Three-dimensional quantitative imaging of retinal and choroidal blood flow velocity using joint...

Three-dimensional quantitative imaging

of retinal and choroidal blood flow velocity using

joint Spectral and Time domain Optical

Coherence Tomography

Anna Szkulmowska, Maciej Szkulmowski, Daniel Szlag,

Andrzej Kowalczyk, Maciej Wojtkowski*

Institute of Physics, Nicolaus Copernicus University, ul. Grudziadzka 5, PL87-100 Torun, Poland

*[email protected]

Abstract: Recently, joint Spectral and Time domain Optical Coherence

Tomography (joint STdOCT) has been proposed to measure ocular blood

flow velocity. Limitations of CCD technology allowed only for two-

dimensional imaging at that time. In this paper we demonstrate fast three-

dimensional STdOCT based on ultrahigh speed CMOS camera. Proposed

method is straightforward, fully automatic and does not require any

advanced image processing techniques. Three-dimensional distributions of

axial velocity components of the blood in human eye vasculature are

presented: in retinal and, for the first time, in choroidal layer. Different

factors that affect quality of velocity images are discussed. Additionally, the

quantitative measurement allows to observe a new interesting optical

phenomenon – random Doppler shift in OCT signals that forms a vascular

pattern at the depth of sclera.

©2009 Optical Society of America

OCIS codes: (170.4500) Optical coherence tomography; (170.3880) Medical and biological

imaging; (170.4470) Ophthalmology.

References and links

1. M. Wojtkowski, R. Leitgeb, A. Kowalczyk, T. Bajraszewski, and A. F. Fercher, “In vivo human retinal imaging

by Fourier domain optical coherence tomography,” J. Biomed. Opt. 7(3), 457–463 (2002).

2. N. Nassif, B. Cense, B. H. Park, S. H. Yun, T. C. Chen, B. E. Bouma, G. J. Tearney, and J. F. de Boer, “In vivo

human retinal imaging by ultrahigh-speed spectral domain optical coherence tomography,” Opt. Lett. 29(5), 480–

482 (2004).

3. B. J. Kaluzny, B. J. Kaluzy, J. J. Kałuzny, A. Szkulmowska, I. Gorczyńska, M. Szkulmowski, T. Bajraszewski,

M. Wojtkowski, and P. Targowski, “Spectral optical coherence tomography: a novel technique for cornea

imaging,” Cornea 25(8), 960–965 (2006).

4. W. Drexler, H. Sattmann, B. Hermann, T. H. Ko, M. Stur, A. Unterhuber, C. Scholda, O. Findl, M. Wirtitsch, J.

G. Fujimoto, and A. F. Fercher, “Enhanced visualization of macular pathology with the use of ultrahigh-

resolution optical coherence tomography,” Arch. Ophthalmol. 121(5), 695–706 (2003).

5. R. Leitgeb, M. Wojtkowski, A. Kowalczyk, C. K. Hitzenberger, M. Sticker, and A. F. Fercher, “Spectral

measurement of absorption by spectroscopic frequency-domain optical coherence tomography,” Opt. Lett.

25(11), 820–822 (2000).

6. E. Götzinger, M. Pircher, and C. K. Hitzenberger, “High speed spectral domain polarization sensitive optical

coherence tomography of the human retina,” Opt. Express 13(25), 10217–10229 (2005).

7. R. A. Leitgeb, L. Schmetterer, W. Drexler, A. F. Fercher, R. J. Zawadzki, and T. Bajraszewski, “Real-time

assessment of retinal blood flow with ultrafast acquisition by color Doppler Fourier domain optical coherence

tomography,” Opt. Express 11(23), 3116–3121 (2003).

8. V. J. Srinivasan, M. Wojtkowski, J. G. Fujimoto, and J. S. Duker, “In vivo measurement of retinal physiology

with high-speed ultrahigh-resolution optical coherence tomography,” Opt. Lett. 31(15), 2308–2310 (2006).

9. B. White, M. Pierce, N. Nassif, B. Cense, B. Park, G. Tearney, B. Bouma, T. Chen, and J. de Boer, “In vivo

dynamic human retinal blood flow imaging using ultra-high-speed spectral domain optical coherence

tomography,” Opt. Express 11(25), 3490–3497 (2003).

10. S. Makita, Y. Hong, M. Yamanari, T. Yatagai, and Y. Yasuno, “Optical coherence angiography,” Opt. Express

14(17), 7821–7840 (2006).

#109957 - $15.00 USD Received 10 Apr 2009; revised 22 May 2009; accepted 5 Jun 2009; published 9 Jun 2009

(C) 2009 OSA 22 June 2009 / Vol. 17, No. 13 / OPTICS EXPRESS 10584

11. Y. Hong, S. Makita, M. Yamanari, M. Miura, S. Kim, T. Yatagai, and Y. Yasuno, “Three-dimensional

visualization of choroidal vessels by using standard and ultra-high resolution scattering optical coherence

angiography,” Opt. Express 15(12), 7538–7550 (2007).

12. L. An, and R. K. Wang, “In vivo volumetric imaging of vascular perfusion within human retina and choroids

with optical micro-angiography,” Opt. Express 16(15), 11438–11452 (2008).

13. A. Bachmann, M. Villiger, C. Blatter, T. Lasser, and R. Leitgeb, “Resonant Doppler flow imaging and optical

vivisection of retinal blood vessels,” Opt. Express 15, 408–422 (2007).

14. Y. K. Tao, K. M. Kennedy, and J. A. Izatt, “Velocity-resolved 3D retinal microvessel imaging using single-pass

flow imaging spectral domain optical coherence tomography,” Opt. Express 17(5), 4177–4188 (2009).

15. T. Schmoll, C. Kolbitsch, and R. A. Leitgeb, “Ultra-high-speed volumetric tomography of human retinal blood

flow,” Opt. Express 17(5), 4166–4176 (2009).

16. M. Szkulmowski, A. Szkulmowska, T. Bajraszewski, A. Kowalczyk, and M. Wojtkowski, “Flow velocity

estimation using joint Spectral and Time domain Optical Coherence Tomography,” Opt. Express 16(9), 6008–

6025 (2008).

17. M. Wojtkowski, A. Kowalczyk, R. Leitgeb, and A. F. Fercher, “Full range complex spectral optical coherence

tomography technique in eye imaging,” Opt. Lett. 27(16), 1415–1417 (2002).

18. Y. K. Tao, A. M. Davis, and J. A. Izatt, “Single-pass volumetric bidirectional blood flow imaging spectral

domain optical coherence tomography using a modified Hilbert transform,” Opt. Express 16(16), 12350–12361

(2008).

19. T. Bajraszewski, M. Wojtkowski, P. Targowski, M. Szkulmowski, and A. Kowalczyk, “Three-dimensional in

vivo imaging by spectral OCT,” Proc. SPIE 5316, 226–232 (2004).

1. Introduction

Spectral Optical Coherence Tomography (SOCT) also called spectral domain OCT is a

spectrometer-based OCT modality, which enables detecting intensity of interfering light

beams as a function of optical frequency. This method is well developed for cross-sectional

imaging of morphological features of weakly scattering biological tissues [1–3]. It has been

successfully applied as imaging and diagnostic tool in ophthalmology [4]. SOCT has been

also adapted to visualize physiological parameters, since the functional studies might be

important for the early detection and prevention of eye diseases [5–9].

The first attempt to provide an image of 3D vasculature in human retina and choroid was

Optical Coherence Angiography (OCA) proposed by Makita et al. [10]. The non-zero phase

difference between adjacent OCT signal measurements was used to contrast the blood vessels.

Due to a limitation of the measurable phase difference it is difficult to detect the relatively low

or high blood flow velocity as well as flows imaged by strongly attenuated OCT signals. To

overcome these drawbacks an alternative method called Scattering Optical Coherence

Angiography (S-OCA) was proposed to map the choroidal vasculature [11]. Here the light

extinction in blood was used as a contrasting parameter. S-OCA is an en-face method with

intensity threshold-based binarization used to separate a choroidal vasculature. Because the

main parameter used here in the segmentation procedure is the OCT back-reflected intensity, a

low scattering non-vessel region might be misinterpreted as a vasculature region. The 3D

images covering 5mm × 5mm of retinal area were obtained within 5.5s examination. A slight

improvement was achieved by combining S-OCA and Doppler OCA using ultra-high

resolution SOCT instrumentation but still the images of choroid were difficult to interpret

[11].

An and Wang proposed another approach to visualize retinal vasculature in three

dimensions [12]. The method called Optical Micro-AngioGraphy (OMAG) was performed to

map vascular perfusion within human retina and choroid. A constant modulation frequency

was introduced to a standard SOCT measurement to separate the moving and static scattering

components within a sample. In this method, Doppler shifts originating from moving

constituents greater than the introduced modulation frequency can be identified and used to

segment vessels. OMAG provides high quality 3D angiogram covering the area of 2.5mm ×

2.5mm collected during approx. 10s of the examination. However, it needs a long and

complicated data processing.

The first quantitative 3D in vivo measurement of human retinal blood flow was

demonstrated by Bachmann et al. using resonant Doppler FdOCT technique [13]. The method



is based on the effect of interference fringe blurring that occurs when the path difference

between structure and reference changes during camera integration. An electrooptic phase

modulator in the reference arm was driven with specific phase cycles locked to the Doppler

frequency of the sample flow. For this reason the signals of blood flow were enhanced,

whereas the signals of static structures were suppressed. The 3D image of the retinal blood

flow (covering 2.4mm × 1.7mm) is measured within ~10s of examination time. Resonant

Doppler FdOCT requires complex optical setup and precise synchronization, however it

operates without elaborate signal processing algorithm (computational time <5min).

Recently, Tao et al. has demonstrated velocity-resolved blood flow imaging technique

called single-pass flow imaging spectral domain OCT (SPFI-SDOCT) [14]. They use a

modified Hilbert transform and spatial frequency analysis to obtain a stack of depth-resolved

images, each representing a finite velocity range. In this way a velocity distribution in depth is

reconstructed. Unfortunately, in case of 3D imaging of the retina the examination takes 25s,

what makes this method rather inconvenient both for the patient and operator.

The latest volumetric tomography of human retinal blood flow was demonstrated by

Schmoll et al. [15].They applied novel CMOS detector and achieved high speed 3D Doppler

FdOCT. Measured velocity is proportional to the phase difference between adjacent OCT

signals. In general, phase measurements are more vulnerable to low signal-to-noise ratio

(SNR) than intensity-based methods thus they are considered as lower performance technique

[12].

In this paper we would like to propose an alternative way of quantitative 3D blood flow

velocity imaging using joint Spectral and Time domain OCT (STdOCT). We report a fully

automated, straightforward and time effective, intensity-based method of extracting

volumetric maps of a human retinal and choroidal blood circulation. We discuss limitations

and advantages of this method and demonstrate human retinal and choroidal images of

vasculature, obtained during examination lasting less than 3 second and processed within 3

minutes.

2. Theory

2.1 Joint Spectral and Time Domain OCT

The method of joint Spectral and Time domain OCT has been described in details elsewhere

[16]. Therefore, we will here only summarize briefly the STdOCT method and focus on the

procedure of 3D velocity imaging.

One of the main advantages of STdOCT is the fact that it does not need a significant

hardware changes in the standard SOCT setup; instead it requires a high density scanning. In

this technique a set of collected spectral fringe signals is arranged into subsets, each consisting

of M consecutive signals. Each subset is regarded as M repetitions of standard SOCT

measurement performed at the same lateral position and creates a 2D interferogram registered

both in wavenumber k and in time space t (Fig. 1(a)). Spectral fringe signals undergo a

standard SOCT preprocessing consisting of background removal, rescaling to wavenumber k

domain and numerical dispersion correction.

Fourier transformation converts the spectra from optical wavenumber domain k into the

depth position z (Fig. 1(b)) and from the time domain t into the Doppler frequency domain

ω (Fig. 1(c)). In order to increase the sampling density in the Doppler frequency domain we

used additional zero-padding in the time domain. Both structural and velocity tomograms are

created from signal in z ω− -plane. For each, in depth position z, the signal with maximal

amplitude Imax is found. This amplitude defines one point of structural A-scan, while its

position along the frequency axis ω(Imax) forms one point of velocity A-scan. As a result all M

spectra are used in the process of creation of both structural and velocity A-scan. Applying

both Fourier transformations, one after another, 2D spectral fringes ( k t− plane) are

converted to the Doppler frequency distribution in depth ( z ω− plane, Fig. 1(d)). All images



obtained by Fourier transformation (Fig. 1(b-d)) are accompanied by their mirror images due

to the fact that registered interferogram is a real-valued function [17]. In STdOCT as well as

in standard SOCT the complex conjugation of the image is considered unwanted, thus not

displayed in the final cross-sectional images. Therefore, in the practical applications only

positive depths are displayed. The sign of the Doppler frequency value indicates forward or

backward direction of the flow.

If any structure within the sample moves with a velocity V at an angle α to the probing

beam, an axial component of the velocity value equals cosv V α= and causes the Doppler

frequency 2vkω = due to the movement.

Fig. 1. Joint Spectral and Time domain OCT analysis of Intralipid flow in a glass capillary

performed in one position of the probing beam; individual images are linked via one- (1D) and

two-dimensional (2D) Fourier transformations (FT). (a) 2D interferogram consisting of M = 40

spectra recorded in time increments ∆t = 40µs. (b) M = 40 reconstructions of the axial structure

of the glass capillary (A-scans). (c) Retrieval of Doppler shift for each k. (d) Doppler shift

distribution in depth. The flow is too high, axial velocity exceeds the velocity range and signal

aliasing can be observed. (e) Single line in final structural tomogram - plot of maximal signal

amplitude as a function of depth. (f) Velocity profile - plot of the points with maximal signal

amplitude; displayed as intensity graph compose a single line in velocity map.

In order to extract information about flow velocities we have to perform additional two

steps. The first step is based on obvious statement that the structural point can be associated

only with one velocity. Therefore, to get a point on v-z diagram (Fig. 1(f)), the pixel of largest

intensity on each line corresponding to depth position z was chosen from the ω-z diagram. In

the second step we apply a signal intensity threshold to suppress the random velocity noise.

The threshold is constant (κ = 3) and chosen on the basis of SNR analysis performed

previously [16]. A suppression of the velocity noise is necessary for 3-D visualization,

however for 2-D cross-sectional velocity maps is unnecessary. In the case of significant drop

in intensity due to the signal washout the threshold can even affect the image negatively by

removing points with high velocity but low intensity from the center of a vessel.

The maximal quantifiable values of bidirectional flow axial velocity max

v±

is given by the

time interval t∆ between consecutive measurements of the spectral fringes, Eq. (1):

max

2v

k t

π±

= ±∆

. (1)

Velocity resolution depends on spatial oversampling as it was demonstrated by Tao et al.

[18]. To avoid loss of lateral resolution in the velocity-resolved vessel maps, the spatial range

of constructed subsets of A-scans should not exceed the scanning beam spot size.



2.2 Three-dimensional velocity imaging and segmentation of retinal and choroidal

vasculature

In STdOCT each set of M spectra generates velocity profile along the direction of the probing

beam. If the total B-scan is collected, both structural cross section and velocity map can be

derived. In order to get a 3D distribution of flows a number of A-scans in the raster pattern

has to be collected. 3-D structural image is displayed as a semi-transparent cloud of points in

false color scale: blue indicates the lowest signal intensity, green the medium one and the

highest signals are encoded by yellow color. Velocity values are encoded by two colors

indicating direction of blood flow: blue for maximal positive velocity value + vmax and red

color for negative one -vmax. Zero value is encoded by white color, thus colors’ saturation

corresponds to the value of velocity. The process of 3D flow imaging is preceded by motion

artifacts compensation due to involuntary eye movements. Axial displacements between

adjacent A-lines are called bulk motion artifacts.

The bulk motion artifacts within single B-scan are compensated by histogram-based

method analogue to that used in the phase sensitive technique presented by Makita, et al. [10].

However, in our case, it operates on Doppler frequency shifts recalculated to velocity values

(Fig. 2(a)) instead of phase changes. The first step in our algorithm for the bulk motion

correction is applying an additional intensity threshold, which has been mentioned above. We

assume here that the motion of the entire structure as an additive process gives much stronger

effect than the blood flow. Another assumption is that signals coming from vessels always

usually give smaller back reflected intensity than static structure due to interference fringe

wash-out.. After eliminating signals, which are below the intensity threshold, the remaining

velocity values correspond mainly to the static tissue (Fig. 2(b)). These velocity values can be

plotted as a histogram. The number of bins is determined by a number of data points after

Fourier transformation. The velocity of the bulk motion vbulk is the maximum count in the

histogram (Fig. 2(c)). Now it is possible to introduce an offset to the velocity profile

corresponding to the value indicated by the histogram. This procedure enables centering the

true zero velocity value and correcting the velocity profile by using a circular shift (Fig. 2(d)).

Although the whole procedure can be performed automatically with the default threshold, it is

also possible to change the threshold value manually. It is useful when signal aliasing occurs

and procedure with default value cannot operate properly.

Fig. 2. Pictorial representation of the bulk motion correction algorithm. (a) Raw velocity profile

with bulk motion artifact, complex conjugation of the image is marked by the gray background

and not considered. (b) Velocity profile plotted for signals that exceed a certain intensity

threshold. (c) Histogram of velocity values corresponding to (b). (d) Corrected velocity profile.

Bulk motion artifacts between neighboring B-scans are eliminated by a cross-correlation

method. Consecutive cross-sectional images are aligned with respect to the previous one and a

shift value is calculated from the position of the maximum of the cross-correlation function

between these images [19].

Except the above mentioned alignments, none of the images presented in this paper

undergo any fitting, filtering, smoothing, edge detection, manual segmentation or any other



advanced image processing techniques to obtain full axial velocity information and to segment

out the blood vessels.

Apart from the standard structural tomogram (Fig. 3(a)) and a velocity map (Fig. 3(b)),

additional image of segmented vessels can be obtained (Fig. 3(c)). The latter can be done by

another simple modification of existing numerical procedure. If we assume that all locations

with velocity values above a certain threshold correspond to ocular vasculature, a structural

image of moving components can be recognized as segmented vessels. To obtain such image,

one can filter out the static components from the structural image. First a binary mask is

created from the velocity map; a point of the mask is set to 1 for velocity higher than arbitrary

value | ± 0.2 vmax|. Otherwise the pixel value is set to 0. The mask image is applied to the

structural tomogram, thus only moving elements such as blood cells remain (Fig. 3(c)).

Structural images of segmented vessels do not require compensation of bulk motion artifact

between A-scans, thus all potential problems with this procedure can be omitted. The contrast

value in this figure is adjusted to increase the visibility of weak intensities and some details

are better visualized here than in velocity images. The entire segmentation process is

performed automatically. The set of segmented structural tomograms forms a qualitative 3D

image of vessels.

Fig. 3. Segmentation of blood vessels. (a) Standard structural image.(b) Velocity map used in

further segmentation procedure. (c) Structural image of segmented vessels. Lower row –

magnification 8.5 × .

Both the set of 2D cross-sectional velocity maps and data showing segmented blood

vessels are loaded to the commercially available visualization software (Amira, Visage

Imaging, Inc.) and after adjusting color bars a 3D image of blood velocity in ocular

vasculature and a 3D image of segmented vessels are reconstructed.

3. Methods

We use a laboratory high resolution Spectral OCT system comprising a femtosecond laser

(Fusion, Femtolasers, ∆λ = 160nm, repetition rate 70MHz, central wavelength 810 nm), a

fiber Michelson interferometer with fixed reference mirror and a custom designed

spectrometer with a volume phase holographic grating and an achromatic lens focusing

spectrum on a 12-bit CMOS line-scan camera Sprint (spL4096-140k), Basler, (Fig. 4(a)).



Fig. 4. (a) Experimental Spectral OCT system: PC polarization controller, DC dispersion

compensator, NDF neutral density filter, XY galvo-scanners, CMOS line-scan camera. (b)

Exemplary high quality and high resolution (2.3µm in tissue) cross-sectional image of human

retina obtained with the described SOCT setup using high speed CMOS camera.

For all retinal blood flow examination the optical power of light illuminating the cornea

was 750 µW. To meet safety conditions pulses from the femtosecond laser were stretched by

the fiber loop to obtain quasi continuous light illuminating the retina. CMOS camera was set

to acquire 2048 pixels from 4096 available photosensitive elements. During the measurement

2200 A-scans and 100 B-scans were registered within less than 3s (Fig. 5(a)). Axial resolution

was set to 2.3µm and the lateral resolution was equal to scanning beam spot-size on the retina

and assumed to be around 20µm. The velocity recovery is based on 16 spectra, taken with

every 4 A-scans, thus 2200 spectra result in 546 points of velocity map in x-axis (Fig. 5(b)).

The total computational time does not exceed 3 min. assuming zero padding in time domain

up to 128 points.

For images that cover the area of 3mm x 2mm the scanning protocol enables dense

sampling (Fig. 5(c)). Spatial range of constructed subsets of 16 A-scans does not exceed the

scanning beam spot size and there is no loss of lateral resolution in the velocity-resolved

vessel maps. There is only one volume (#1) that was sparsely scanned and does not fulfill the

condition of lossless sampling.

Fig. 5. Scanning protocol (a) 3-D imaging - driving signals for X and Y scanners. (b) The

procedure of generating 2-D velocity map from a single B-scan (c) Two types of sampling

depending on the size of imaged area.

Most of blood flow images were obtained with exposure time equal to 12 µs (repetition

time t∆ = 13.3 µs) that corresponds to the velocity range of ± 15.2 mm/s. Only section 4.3.

presents images taken for some other velocity ranges. Scanning protocols for measurements

with repetition time longer than 30 µs was slightly modified to preserve the short examination



time. In the case of Fig. 11 the number of B-scan was twofold decreased with preserving the

size of imaged area. The reconstruction of vessels network in y-direction was deteriorated,

however the quality in x-direction was maintained. In Fig. 12 the same sampling condition are

preserved for all presented images, however in the case of measurement with repetition time

t∆ = 61.3 µs the size of imaged area was decreased.

4. Results

Three-dimensional images of blood vessels and their velocity distribution were measured at

several locations across the healthy human retina (Fig. 6). Fundus regions of 5 × 5mm and 3 ×

2mm (marked in Fig. 6 as #1 and #2, respectively) were scanned in the location of optic nerve

head. The other volumes were acquired from the 3 × 2mm area located in a close proximity to

the optic disc and in the fovea (Fig. 6, #3 and #4, respectively). These examples are used to

discuss different imaging parameters that affect quality of velocity images.

Fig. 6. Regions of interest in STdOCT measurements of retinal and choroidal blood flow. (a)

Fundus photo with marked areas of STdOCT scanning (white rectangles #1-4), red dashed

rectangle corresponds to the area covered by angiographic image (right). (b) ICG angiography

(TRC-50DX Type LA, Topcon).

4.1 Three-dimensional imaging with detailed velocity information

The upper part of Fig. 7 shows the retinal and choroidal vasculature in the same fundus region

obtained with red free fundus photography, ICG angiography and SOCT fundus view.

STdOCT data are presented in Fig. 7 (d-f) in three different ways as: a rendering of 3D

distribution of blood flow velocity (d), blood flow velocity map in en-face projection (e) and

segmented blood vessels also presented in en-face projection (f). A direction of blood flow is

determined and coded in reference to the direction of light propagation, thus changes of a

vessel orientation in z direction result in a superficial change of blood flow direction. When

blood flow direction becomes perpendicular to the probe beam, a Doppler shift is zero (Fig.

9(b)). In general, knowing the direction of blood flow and vessel orientation, a vein or artery

can be unambiguously recognized. However, this is only true for vessels that can be

continuously tracked starting from the optic nerve head.



Fig. 7. Blood vessels in the region of optic nerve head (#1, 5mm × 5mm, exposure time 12 µs,

maximum value of the axial velocity ± 15.2mm/s, measurement time <3s.). (a) Red-free fundus

photography. (b) ICG angiography. (c) SOCT fundus view. (d) Reconstructed 3D velocity

image overlaid onto structural SOCT data (Media 1). (e) Velocity en-face map created from 3D

STdOCT data. (f) En-face view of segmented vessels. None of presented images require

filtering, smoothing or manual segmentation.

Despite STdOCT method provides distribution of true values of axial velocity component

in three dimensions there are problems with visual assessment of these flows in volumetric

renderings or en-face maps. In the case of color coding the gradient of velocities (usually

parabolic cross-sectional distribution of velocity) causes a fading effect of displayed blood

vessels. Therefore, in order to make the vessels visible and to enhance the contrast we have

brought to saturation the color maps displaying the true axial velocity values in three

dimensional blood flow maps (Fig. 7(d, e)). In order to get insight on velocity distribution

within the vessel it is much better to look at 2D cross-sectional velocity maps (Fig. 8(a)) –

exactly the same which were used to create the 3D image presented in Fig. 7. Choosing the

location of a vessel at the en-face map and decoding velocity values one can obtain velocity

profiles in a desired direction (Fig. 8(b)).



http://www.opticsexpress.org/viewmedia.cfm?URI=oe-17-13-10584-1

Fig. 8. Velocity distribution within vessels. (a) 3D en-face and 2D cross-sectional STdOCT

velocity maps (right panel); 2D map is extracted from 3D image with the exact location marked

at the en-face map (left panel). (b) Velocity profiles in both lateral (x) and axial (z) directions

for vessels visible at presented cross-sectional velocity map.

4.2 Discontinuities in reconstructed images of blood vessels

Above demonstrated volumetric velocity image #1 is rather low sampled (550 × 100 pixels, 5

× 5mm). A single point of the velocity map corresponds to 36µm in x-direction, which is more

than assumed 20µm of lateral resolution. Furthermore, the distance between successive B-

scans is 50µm, so the light beam covers only 40% of imaged area. This low sampling density

leads to discontinuities in y-direction of the image. It is clearly visible in the case of laterally

tilted small vessels (Fig. 9(a)). Another reason for low visibility of reconstructed blood vessels

is their almost perpendicular orientation in respect to the direction of OCT light beam. In this

case the value of the axial component of the velocity is very low. Additionally due to small

variations of the blood vessel topography a direction of the flow often changes generating a

pattern of alternating blue and red patches distributed along the vessel (Fig. 9(c)). Finally the

velocity signal can decay when flow is high and intensity signal vanishes due to fringe wash-

out (Fig. 9(d)).



Fig. 9. Different types of discontinuities of recovered blood vessels (details from Fig. 7(e)). (a)

Discontinuity due to low sampling in y-direction (upper part of Fig. 7(e)). (b) Lack of signal

due to vessel reorientation (in the middle of Fig. 7(e)). (c) Discontinuity due to perpendicular

direction of the vessel in respect to the direction of sampling beam (bottom left hand corner of

Fig. 7(e)). (d) Discontinuity due to a high value of the blood flow velocity exceeding the

measurement range (top right hand corner of Fig. 7(e)).

The influence of the low sampling density into a quality of 3-D velocity maps can be

estimated and the deterioration of 3D images due to low density scanning can be assessed by

comparison with more densely sampled images. As it is shown in Fig. 10 the lateral

oversampling – as it is in the volume #2 (550 × 100pixels, 3 × 2mm) – is sufficient to avoid a

loss of lateral resolution. Successive B-scans do not overlap, however they are taken with

every 20µm to cover the entire scanned area.

Fig. 10. Comparison between densely-sampled volume #2 and sparsely-sampled volume #1,

3mm × 2mm, exposure time 12 µs, axial velocity range ± 15.2mm/s. (a) Movie of 3D

reconstructed velocity image #2 overlaid onto structural OCT data (Media 2). (b) En-face

velocity map obtained from 3D image #2. (c) En-face view of 3D image of segmented vessels

#2. (d) En-face velocity map obtained from cropped 3D image #1. (e) En-face view of

segmented vessels from cropped volume #1. None of the presented images require filtering,

smoothing or manual segmentation.

4.3 Quantifiable velocities

The maximum quantifiable velocity depends on the repetition time between consecutive A-

scans (Eq. (1). In order to measure high velocities, the A-scan rate should be shortened. We

also expect that increasing the maximum velocity value we should broaden the velocity range.

However, this would be true if the smallest detectable velocity depends only on the random

phase fluctuations and do not depend on the repetition time. To verify experimentally these

thoughts we performed two volumetric measurements at the same location but for two

different repetition times of 13.3µs and 36.3µs, corresponding to exposure time of 12 µs and

35 µs, respectively (Fig. 11). Although, the velocity images with the range of ± 5.5 mm/s (Fig.

11(c,e)) are sparsely scanned in y-direction (discontinuous the light beam covers 50% of the

imaged area) they reveal more details. The vessel oriented horizontally in the bottom of Fig.

11(c) is visible almost through the entire volume and while it is only partially visible in Fig.

11(b). Also some capillaries (green arrows, Fig. 11(c)) are only visible when measured with




repetition times of 36.3µs. This observation implies that the minimum detectable velocity is

different for both repetition times. Similar results were obtain by Schmoll et al. [15].

Fig. 11. Comparison between imaging with two different velocity ranges (volume #3). (a) 3D

velocity image overlaid onto structural OCT data with delimitation between sensory retina and

choroid. (b, c) En-face velocity maps corresponding to retina layer for ± 15.2mm/s and ±

5.2mm/s, respectively. Green arrows indicate visible details, asterisks mark points of size

measurement: capillary diameter is ~20 µm, bigger vessel is ~50 µm. (d, e) En-face velocity

maps corresponding to choroid and sclera for ± 15.2mm/s and ± 5.2mm/s, respectively. Note,

that color scale was adjusted separately for each velocity range.

Usually, qualitative measurements provide seemingly better reconstruction of vasculature

than quantitative measurements. In qualitative measurements signal aliasing enhances

visualization of vessels network. However, choosing a proper velocity range is crucial to

quantify the velocity. To present that issue we have performed 3 additional measurements for

3 different velocity ranges ± 24.2 mm/s (the highest possible for presented setup), ± 9.5 mm/s

and ± 3.3 mm/s (the lowest possible to keep the examination time within 4 s); all other

settings remained unchanged (Fig. 12). The chosen ranges correspond to exposure times: 7 µs

(repetition time 8.3 µs), 20 µs (21.3 µs) and 60 µs (61.3 µs).



Fig. 12. Velocity maps obtained from 3-D images for three different velocity ranges. Note that

color scale was adjusted separately for each velocity range. (a, d, g) Velocity maps of sensory

retina. (b, e, h) Velocity maps of choroid and sclera. (c, f, i) Cross-sections of vessels with

velocity profiles indicated by green arrows on velocity maps.

Although velocity maps obtained for ± 3.3 mm/s of velocity range provide details invisible

elsewhere, this measurement is useless to quantify the velocity in two bigger vessels. Even

though all visible vessels are rather small and their diameters do not exceed 100 µm, the range

should be matched to bigger and smaller vessels separately. Otherwise, either small vessels

are distinguished or bigger are distorted.

It has to be noted that the main difficulty with imaging of small capillaries is due to their

almost perfect perpendicular orientation in respect to the scanning beam. This is especially

visible in close proximity to the macula. To have a closer look into this region we performed a

dense STdOCT scan in the macular region (volume #4, 3mm × 2mm, exposure time 12 µs,

axial velocity range ± 15.2mm/s, measurement time <3s, Fig. 13.).

Fig. 13. Reconstructed 3D velocity image at the region of fovea overlaid onto structural OCT

data (Media 3); volume #4, 3mm × 2mm, exposure time 12 µs, axial velocity range ± 15.2

mm/s, measurement time < 3 s.




Beyond the foveal avascular zone in the retina layer a few vessels are visible. The vascular

structure also can be distinguished in the choroidal layer.

5. Discussion and conclusion

Quantitative retinal blood flow imaging raises an issue of providing true values of blood

velocity in the retinal and choroidal vasculature. STdOCT has been already proved as a

sensitive and reliable method for flow velocity estimation [16]. However, similarly to the

phase sensitive techniques it has a finite measurement window of velocities, which can be

reliably found. Because the most of Doppler OCT techniques are able to measure only the

axial component of the velocity the entire span of different blood flow velocities in a real

retina is large. It depends on the sizes of blood vessels, general blood circulation and

additionally on the retinal topography. Therefore, even in a small area (2 × 3mm) it is difficult

to have the measurement range which will cover blood flow velocities in all imaged vessels,

especially if we would like to measure simultaneously both vessels and capillaries.

A closer look at the Fig. 11 reveals that quantitative measurements of blood flow beneath

the retina provides additional random Doppler signals that form well-defined vasculature

pattern. The velocity images of volume #3 reveals that this vascular pattern considered as a

choroidal vessel, surprisingly is observed at the depth of sclera (Fig. 14(d)). Since observed

vessels partially correlate with the vasculature visible in the retina and/or choroid, the origin

of Doppler shift in signal corresponding to the sclera is unclear. It seems that some residual

signals appears beneath the vessel and create “virtual” vasculature in deeper layers. In contrast

to the real vasculature the velocity values in the virtual vessels are random.

Fig. 14. Three-dimensional structural OCT image (volume #3) with indicated three main

layers: retina, choroid and sclera and corresponding velocity maps obtained by STdOCT (a)

Delimitation between sensory retina, choroid and sclera. (b-d) En-face velocity maps

corresponding to retinal, choroidal and scleral layer, respectively.

Signals detected at the depth of sclera originate either from scattering on scleral tissue or

from the multiple scattering in choroid. The question is how a movement that occurs on the



path of light beam affects the optical frequency and phase of penetrating light. This

phenomenon has not been reported yet and requires a systematic study.

In conclusion, we demonstrate the capability of joint Spectral and Time domain Optical

Coherence Tomography (STdOCT) to assess human ocular blood axial velocity in-vivo with

high sensitivity in three dimensions. Straightforward segmentation of vessels is

simultaneously performed with velocity estimation resulting in two sets of 3D data, one

quantitative and the other qualitative. The data are acquired within regular measurement time.

Time requirements of both measurement (3s) and post-processing (3min) renders STdOCT the

fastest three-dimensional OCT technique to image blood vessels and blood velocity in retina

and choroid. For the first time the quantitative method is applied to image choroidal

vasculature.

Additionally, we report an observation of vascular pattern in OCT signals at the depth of

sclera. Further systematic investigation of observed Doppler random signals in light

backscattered from sclera is however beyond the scope of the current study.

We also discuss the question of unambiguous velocity estimation for ocular vessels in

terms of chosen scan density and velocity range.

Future work on this technique should include accurate analysis of the retinal and choroidal

blood flows, which requires extended experiments with additional heartbeat control and

statistical analysis performed for many subjects.

Acknowledgments

Project supported by Ventures Programme co-financed by the EU European Regional

Development Fund and EURYI grant/award funded by the European Heads of Research

Councils (EuroHORCs) together with the European Science Foundation (ESF- EURYI

01/2007PL); both programs are operated within the Foundation for Polish Science. Anna

Szkulmowska and Maciej Szkulmowski acknowledge additional support of Foundation for

Polish Science (scholarships START 2008 and 2009). We would like to acknowledge support

of FEMTOLASERS Produktions GmbH for their support.



Three-dimensional quantitative imaging of retinal and choroidal blood flow velocity using joint...

Documents

Transcript of Three-dimensional quantitative imaging of retinal and choroidal blood flow velocity using joint...