blood flow under the skinsurface is useful for burn-depth determination andport wine stains treatment [9]. Combining Dopplerdetection with FD-OCT enables imaging of micro-scopic flow, such as in vivo blood flow in capillarynetwork, at high speed, which is particularly usefulfor real time flow monitoring purpose [10-12].

P. Meemon 27

Fig.1:: System layout.

2. MATHEMATICAL DESCRIPTION OFFD-OCT

FD-OCT is built on a concept of the detection ofinterference pattern of low-coherence light beam inspectral domain. A commonly used detection sys-tem is a high resolution and high speed spectrometer.Considering a system as shown in Fig. 1 for instance,a complex spectral electric field in the reference armcan be expressed as

ER(k) = KRE0(k)rR exp(iklR) (1)

where the caret denotes a function in the frequencydomain, k = 2π

λ is the wave propagation number,

E0(k) represents the spectral electric field emittedfrom the light source, KR is a real number represent-ing total losses in the reference path, lR is a round-trip optical path length along the reference arm, andrR is the reflectivity of the reference reflector [13].

In the sample arm of the system, the spectral elec-tric field is a collection of many backscattering eventshappening at various depths of the sample that canbe modeled as

ES(k) = KSE0(k)

∫ +∞

−∞r(lS) exp(iklS)dlS (2)

where KS is a real number representing total lossesin the sample path, lS is a round-trip optical pathlength along the sample arm, and rS(lS) representsthe sample reflectivity profile along the depth as afunction of lS [14]. Therefore, the spectral intensityas detected at the output of an interferometer is asuperposition of the two signals that can be expressed

Fig.2:: Series of en face images of an African frogtadpole reconstructed from a 3D OCT dataset at ev-ery 100 µm depth locations from the sample surface.

as

ID(k) =∣∣∣ER(k) + ES(k)

∣∣∣2=

∣∣∣E0(k)∣∣∣2 ·

|KRrR|2

+2KRKSrR∫∞−∞ rS(ls)cos(k(lS − lR))dlS

+∣∣∣KS

∫∞−∞ rS(lS)e

iklSdlS

∣∣∣2 (3)

The 1st term is regarded as a DC-signal that canbe removed through the direct subtraction method.The 3rd term is an autocorrelation, which is regardedas noise. Nevertheless, when imaging most biologi-cal samples, the backscattering signal from the sam-ple is usually much smaller than the reference signal(i.e. rS ≪ rR) and hence the autocorrelation termis negligible. By defining the optical path lengthdifference lD = lS − lR, the overall constant factorK = 2KRKSrR, and ignoring the DC and auto-correlation terms (1st and 3rd terms), the spectralinterference signal can be reduced as

Iint(k) = K · S(k) ·∫ ∞

−∞rS(lD)cos(klD)dlD (4)

where S(k) =∣∣∣E0(k)

∣∣∣2 represents the power spectral

density of the light source and rS(lD) is the samplereflectivity profile as a function of the optical pathlength difference. Equ. (4) represents the spectralinterference pattern as detected by the spectrometer.Without losing any general description, Equ. (4) can

28 INTERNATIONNAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.6, NO.1 2013

Fig.3:: Doppler color map of flow activity inside theheart chamber of an African frog tadpole superim-posed with the structural map of the sample. Theflow velocity corresponds with different color is des-ignated by the color bar.

Fig.4:: Graphic User Interface of the M-modeDoppler shows measurement of flow profile as a func-tion of time, which can be served as optical cardio-gram.

be rewritten in a complex form as

Iint(k) = K · S(k) ·[∫ ∞

−∞rS(lD) exp(iklD)dlD

](5)

Equ. (5) is now in the form of the Fourier transfor-mation and can be written as

Iint(k) = K · S(k) · [ℑ{rS(ID)}]. (6)

Consequently, the inverse Fourier transform ofEqu. (6) yields OCT signal as

IOCT (ID) = K · ℑ−1{S(k)} ⋆ rS(lD). (7)

Equ. (7) states that the sample reflectivity profilealong depth, i.e. rS(lD), can be reconstructed by theinverse Fourier transform of the measured spectralinterference signal. The term ℑ{S(k)} is known asthe temporal coherence of the light source [8], whichalso serves as an axial point spread function of theFD-OCT system [3]. Combining this concept withthe lateral scanning, 2D and 3D OCT images can beconstructed.

3. SYSTEM AND METHOD

An OCT system used for collecting data in ourearly development of OCT-based flow imaging is aswept-source based FD-OCT system that is customdesigned and built at the Optical Diagnostics and Ap-plications Laboratory (ODALAb) at the Institute ofOptics, University of Rochester [10, 11, 13, 15]. Thesystem is built on a fiber-based Mach-Zehnder inter-ferometer as shown in Fig. 1 [11, 16]. Light fromthe laser is split by a 80/20 fiber coupler and thendelivered to a sample and reference arms of the in-terferometer. Light in the sample arm is focused intoa sample through the objective lens. Backscatteredlight from the sample is then collected and recom-bined with light from the reference arm at the 50/50coupler.

The 3D scanning scheme is implemented using adual axis galvanometer beam steering (VM500, GSILumonics). The spectral interference at the outputof the interferometer is recorded while scanning thesample beam across the 2D surface of the sample.The captured interference signal is then streamed tothe computer memory for processing. The data pro-cessing involved signal pretreatment and then Fouriertransform to obtain depth profile, representing sam-ple microstructure along the beam path. From thecapturing FD-OCT dataset, flow information, suchas location, velocity, direction, and profile, can be ex-tracted through the detection of Doppler phase shiftof two interference signals obtained at the same loca-tion [10, 11].

Two modes of operation of Doppler imaging arenormally performed in DOCT. One is a brightnessmode or B-Mode Doppler, in which multiple axialscans (A-scans) are collected while performing a lat-eral scan (B-scan). An intensity map generated in

P. Meemon 29

Fig.5:: The system layout of the new spectrometer-based FD-OCT at Suranaree University of Technology.

Fig.6:: 3D images of a cucumber section taken by thedeveloped spectrometer-based FD-OCT at its currentstage. (a) Volumetric rendering of the 3D datasetobtained with the system. (b) An example of en facereconstruction from the same 3D dataset, showingcellular level structure of the sample.

B-mode represents the cross-sectional image of thesample structure. Corresponding to the structuralmap, the magnitude of the local phase shift is rep-resented in 2D color mapping. Therefore, B-ModeDoppler is useful for locating the flow location insidethe mainly static structure.

The other is amotion mode or M-Mode Doppler, inwhich multiple A-scans are collected at a fixed posi-tion of the sample beam. M-Mode Doppler generatesa 2D map of Doppler signal, in which one axis is adepth profile and the other axis represents the timeevolution of the flow. M-Mode Doppler is useful whenthe location of the flow is known, and one wants tomonitor the flow changes as a function of time.

4. RESULTS

The results demonstrated in this paper areprogress results over the past three years of the devel-opment of OCT-based flow detection system at theODALab at the University of Rochester. The firstdataset demonstrates the capability of the system tooptically and noninvasively perform depth-sectioningthe sample at microns resolution. With the devel-oped FD-OCT system, we acquired a 3D dataset ofan African frog tadpole over a lateral scanning field-of-view (FOV) of 4 mm × 2 mm. The imaging FOValong the depth is about 1 mm. From the 3D OCTdataset, a series of en face images of the sample wasreconstructed as shown in Fig. 2(a-h). Each en faceimage represents sample structure analogous to thatobserved under a light microscope. However, unlikea conventional microscope, OCT is capable of virtualdepth-sectioning of living sample, i.e. microscopy-like image at different depth of up to 2 mm from the

30 INTERNATIONNAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.6, NO.1 2013

sample surface, nondestructively.The second dataset demonstrates the performance

of the system in detection of microscopic flow infor-mation. In FD-OCT, the phase information is imme-diately obtained after the Fourier transform, allowingthe ease of determination of the amount of Dopplerphase shift. One commonly used algorithm is thetechnique of modified Kasai autocorrelation [11, 17].

Fig. 3(a-c) shows Doppler signal, representing flowactivity at different states of the contraction of theheart chamber of an African frog tadpole capturedby our developed method [11]. The Doppler phaseshift was displayed in colors map, ranging from -2.7mm/s to +2.7 mm/s, as designated by the color bar.The plus and minus signs represent flow in oppositedirection, and hence red and blue regions in Fig.3(a-c) represent flow activity in opposite directions (i.e.inflow and outflow). It should be noted that, withhigh speed imaging capability, the system is capableof real time acquisition and display of flow activity atcurrently about 3-4 frame/s.

Moreover, in a similar manner with ultrasoundDoppler, M-mode Doppler OCT was performed byfixing the lateral position of the sample beam asshown in Fig. 4(a) and acquiring multiple depth scansover time and then computing the Doppler phase shiftbetween consecutive scans as shown in Fig. 4(b).From the M-mode Doppler map in Fig. 4(b), a flowprofile as a function of depth (Fig. 4c) and a flowprofile as a function of time (Fig. 4d) were extracted.The flow profile as a function of time as shown in Fig.4(d) can be used as optical cardiogram for monitoringflow activity of in vivo biological samples.

5. DEVELOPMENT OF NEW FD-OCTSYSTEM

As part of a plan to improve Doppler flow imag-ing using FD-OCT, particularly for real time flowmonitoring purpose, we are developing a new FD-OCT system at Suranaree University of Technology.The new system is a spectrometer-based FD-OCTas shown in Fig. 5. The light source is a superluminescent diode (SLD) that emits a broad spec-tral light, expanding from 800 900 nm output wave-length. An interferometer is a fiber-based Michel-son interferometer with 50/50 split ratio. The de-tector is a custom built spectrometer that was de-signed and built in our laboratory, utilizing a highspeed CMOS line camera with data capturing speedof over 70,000 lines/second. A 3D data acquisition isachieved by a dual mirror galvanometer beam steer-ing, which is synchronized with data capturing fromthe CMOS camera through a PCI express interfacehigh speed frame grabber device. The overall imag-ing speed is currently 20 frame/second for a framesize of 500 spectra/frame (i.e. about 10,000 spec-tra/second). The operated speed is currently limitedby hardware synchronization, which will be furtheroptimized to achieve maximum speed as provided bythe camera. Both lateral and axial resolutions of the

system are currently about 20 microns. A scanningfield of view is up to 10 mm × 10 mm and about 2mm imaging depth.

Fig. 6 shows an early result on 3D microscopicimaging of biological sample, which is a cucumbersection, using the developed spectrometer-based FD-OCT. An imaging FOV was about 10 mm × 10 mm,consisting 500 × 500 depth scans. Even though,the new system is currently operated at 80% of itsfull performance, the microscopic structure at cellu-lar level is readily observed. Further improvementin term of imaging speed and resolution is under in-vestigation. Fig. 6(a) shows volumetric rendering ofthe 3D dataset acquired by the new system, demon-strating its capability for 3D visualization at micro-scopic level. Fig. 6(b) is an en face reconstructionfrom the same 3D dataset, showing structural infor-mation at certain depth beneath the sample surfacewithout actual sectioning. The en face reconstructionprovides an image similar to that can be obtained bya confocal microscope. However, with high resolu-tion depth sectioning capability of OCT, multiple enface reconstructions at different depths can be digi-tally obtained at the depth resolution of less than 10microns.

6. SUMMARY

OCT technology, particularly FD-OCT, has beenproven to be a useful tool for not only in vivo visu-alization of microstructure of biological sample butalso for in vivo monitoring of flow activity within thesample. Flow activity serves as valuable informationin diagnosis of the functionality and abnormality ofin vivo biological tissue since most pathological de-velopment is related to the change in blood circu-lation system. The technique demonstrated here isonly one of the methods of OCT-based flow imag-ing techniques. Over the past several years, thereare many techniques have been developed. Combin-ing of multiple technique will allow for faster, moresensitive, and more precise detection of flow activityin living tissue, enabling a path for early diagnosisof many pathological development. In addition, wereported the progress of the development of our newspectrometer-based FD-OCT system. The develop-ing system has potential for high speed Doppler flowimaging that will be particularly useful for applica-tion that requires real time monitoring of blood flow,such as monitoring a circulation system in biologicalsamples.

Most typical commercial OCT FD-OCT systemsare currently operated at imaging speed of 20 30frames per second, which is still far from ideal fora snap-shot of 3D imaging. In this work, the pro-posed FD-OCT was designed to be capable of highresolution 3D imaging at high speed data acquisitionof up to 200 frames per second, enabling by a highspeed and high throughput line-scan CMOS sensortechnology. Nevertheless, the first implemented pro-totype is currently operated at acquisition speed of

P. Meemon 31

about 20 frames per second for up to 1000 depthscans per frame, which is limited by the triggeringspeed of the scanning waveform generator for drivingthe 3D scanning unit at the sample arm of the pro-totype. This speed will be improved in the future byusing a higher performance device for the waveformgenerator to control the 3D sample scanning. At itsmaximum potential speed, the prototype is expectedbe able to capture a single 3D dataset within about3 seconds.

7. ACKNOWLEDGEMENT

The development of the new high-speed spectrometer-based FD-OCT is supported by Suranaree Universityof Technology and the Higher Education ResearchPromotion and National Research University Projectof Thailand, Office of the Higher Education Com-mission. The development of the swept-source basedFD-OCT and the preliminary experiment on Dopplerflow imaging was performed at the Optical Diagnos-tics & Applications laboratory (ODAlab) at the in-stitute of Optics, University of Rochester under thefunding support of the NYSTAR Foundation (USA).

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