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Speckle variance optical coherence tomography forimaging microcirculation
D. Klawitter, D. Hillmann, M. Pieper, P. Steven, J. Wenzel, and G. Huttmann
Abstract—Non-invasive flow detection and in-vivo measure-ment are one of the most requested clinical applications formedical imaging modalities to investigate vascular pathologiesand responsiveness to drug therapy. Implementing speckle vari-ance calculation & visualization in a market-proven opticalcoherence tomography (OCT) system we demonstrate an an-giography imaging algorithm that provides angle independent,depth-resolved flow measurement up to a capillary scale. Thefeasibility of quantitative flow analysis with speckle variance OCT(svOCT) is to be demonstrated by the evaluation with siliconephantom, animal and human probes, respectively. First resultslead to the assumption that reliable access to quantified flowvelocities with svOCT is not obtainable. However svOCT delivershigh vascular contrast at minimal oversampling rates, being asimple implementation module and a beneficial extension to OCTsystems in general.
I. INTRODUCTION
Optical coherence tomography (OCT) has become one ofthe most promising medical imaging modalities generatingdepth-resolved, high resolution, in-vivo, 3D structural andfunctional data non-invasively over the last two decades. OCTuses coherence gating of backscattered light for tomographicimaging of tissue structure and can be described as a reflectingmode scanning confocal microscope (CM) using a coherentlight source. Imaging contrast is provided by variations intissue scattering due to inhomogeneities in the optical indexof refraction. One of the biggest challenges in exploringnovel clinical applications of OCT is to develop contrastmechanism that generate physiological information in additionto morphological structure. Consequently the ability to detectand quantify flow, if possible in support of 3D or even 4Dvisualization, is one of the most required specification ofoptical imaging modalities for medical research.
In the field of OCT two types of vascular imaging tech-niques have been established, those who visualize quantifiedflow and those that uncover vascular networks but essentiallygenerate no quantitative flow information. Applications of the
A. D. Klawitter, Medizinische Ingenieurwissenschaft, University of Lue-beck; the work has been carried out at the Insitute of Biomedical Optics, Uni-versity of Luebeck, Luebeck, Germany (e-mail: [email protected]
B. D. Hillmann is with Thorlabs HL AG, Luebeck, Germany (e-mail:[email protected])
C. M. Pieper is with Institute of Anatomy, University of Luebeck, Luebeck,Germany (e-mail: [email protected])
D. P. Steven is with Centre of Ophthalmology, University Hospital ofCologne, Cologne, Germany (e-mail: [email protected])
E. J. Wenzel is with Institue of Pharmacology and Toxicology, Universityof Luebeck, Luebeck, Germany (e-mail: [email protected])
F. G. Huttmann is with Institute of Biomedical Optics, University ofLuebeck, Luebeck, Germany (e-mail: [email protected])
first type, as phase-resolved Doppler imaging, measure phaseshifts evoked by moving scatterers in the observed tissue.The second type including speckle contrast (sc) and specklevariance (sv), respectively, use speckle fluctuations to identifymicrovasculature by calculating either the interframe contrastor the intensity variance of structural images [3].
To understand interframe speckle fluctuations a short reviewof laser speckle and its properties is presented. The advent oflaser speckle theory is dated to the invention of laser in the1960’s. When laser light illuminates an irregular surface, thehigh coherence of the light generates a random interference ef-fect. At first the new phenomenon was called ”granularity”, but
Fig. 1: (a) PDF of the intensity ILS of laser speckle with 〈ILS〉as its mean. (b) Photographic image of a white piece of paperilluminated by coherent laser radiation shows fully developedlaser speckle. Scale, ILS/〈ILS〉 [2].
soon renamed by the more popular expression ”speckle” [1].Fig. 1 shows a typical ”speckle pattern” and its probabilitydense function (PDF). The PDF of the intensity ILS oflaser speckle is negative exponential and characterized by amaximum probability of ILS = 0, leading to the well-knownhigh contrast CI = σILS
/〈ILS〉 = 1. Here σILSis the standard
deviation of ILS , 〈ILS〉 is its mean.In the early years of lasers, speckle was mainly depicted as
noise: it strongly affects resolution when laser light is used,much effort was directed towards reducing speckle in imagesformed in laser light. Later, researcher started to study specklefor its own sake and to developed practical applicationsof the effect. As a random phenomenon laser speckle canbe described statistically. Although a detailed view on laserspeckle statistics is beyond the scope of this paper, it is worthpointing out that the scale of the speckle pattern - the size ofthe individual speckles - has in general nothing to do with thestructure of the surface producing it. It is defined only by theaperture of the beam optics used to observe the speckle pattern.If the speckle pattern is being observed by the human eye, itis the pupil of the eye that determines the speckle size. When
an object moves, the generated laser speckle pattern changes,too. Concerning small motions of an object, the speckle movewith the object, i.e. they remain correlated and the intensityvalues behave according to a Gaussian distribution.
A single speckle seems to ”twinkle” like a star. Thisphenomenon is known under the expression ”time-varyingspeckle”. It is frequently obtained when living organisms areobserved under laser light illumination. Probably the mostimportant potential application of speckle fluctuation, firstrecognized by Stern in 1975, arises when they are inducedby the flow of blood, precisely the movement of red bloodcells (RBC) or erythrocytes.
Accordingly svOCT detects microvasculature by calculatingthe interframe intensity variance of structural images, wherecontrast is provided through different time-varying propertiesof fluid versus solid tissue components [4]. In other wordssvOCT dissociates static scattering from dynamic scatteringcomponents. In animal and human tissue the predominantcellular scattering factors are RBC. Thus blood vessels aretypically visualized as highly scattering regions in standardOCT intensity images. Unfortunately interframe speckle vari-ance calculation is strongly influenced by bulk tissue motion(BTM) leading to an artificial increased variance signal. Thismotion artefact can be reduced choosing optimized acquisitionparameters [5]. Here we implement svOCT into a market-proven and commercial available OCT system and show itscapability of simple and computational efficient access to mi-croangiography. Furthermore its performance is evaluated byanimal models, especially investigating the ability to quantifyblood flow with a silicone phantom at different flow velocities.
II. MATERIAL AND METHODS
All measurements were performed with the TelestoSpectral-Domain OCT (SDOCT) imaging system from Thor-labs GmbH, Dachau, Germany. SDOCT is one of two embod-iments known under the overall category of Fourier-DomainOCT (FDOCT). These methods are called ”Fourier-Domain”because they detect the interference spectrum and do notrequire mechanical scanning of the reference path length intime. For this reason a SDOCT system with a broadbandlight source extracts spectral information from the interferencesignal by distributing different optical frequencies onto a high-speed, high resolution spectrometer (detector line-array) usinga dispersive element. Thereby the information of a full depthscan (A-scan) can be measured within a single shot. Specifi-cations of the Telesto imaging system are: center wavelengthλ0 = 1325 nm, axial scan frequency fs = 5, 5/28/91 kHz,axial resolution (air / water) ∆z = 6, 5 µm / 4, 9 µm, lat-eral resolution ∆x, y = 15 µm, optical power on sampleP = 3 mW , pixel per A-scan = 512, maximum imagingdepth d = 2.5 mm and a maximum field of view (FOV)l · w · d = 10 · 10 · 2, 5 mm3.
Speckle variance calculation was performed under use of anexperimental software module based on LabVIEW (NationalInstruments, 2012, 64 bit, Austin, US). After installation ofthe software add-on to the working computer (Dell T5500, 4 x2,13 Ghz, 12 GB DDR3 RAM, 500 GB HDD) and its operating
system (Microsoft Windows 7 x64) the system was capableto process interframe speckle variance images (SVijk) of thestructural OCT intensity (Iijk) based on the following equation
SVijk = σ2I =
1
N
N∑i=1
(Iijk −
1
N
N∑i=1
Iijk
)2
(1)
where the gate length N sets the number of B-scans used inthe variance calculation, and i,j, and k are indices for the B-scan or frame, transverse, and axial pixels. Calculation of theA-scans yields complex OCT signal consisting of the absolutevalue and the phase. For structural intensity images only theabsolute value will be represented on screen. Our softwaremodule allows several options to process speckle variance.Either the original OCT signal as input variable is linear (lin),logarithmized (log) or complex (com). After calculation ofvariance the output can be chosen as linear or logarithmized.Consequently there are six different in- & output variations:lin - lin, lin - log, log - lin, log - log, com - lin and com - log.
Although the software module provides averaging axially(averaging several A-scan-points) and along fast axis (over-sampling of several A-scan-lines) the most sensitive scanningmode to detect microcirculation is along slow axis (oversam-pling of several B-scans). Caused by the slow flow velocities incapillaries of about 0.1 - 0.9 mm/s there must be a time gapbetween following B-scans of some microseconds. B-scansoversampling leads to adequate time gaps and image scalingstays constant. The methods consist of five studies:
1) silicone phantom (variance calculation)2) silicone phantom (flow quantification)3) animal model (mouse ear - microangiography)4) animal model (mouse cranium - microangiography)5) animal model (mouse cornea - microangiography)
For the phantom measurements a polymer tube embedded insilicone phantom with an inner diameter of 280 µm was filledwith Intralipid 10 % (Baxter) to study variance calculation ofdifferent signal input and output visualization . The siliconewas mixed with white nanoparticles to reach a similar level ofscattering. A syringe pump generated various flow velocitiesfrom 0.135 to 8.9 mm/s to verify the potential of flowquantification.
(a) (b)
Fig. 2: (a) Mouse cranium with cranial windows (red cir-cles); (b) mouse eye with red arrow indicating the B-scanof Fig. 6 (a)and red circle representing the C-scan position inFig. 6 (b).
The experiments with animal models consisted of measuringmouse ear, cranium and cornea. All mice had to be sedated.The mouse ear had to be depilated with creme so that thefine hairs could not irritate the image acquisition. To preventthe measurement from BTM the ear was taped on the samplestage. A second mouse with prepared cranial windows wasobtained to uncover the cerebral vessel network, especiallychallenging the question of vasculature imaging through theintact skull (Fig. 2(a)). A third mouse with induced neovas-cular defects, caused by implanted suture into cornea, wasanaesthetized and fixation of the eye lid was attended (Fig.2(b)). Stored into a customized mouse holder with temper-ature controlled bottom and artificial respiration support themeasurements could be accomplished. All measurements wereperformed with a scan frequency of 28.000 Hz.
III. RESULTS AND DISCUSSION
A. Silicone phantom
1) Evaluation of different variance calculation methods:Fig.3 represents a typical B-scan of the silicone phantomwith polymer tube embedded. Here the intralipid solution iscompletely static and Brownian motion is dominant. Timestacks of 100 frames at the same location were acquired andthe region of interest (RIO) was set as circular region insidethe polymer tube (Fig 3 (b)). In Table I we see the overallmean, minimum and maximum at the ROI of all frames forthe six different calculations. In comparison no significantdifference between the calculation variations can be obtained.However the main effect of svOCT can be observed. Thecontrast between static and dynamic scattering components oftissue increases so that fluids and their canalization can clearlybe identified.
(a) (b)
Fig. 3: B-scan of silicone phantom with polymer tube em-bedded (a) mean intensity image and (b) variance image oflin - lin calculation with ROI as red circle (N = 3, FOV =2 · 2.5 mm).
TABLE I: Variance calculation results
in out mean min maxlin lin 3.777 1.555 5.588lin log 3.802 2,010 5.789log lin 3.794 1.401 5.605log log 3.811 1.267 5.621comp lin 3.820 1.549 5.611comp log 3.764 1.721 5.633
Calculating michelson contrast (KM = Imax−Imin/Imax+Imin) between the ROI inside the polymer tube and anotherROI outside at the same depth level reveals KM= 0.033 for anormal intensity image and KM = 0.14 for a variance image.
2) Evaluation of quantified flow measurements: Using thesyringe pump to generate flow rates of 0,1 ,2 ,4 , 5, 10, 15, 20,25, 30 µl/min. Variations of the speckle variance signal wereinvestigated. Analysing the circular ROI (red circle Fig. 3 (b)inside the polymer tube and its lateral progress a dependencyof the variance signal to the velocity of RBC has to be denied.A dependency should be reflected in a dramatic change ofthe maximum variance signal but neither the mean nor themaximum signal is altering (Fig. 4). In this case the results ofBarton 2005 [6] can not be reproduced. She showed a depth-resolved flow profile and increased flow signal due to risingflow velocities with svOCT.
Fig. 4: Minimum, mean and maximum values of specklefluctuations inside the RIO in relation to the flow velocity(see Fig. 3 (b)).
B. Animal model
1) Mouse ear: In Figure 5 (a) we see delimited layers of a
(a) (b)
Fig. 5: Mouse ear in-vivo (a) B-scan of mean intensity and (b)vessel network detected by MIP of variance images (N = 3,FOV = 8 · 8 · 2.5 mm).
we see delimited layers of a mouse ear in a mean intensityB-scan. Large field of view of the Telesto SDOCT allowedto image almost the whole mouse ear without the need tostitch several pictures. MIP of the speckle variance allow touncover the complex vessel network (Fig.5 (b)) and enablesinvestigation of vessel localization, ramification and diameter.
2) Mouse cranium: Is it possible to depict vessel throughthe transparent cranium (≈ 200µm) of a mouse in-vivo withsvOCT when vessels are visible to the naked eye? The answerto this question can be seen in Fig. 6 (b), where vessels canclearly be identified not just inside the cranial windows butalso next to them with intact cranium. This advantage allowsscientist to measure a living subject repeatedly without theneed to open the skull and consequently dispatch the animal.
(a) (b)
Fig. 6: (a) C-scan of mean intensity images showing mousecranium with two cranial windows represented as red cir-cles and (b) MIP of variance C-scans (N = 3, FOV= 5 · 5 · 2.5 mm).
3) Mouse cornea: Neovascular defects in cornea are one ofthe clinical important reasons for corneal opacity. Thereforethe detection and visualization of such growing vessels incornea is a relevant field of medical research. In Figure 7 (a)we see a B-scan of mean intensity through a mouse eye in-vivo (see also Fig. 2(b)). The white line indicates the sectionswhere the MIP of variance images was processed. Invisible forlight microscopy the corneal vessels are disclosed by svOCT(Fig. 7 (b)).
(a) (b)
Fig. 7: Mouse eye (a) B-scan of mean intensity and (b) MIPof several variance C-scans showing corneal vasculature (N =2, FOV = 4 · 4 · 2.5 mm).
IV. CONCLUSIONS
In recapitulation of previous experiments we can statesvOCT with the Telesto SDOCT is a fast imaging technique todetect microcirculation and structural information at the sametime. To uncover vessel networks C-scan or En-Face-scansmust be calculated of variance B-scan stacks. A simple way toattain 2D angiography images is a maximum intensity projec-tion. Experiments with the silicone phantom showed neithera significant dependency of variance to different calculationalgorithms nor to varying flow velocities. With no possibledifferentiation of flow velocities quantification of perfusionis out of reach. We showed the feasible imaging of vesselnetworks in physiological tissue of animal probes in-vivo.Practical clinical application could be the investigation ofcorneal neovascular defects.
Caution must be practised in the analysis and interpretationof OCT angiograms due to possible intensity changes byincreased RBC velocity, increased hematocrit or alternatingRBC orientation. Optimization of svOCT imaging parametersas resolution and frame rate will lead to an improvement incapillary detection. In situations where tissue motion is high,the field of view or frame rate during acquisition must beoptimized to keep interframe displacements to less than thebeam waist radius.
To summarize, svOCT is a highly sensitive, angle inde-pendent, endogenous-contrast vascular imaging technique thatprovides best results in tissue with low BTM. Unlike DOCTimaging svOCT is not able to provide information about theflow orientation. However, svOCT delivers high vascular con-trast at minimal oversampling rates, visualizing flow even atmolecular motion level. Speckle analysis in OCT images couldbe advantageous to DOCT because it is sensitive to motionnormal to the incident beam, eliminating the need for phase-sensitive detection. A combination of svOCT and DOCTcould resolve problems on both sides of optical coherenceangiography. In conclusion, svOCT is an microangiographyimaging method with simple implementation and an advantagefor clinical diagnostic, as well as a beneficial extension forOCT system in general.
REFERENCES
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