2 May 2013 Dual-wavelength photothermal optical coherence tomography for imaging microvasculature blood oxygen saturation
Author Affiliations +
J. of Biomedical Optics, 18(5), 056005 (2013). doi:10.1117/1.JBO.18.5.056005
Abstract
A swept-source dual-wavelength photothermal (DWP) optical coherence tomography (OCT) system is demonstrated for quantitative imaging of microvasculature oxygen saturation. DWP-OCT is capable of recording three-dimensional images of tissue and depth-resolved phase variation in response to photothermal excitation. A 1,064-nm OCT probe and 770-nm and 800-nm photothermal excitation beams are combined in a single-mode optical fiber to measure microvasculature hemoglobin oxygen saturation (SO 2 ) levels in phantom blood vessels with a range of blood flow speeds (0 to 17  mm/s ). A 50-μm-diameter blood vessel phantom is imaged, and SO 2 levels are measured using DWP-OCT and compared with values provided by a commercial oximeter at various blood oxygen concentrations. The influences of blood flow speed and mechanisms of SNR phase degradation on the accuracy of SO 2 measurement are identified and investigated.
Yin, Kuranov, McElroy, Kazmi, Dunn, Duong, and Milner: Dual-wavelength photothermal optical coherence tomography for imaging microvasculature blood oxygen saturation

1.

Introduction

Noninvasive quantitative evaluation of microvasculature hemoglobin oxygen saturation (SO2) in tissue is important in early detection and monitoring the progression of inflammatory and ischemic diseases such as cancer, stroke, and glaucoma.1,2 Various approaches have been used to assess in vivo microvascular oxygen saturation, including oxygen-sensitive microelectrodes,34.5.6.7 magnetic resonance imaging (MRI),89.10.11.12 reflection spectroscopic oximetry.1314.15.16.17.18 and phosphorescence quenching.17,1920.21.22.23.24 Measurement by oxygen-sensitive microelectrodes is a point measurement method and is primarily limited to animal studies. MRI has limited spatial (100 to 150 μm) and temporal (tens of seconds to minutes) resolution. The phosphorescence quenching technique has limited spatial resolution (e.g., 50 μm), and no oxygen sensitive dyes that are approved by the United States Food and Drug Administration are available for clinical translation.

A useful three-dimensional imaging technique, optical coherence tomography (OCT), was introduced in 1991 as a time domain approach25 and was later used as a frequency domain method.26,27 OCT implementation in the frequency domain improves signal-to-noise ratio and allows high-speed image acquisition. More recently, Huber et al. showed that, for an equivalent signal-to-noise ratio, swept-source (SS) OCT with balanced detection provides higher acquisition rates compared with spectrometer-based Fourier domain OCT approaches.28

Biomedical investigators are interested in applying OCT approaches to measure microvasculature SO2 in tissues. Spectroscopic Fourier domain OCT (SFD-OCT)29,30 has been reported to measure depth-resolved microvasculature oxygenation, but an appropriate model has not been given to estimate the attenuation coefficients required to determine blood SO2 levels using OCT light in the near infrared spectral region.31 SFD-OCT has been shown to provide sufficient sensitivity to quantify micro vascular SO2 levels using visible wavelengths (460 to 700 nm) where hemoglobin absorption is relatively large.32,33 However, SFD-OCT using visible wavelength sources is compromised by limited imaging depth, which is restricted by increased scattering.

Photothermal OCT is a functional imaging technique that can measure the optical pathlength variation of OCT light backscattered from tissues in response to an excitation beam. Adler has demonstrated photothermal OCT using a gold nanoparticle contrast agent.34 Skala has developed photothermal OCT for high-resolution molecular imaging,35 and Paranjape has reported using photothermal OCT to detect macrophages in tissue.36 So far, reported applications of photothermal OCT have focused primarily on light absorption by nanoparticles. Previously, we reported using dual-wavelength photothermal OCT (DWP-OCT) to measure microvasculature SO2 in both phantom37 and in vivo38 blood vessels using a common-path phase-sensitive (PhS) OCT system.39 Because we used a common path interferometer, imaging by scanning the beam was difficult, and the results were limited to point microvasculature SO2 measurement. In this study, we report a DWP-OCT system that uses a two-beam interferometer and allows for the imaging and measurement of SO2 levels.

Compared to two-beam interferometers, common path interferometry provides higher sensitivity and stability to measure the phase of interference fringes of light backscattered from transparent and scattering media. Despite these advantages, several drawbacks of prior common-path DWP-OCT system3738.39 design are recognized. Recording en-face images is challenged by incorporating a 2D scanning system into the sample arm. A short working distance associated with common-path DWP-OCT introduces problems for positioning the OCT probe beam at a desired sample measurement location. Also, the short working distance makes intravascular, retinal, and endoscopic applications challenging. We present a DWP-OCT system using a fiber two-beam interferometer to image and measure SO2 levels in a phantom blood vessel.

2.

Methods

2.1.

DWP-OCT System

In this study, a DWP-OCT system using a fiber Michelson interferometer was constructed for imaging and blood SO2 measurement. Interferometric fringe phase stabilization is a critical feature required for SO2 measurement. In a generic phase-sensitive SS OCT system, two mechanisms contribute to phase noise: inconsistency of the start wavelength between successive A-scans, and nonspecific mechanical movement of optical elements in sample and reference arms. To resolve the first issue, 5% of light in the sample arm is coupled to a high-reflectivity mirror, which is sufficient to form a high SNR interference fringe signal with reference light but too weak to introduce an artifactual autocorrelation and interference signal with light backscattering from the sample. Light reflecting from a high-reflectivity mirror in the sample path introduces a feature-line in recorded B-scans positioned below the imaging media and does not compromise image quality. To minimize the second source of phase noise (due to nonspecific mechanical movement of optical elements), the sample beam scanning system is constructed using a stable mechanical cage system.

Blood SO2 measurement value is dependent on the ratio (χ) of optical pathlength (op) signal amplitude at modulation frequencies introduced by 770-nm and 800-nm photothermal excitation light. To balance incident fluence of photothermal excitation beams, the scanning optics is designed to provide nearly equivalent spot sizes for 770-nm and 800-nm light.

The DWP-OCT system (Fig. 1) for imaging and blood SO2 measurement consists of two major systems: a SS PhS OCT system that provides accurate depth-resolved phase measurement with a 300-pm lower bound of detectable op signal amplitude, and two excitation lasers (770 and 800 nm) that are intensity modulated at 400 and 380 Hz, respectively, and introduce a nanometer-scale harmonic op signal amplitude due to blood absorption.

Fig. 1

DWP-OCT system schematic. WDM: wavelength division multiplexer, FBG: fiber Bragg grating, PC: polarization controller, PD: photodetector.

JBO_18_5_056005_f001.png

The phase-sensitive OCT system uses a swept source laser (HSL-1000, Santec Corp. Komaki, Aichi, Japan) with a 28-kHz A-line rate, a center wavelength at 1,060 nm, and full-wave-half-maximum spectral width of 58 nm. Single-mode optical fiber (HI1060, Corning Inc., Corning, NY) is utilized to construct the interferometer. Light emitted by the SS laser is split into three subsystems: trigger, sampling clock, and signal interferometer.

The trigger subsystem utilizes a fiber Bragg grating (FBG) to ensure the digitizer starts data acquisition at a consistent and repeatable wavenumber for each A-scan. The sampling clock subsystem consists of a Mach-Zehnder interferometer with a clock rate set by adjusting the interferometric light delay. The sampling clock signal received by a balanced photodetector is input into an external analog circuit, frequency quadrupled, and used as a sampling trigger for the analog-to-digital converter.40 The third subsystem is the Michelson signal interferometer with sample and reference arms. An optical circulator (1060 PI TGG, Agiltron Inc., Woburn, MA) is used in the sample arm of the Michelson interferometer to increase SNR.41 The sample arm contains two light paths: a path to the phantom blood vessel with an achromatic scanning system (consisting of two galvanometers and an afocal telescope), and a high-reflectivity mirror used for phase stabilization. The achromatic scanning system is designed and simulated in optical design software (Zemax, Radiant Zemax LLC, Redmond, WA) and provides micrometer-scale lateral resolution for imaging three co-aligned beams; the computed diffraction encircled energy computation gives a 13-μm lateral resolution for 770-nm and 800-nm excitation beams, along with a 14-μm resolution for the 1,060-nm PhS-OCT probe beam.

After the interference fringe signal is acquired uniformly in wavenumber (or optical frequency), computing a fast Fourier transform (FFT) of the signal, we obtain a complex number data array for each A-scan. The complex number amplitude is used to construct an OCT intensity image, and the complex number angle is used to determine the phase of the depth-resolved fringe signal. The signal phase of light reflecting from the mirror in the sample path is utilized to correct for any error introduced by delay in data acquisition. Phase errors at any sample depth (ds) are eliminated by subtracting the reference phase scaled by depth from the sample phase42 as

(1)

φsc=φsdsdrφr,
where φsc is the corrected sample phase, φs is the sample phase acquired from the raw signal FFT, φr is the reference phase obtained from interference between light reflected from the mirror in the sample path and the reference arm, and ds and dr are the sample and reference depths, respectively.

The system operates in real-time in either OCT intensity imaging or M-mode phase imaging. Data acquisition and signal processing software are written in Labview (National Instrument Corp., Austin, TX). The system sensitivity is 102 dB (with shot-noise limited sensitivity of 107 dB), and the axial resolution is 13 μm in tissue with the application of a real-time digital dispersion compensationalgorithm.43,44 The axial resolution is limited by polarization mode dispersion in the circulator. After Fourier transform of M-mode phase data (i.e., 1-s duration), with a calibration process, the phase of light backscattered from a selected sample depth is converted to the optical pathlength (op=λ*ϕsc/2π, where λ is the center wavelength and ϕsc is the corrected sample phase). The mean noise level in the signal frequency region corresponding to the intensity modulation of photothermal excitation light (360 to 420 Hz) is taken as the op signal noise floor and measured at 300 pm.

Photothermal excitation beams are emitted from two 100-mW single-mode fiber (HI780, Corning Inc., Corning, NY) pigtailed laser diodes (QFLD-780-100S,QPhotonics LLC, Ann Arbor, MI for 770 nm and QFLD-795-100S for 800 nm). Light from these sources is coupled into the DWP-OCT system’s sample arm through a wavelength division multiplexer (WDM) (PSK-000851, Gould Fiber Optics, Millersville, MD). Both the WDM and the PhS-OCT system are constructed using HI1060 Corning fiber, which is single-mode fiber for 1,060-nm probe light and allows two or three propagation modes at photothermal excitation wavelengths of 770 nm and 800 nm. The temperature of each laser diode is precisely controlled within a fraction of a degree (K) and selected to ensure emission at the desired wavelength as calibrated using a spectrometer. The photothermal excitation power incidents on the sample for 800-nm and 770-nm wavelengths are 2.78 mW and 2.87 mW, respectively; both are within ANSI limits for skin. Intensity modulation frequencies for photothermal excitation light [770 nm (400 Hz) and 800 nm (380 Hz)] are selected in a signal frequency range where the phase noise is low (0.3 nm) and the op signal amplitude is high. The procedure for determining the optimum photothermal excitation frequency to maximize op signal-to-noise ratio for blood was reported previously.38 OCT probe (1,064-nm) and photothermal excitation (770-nm and 800-nm) beams are co-aligned and coincident on the sample.

2.2.

SO2 Calculation

We assume that op signal amplitude due to absorption by blood is linear with the fluence of photothermal excitation light,45 as derived and reported previously.37,38 Neglecting the effect of thermal diffusion, SO2 level can be estimated from op measurement in response to 770-nm (1) and 800-nm (2) photothermal excitation.

(2)

SO2=c0c0+cd=αd1χ12αd2χ12(αo2αd2)(αo1αd1),
where co is the concentration of oxygenated hemoglobin (mM); cd is the concentration of deoxygenated hemoglobin (mM); χ12=(op1/Φ1)/(op2/Φ2); op is the measured optical pathlength signal amplitude; Φ=τI represents the fluence over one period;τ is the period of photothermal excitation; I is the average intensity of excitation light on the blood vessel; αo and αd are the tabulated molar extinction coefficients of oxygenated and deoxygenated hemoglobin (cm1mM1), respectively; and subscripts 1 (770 nm) and 2 (800 nm) correspond to the wavelengths of excitation light (αo1=0.65cm1mM1, αo2=0.79cm1mM1, αd1=1.312cm1mM1, αd2=0.793cm1mM1). The ratio of the two excitation beams’ fluence (Φ2/Φ1) at the sample is calibrated before measurement. The op signal amplitude at each photothermal excitation wavelength (op1 and op2) is determined by computing the magnitude of the signal phase oscillation at the respective modulation frequencies of excitation light (Fig. 2).

Fig. 2

Spectra of op signal amplitude induced by 770-nm (5-nm, 400-Hz) and 800-nm (6-nm, 380-Hz) excitation light.

JBO_18_5_056005_f002.png

2.3.

Blood Vessel Phantom and Blood Flow

A 50-μm inner diameter polytetrafluoroethylene conduit (SUBL 060, Braintree Scientific Inc., Braintree, MA) containing porcine blood is used as a blood vessel phantom. A desired blood SO2 level is achieved by adding sodium dithionite to the blood sample to deoxygenate. Six blood samples are prepared at different SO2 levels (99.6%, 89.2%, 84.1%, 69.0%, 57.3%, and 3.0%). To provide a scattering background for imaging, the phantom blood vessel is placed on a sheet of white-colored copy paper. After imaging, blood SO2 measurements are recorded in an M-mode acquisition at a selected position in the lumen of the phantom vessel (Fig. 3).

Fig. 3

B-scan image of a phantom vessel with a 50-μm inner diameter containing blood positioned on a sheet of white-colored copy paper.

JBO_18_5_056005_f003.png

To investigate the effect of blood flow on SO2 measured by DWP-OCT, a digital syringe pump (AL-1000,World Precision Instruments, Sarasota, FL) is used to introduce blood flow in the phantom vessel at a fixed SO2 level (98.2%) corresponding to an arteriole. At the fixed SO2 level, DWP-OCT SO2 measurements are recorded at blood flow speeds from 0 to 17mm/s. For each blood flow speed, SO2 levels are also measured at the same position in the lumen of the phantom vessel.

3.

Results

We observed op signal amplitude in the phantom vessel containing blood resulting from photothermal excitation with 770-nm and 800-nm light. In a control experiment, with the phantom vessel containing water, no op signal was detected in response to photothermal excitation. Three experiments were completed to investigate the functionality of the DWP-OCT system: en-face imaging of the blood vessel phantom, blood SO2 measurement without flow, and influence of blood flow speed on SO2 measurement.

3.1.

Phantom Image

A two-vessel phantom was constructed to demonstrate DWP-OCT imaging of an arterial-venous vessel pair. Two 50-μm inner-diameter phantom vessels were attached to a sheet of white-colored copy paper to provide a scattering background for imaging. The two phantom vessels were filled with porcine blood, and digital syringe pumps were used to introduce flow (2.8mm/s) in opposite directions in each phantom vessel (Fig. 4). Average flow speed was calculated by dividing the syringe pump infusion flow rate (0, 20, 40, 60, 80, 100, and 120μL/h) by the phantom vessel’s lumen cross-sectional area (1.96×103μm2).

Fig. 4

(a) En-face image of an arterial-venous phantom vessel pair. (b) B-scan image at the indicated site. Arrows in (a) indicate blood flow direction.

JBO_18_5_056005_f004.png

3.2.

Blood SO2 Measurement in Phantom Vessel without Flow

DWP-OCT phase data was recorded over a time period of 1-s at the bottom of the lumen in one of the phantom vessels (Fig. 3). The op signal amplitude was determined for each 0.5-s data acquisition period by computing the fast Fourier transform (FFT) of the phase (φsc) data. For each 1-s of acquired DWP-OCT data, 15 sub-segments were analyzed with a 1/28-s offset between successive 0.5-s data segments. For each 0.5-s data segment, the op signal amplitudes at 380 and 400 Hz were calculated, and the SO2 level was estimated according to Eq. (2). In the experiment, DWP-OCT data segments longer than 1-s were not recorded due to phase drift. Estimates of op were obtained using a moving average approach, which is preferred for short signal durations to reduce high-frequency noise. Phase noise in the op signal amplitude increases variance in the computed SO2 levels (see error propagation model in Sec. 4.1).The mean of SO2 values derived from 15 sub-segments gives a better estimate, and a moving window will smooth the time variation of oxygen saturation. Averaging SO2 values over the sub-segments suppresses the phase noise in the op signal amplitude.

To demonstrate DWP-OCT for blood SO2 measurement, the six blood samples prepared at different SO2 levels were measured (99.6%, 89.2%, 84.1%, 69.0%, 57.3%, and 3.0%) with a commercial blood oximeter (AVOXimeter 1000E, International Technidyne Corp., Edison, NJ). Each blood sample was separated into two volumes to ensure DWP-OCT and oximeter measurements could be carried out simultaneously, thus reducing measurement variation due to differences in reoxygenation. The DWP-OCT measurement time of a single blood sample was shorter than 30 min to minimize the effect of drift in the blood SO2 levels (blood samples were deoxygenated by sodium dithionite).46 The DWP-OCT SO2 measurement results of the blood samples are shown in Fig. 5. Each plot indicates the SO2 level deduced from Eq. (2) and derived from the 15 segments lasting 0.5-s each. The solid line (green) and dashed lines (red and blue) represent the mean and standard deviation, respectively, of the 15 segments’ DWP-OCT SO2 values. The SO2 levels measured by a commercial oximeter are indicated in the right portion of each plot.

Fig. 5

Blood SO2 levels measured in phantom vessels by DWP-OCT. The solid line represents the mean of 15 segments of 0.5 s each, and the dashed lines represent standard deviation. The SO2 levels measured by a commercial oximeter are indicated in the right portion of each plot. Blood is stationary for all measurements.

JBO_18_5_056005_f005.png

The six blood samples’ SO2 levels cover a substantially wider range than physiological variation [from 70% (veins) to 97 to 99% (arteries)]. For each measured level, the oximeter SO2 measurement results are within the experimental error of DWP-OCT measurement values (Fig. 6). The AVOXimeter 1000E features a specified accuracy of ±1% and a precision of ±0.5% for blood SO2 measurements.

Fig. 6

Blood SO2 level measured by DWP-OCT (vertical) versus oximeter values (horizontal). Blood is stationary for all measurements.

JBO_18_5_056005_f006.png

3.3.

Influence of Blood Flow on DWP-OCT SO2 Measurement

To determine the impact of blood flow on DWP-OCT SO2 measurement, we recorded the op signal amplitude in a 50-μm inner-diameter phantom blood vessel at different average blood flow speeds introduced by the syringe pump. For each DWP-OCT measurement, the SO2 level was fixed at 98.2%. At increasing blood flow speeds, the op signal amplitude induced by blood absorption of each photothermal excitation beam was reduced, as shown in Fig. 7(a). A substantial reduction (80%) in the op signal amplitude was observed at the greatest average blood flow speed (17mm/s).

Fig. 7

(a) Reduction in the op signal amplitude at 800 nm (380 Hz) and 770 nm (400 Hz) from stationary (solid line) to increased average blood flow speed (dashed line, 8.5mm/s) in a phantom blood vessel with a 50-μm inner diameter. (b) Normalized op signal amplitude versus average blood flow speed. Circle: op signal amplitude in response to 770-nm excitation, dashed line: linear fit, diamond: op signal amplitude in response to 800-nm excitation, solid line: linear fit.

JBO_18_5_056005_f007.png

The DWP-OCT measurements were recorded at average blood flow speeds from stationary to 17mm/s, and the op signal amplitudes for 770-nm (400-Hz) and 800-nm (380-Hz) light were normalized by respective amplitudes at the stationary condition, as shown in Fig. 7(b).

4.

Discussion

In this study, we constructed a DWP-OCT system for the imaging and measurement of static and flowing blood SO2 level in a phantom vessel. From Eq. (2), we find that the relative uncertainty in DWP-OCT blood SO2 values can be written as

(3)

δSO2SO2=(αd2αo1αd1αo2)(δχ12/χ12)[χ12(αo2αd2)(αo1αd1)](αd1/χ12αd2).

The variation in χ12 [δχ12/χ12; see Eq. (4)] can originate from phase variation in the optical pathlength (op1 or op2) or the fluence (Φ1 or Φ2) of photothermal excitation beams.

(4)

(δχ12χ12)2(δop1op1)2+(δop2op2)2+(δΦ1Φ1)2+(δΦ2Φ2)2.

We define the op signal-to-noise ratio [SNR; see Eq. (5)], where op is the optical pathlength signal amplitude in response to photothermal excitation (380 Hz or 400 Hz), and δ op corresponds to the optical pathlength variation due to either the DWP-OCT system or relative motion between the DWP-OCT source beams (PhS-OCT probe beam and photothermal excitation beams) and sample constituents.

(5)

SNR(dB)=10log(opδop).

4.1.

DWP-OCT Static Blood SO2 Measurement Error

In phantom vessel static blood SO2 measurement, low-power (2.8mW) photothermal excitation light gives op amplitudes of 2 to 5 nm, and a 0.3-nm uncertainty in op amplitude gives a relative uncertainty δop/op=6 to 15% (op SNR 8.2 to 12.2 dB). Laser power fluctuation can introduce a 2% uncertainty in δΦ/Φ. Based on Eqs. (3) and (4), the effect of the op SNR on the relative blood SO2 measurement error (δSO2/SO2) was estimated (Fig. 8). Relative uncertainty in χ12 decreases with an increasing op SNR, as shown in Fig. 8(a). Relative uncertainty in DWP-OCT blood SO2 increases with decreased SO2 values, as shown in Fig. 8(b). At any blood SO2 level, δSO2/SO2 increases with increasing relative uncertainty in χ12. Each of the six measured blood samples’ relative SO2 measurement errors in a single (0.5-s) segment was deduced and plotted, as shown in Fig. 8(b), and they have values close to curves corresponding to 20% and 30% relative uncertainty in χ12.

Fig. 8

(a) Relative χ12 error (δχ12/χ12) versus op SNR.(b) Relative blood SO2 measurement error (δSO2/SO2) versus SO2 for various levels of relative χ12 error. Horizontal axis: blood SO2 level, vertical axis: relative error of SO2. Solid curves represent conditions when the relative variations of χ12 are 5%, 10%, 20%, and 30%. Dashed lines: SO2 of veins (70%) and arteries (97%), circles: relative blood SO2 measurement error in six blood samples.

JBO_18_5_056005_f008.png

To reduce DWP-OCT’s relative blood SO2 measurement error to within 5% (SO2 above 60%), relative uncertainty in χ12 must be less than 5%, requiring an op SNR above 15 dB (δop/op below 3%). A substantial increase in DWP-OCT SO2 measurement errors observed in 57.3% and 3% SO2 blood levels are consistent with computed values, as shown in Fig. 8(b). To increase DWP-OCT blood SO2 measurement accuracy and reliability, system phase stabilization is critical.

4.2.

Effect of Blood Flow on SNR of the Optical Pathlength Signal

The accuracy of DWP-OCT SO2 measurement at various blood flow speeds can be determined by analysis of the SNR of the op signal in response to laser excitation [Eq. (5)]. SNR degradation with respect to increasing blood flow speed, illustrated in Fig. 9(a), suggests that the most reliable DWP-OCT SO2 measurements can be obtained at blood flow speeds up to 13mm/s.

Fig. 9

(a) SNR degradation versus blood flow speed. Circle: SNR in response to 770-nm excitation, dashed line: linear fit, diamond: SNR in response to 800-nm excitation, solid line: linear fit, dotted line: 10-dB SNR op degradation. (b) SO2 measurement in blood vessel phantom at various blood flow speeds. Diamond: SO2 measured by DWP-OCT, solid line: SO2 measured by oximeter (98.2%), dashed line: threshold speed above which SNR degradation exceeds 10 dB. (c) Relative blood SO2 measurement error (δSO2/SO2) for a single segment (0.5 s) versus blood flow speed. Dashed line is linear fit.

JBO_18_5_056005_f009.png

The SO2 level is calculated for average blood flow speeds up to 17mm/s, as shown in Fig. 9(b). The SO2 measured by DWP-OCT is within the experimental error of values measured by a commercial oximeter for average blood flow speeds less than 13mm/s. A 13-mm/s average blood flow speed is found in retinal arterioles47 30 to 40 μm in diameter. Relative blood SO2 measurement error increases with increasing blood flow speed, as shown in Fig. 9(c). The op SNR is a critical factor that determines accuracy of measured SO2 levels, as shown in Fig. 9(b). The results suggest that when the op SNR degradation exceeds 10 dB, SO2 levels measured by DWP-OCT are no longer reliable. Experimental results suggest that a DWP-OCT system utilizing low-power (2.8mW) photothermal excitation has sufficient stability and sensitivity to measure SO2 levels in a 50-μm-diameter stationary blood vessel phantom with average blood flow speeds from stationary up to 13mm/s.

Brownian motion and blood flow can also contribute to an increased op signal noise floor; in the blood flow experiments reported here, the difference in refractive indices between red blood cells (RBC) and blood plasma is one source of increased op signal noise. The time dependent optical pathlength [op(t)] of the probe beam traveling through the phantom vessel lumen can be expressed as

(6)

op(t)=nRBClRBC(t)+nplasmalplasma(t),
where nRBC and nplasma are the group refractive indices of red blood cells and plasma, respectively; and lRBC and lplasma are the physical pathlengths that probe beam travels through RBC and blood plasma, respectively. The values of lRBC and lplasma vary randomly due to blood flow; a higher blood flow speed will cause op signal amplitude to change more rapidly, as indicated in Eq. (6), which results in an increased op signal noise floor between successive A-scans. In the case of stationary blood, Brownian RBC motion contributes to the op signal noise. For the phantom blood vessel tested here (with a 50 μm innerdiameter), the effect of Brownian RBC motion on op signal noise is approximately equivalent to the increase associated with a 6-mm/s blood flow speed relative to the stationary state.48 An increased op signal noise floor is observed in a larger vessel (300 μm innerdiameter) due to a longer physical pathlength. The SO2 measurement has also been recorded in a 300-μm-diameter phantom blood vessel. At an equivalent average blood flow speed (11.8mm/s), the op signal noise floor (1.82 nm) in the larger phantom vessel (300 um innerdiameter) increases by 1.3 nm over the signal noise floor (0.52 nm) in the phantom vessel with a 50-μm innerdiameter.

For in vivo measurements, the relative motion between the DWP-OCT source beams and the bulk tissue is an additional noise source that degrades the op SNR. Tissue motion artifacts can be suppressed by increasing either the modulation frequency or the DWP-OCT A-scan rate. SS laser sweep rates of up to 5 MHz have been demonstrated.49 A higher modulation frequency will require photothermal excitation lasers with greater instantaneous power (corresponding to a shorter excitation period) to maintain fluence at the same level as the system presented here. In studies reported here, the incident radiant power (2.8mW) is within the ANSI limits for skin. For retinal applications, the photothermal excitation power must be less than 0.75 mW.

Acknowledgments

This study was partially supported by NIH KL2 training grants (Parent Grant Nos. UL1RR025767 and KL2RR025766); by the San Antonio Area Foundation (Grant No. 130977); and by research support from Carl Zeiss Meditec to RVK and TEM, from the Department of Veterans Affairs (VA MERIT Award) to TQD, and by the NIH (R01 EY018855 and R01 EY014211) to TQD. The authors also gratefully acknowledge support from the National Institutes of Health (NIH R01EY016462).

References

1. 

P. CarmelietR. K. Jain, “Angiogenesis in cancer and other diseases,” Nature 407(6801), 249–257 (2000).NATUAS0028-0836http://dx.doi.org/10.1038/35025220Google Scholar

2. 

P. Carmeliet, “Angiogenesis in life, disease and medicine,” Nature 438(7070), 932–936 (2005).NATUAS0028-0836http://dx.doi.org/10.1038/nature04478Google Scholar

3. 

R. A. LinsenmeierC. M. Yancey, “Effects of hyperoxia on the oxygen distribution in the intact cat retina,” Investig. Ophthalmol. Vis. Sci. 30(4), 612–618 (1989).IOVSDA0146-0404Google Scholar

4. 

L. Padnick-Silveret al., “Retinal oxygenation and oxygen metabolism in Abyssinian cats with a hereditary retinal degeneration,” Investig. Ophthalmol. Vis. Sci. 47(8), 3683–3689 (2006).IOVSDA0146-0404http://dx.doi.org/10.1167/iovs.05-1284Google Scholar

5. 

D. Y. YuS. J. CringleE. N. Su, “Intraretinal oxygen distribution in the monkey retina and the response to systemic hyperoxia,” Investig. Ophthalmol. Vis. Sci. 46(12), 4728–4733 (2005).IOVSDA0146-0404http://dx.doi.org/10.1167/iovs.05-0694Google Scholar

6. 

R. N. Gludet al., “Planar optrodes: a new tool for fine scale measurements of two-dimensional O2 distribution in benthic communities,” Mar. Ecol. Prog. Ser. 140, 217–226 (1996).MESEDT0171-8630http://dx.doi.org/10.3354/meps140217Google Scholar

7. 

C. Y. Yuet al., “Oxygen distribution and consumption in rat lower incisor pulp,” Arch. Oral Biol. 47(7), 529–536 (2002).AOBIAR0003-9969http://dx.doi.org/10.1016/S0003-9969(02)00036-5Google Scholar

8. 

H. Y. Chenget al., “Structural and functional MRI reveals multiple retinal layers,” Proc. Natl. Acad. Sci. U.S.A. 103(46), 17525–17530 (2006).PNASA60027-8424http://dx.doi.org/10.1073/pnas.0605790103Google Scholar

9. 

B. A. Berkowitzet al., “Subnormal retinal oxygenation response precedes diabetic-like retinopathy,” Investig. Ophthalmol. Vis. Sci. 40(9), 2100–2105 (1999).IOVSDA0146-0404Google Scholar

10. 

T. Q. Duonget al., “Layer-specific anatomical, physiological and functional MRI of the retina,” NMR Biomed. 21(9), 978–996 (2008).NMRBEF0952-3480http://dx.doi.org/10.1002/nbm.v21:9Google Scholar

11. 

A. Karniet al., “Functional MRI evidence for adult motor cortex plasticity during motor skill learning,” Nature 377(6545), 155–158 (1995).NATUAS0028-0836http://dx.doi.org/10.1038/377155a0Google Scholar

12. 

P. J. KoopmansM. BarthD. G. Norris, “Layer-specific BOLD activation in human V1,” Hum. Brain Mapp. 31(9), 1297–1304 (2010).HBRME71065-9471http://dx.doi.org/10.1002/hbm.v31:9Google Scholar

13. 

K. R. Denninghoffet al., “Retinal venous oxygen saturation and cardiac output during controlled hemorrhage and resuscitation,” J. Appl. Physiol. 94(3), 891–896 (2003).JAPYAA0021-8987Google Scholar

14. 

M. HammerD. Schweitzer, “Quantitative reflection spectroscopy at the human ocular fundus,” Phys. Med. Biol. 47(2), 179–191 (2002).PHMBA70031-9155http://dx.doi.org/10.1088/0031-9155/47/2/301Google Scholar

15. 

P. L. MadsenN. H. Secher, “Near-infrared oximetry of the brain,” Prog. Neurobiol. 58(6), 541–560 (1999).PGNBA50301-0082http://dx.doi.org/10.1016/S0301-0082(98)00093-8Google Scholar

16. 

M. G. Sowaet al., “Noninvasive assessment of regional and temporal variations in tissue oxygenation by near-infrared spectroscopy and imaging,” Appl. Spectrosc. 51(2), 143–151 (1997).APSPA40003-7028http://dx.doi.org/10.1366/0003702971939901Google Scholar

17. 

A. K. Dunnet al., “Simultaneous imaging of total cerebral hemoglobin concentration, oxygenation and blood flow during functional activation,” Opt. Lett. 28(1), 28–30 (2003).OPLEDP0146-9592http://dx.doi.org/10.1364/OL.28.000028Google Scholar

18. 

D. Izhakyet al., “Functional imaging using the retinal function imager: direct imaging of blood velocity, achieving fluorescein angiography-like images without any contrast agent, qualitative oximetry, and functional metabolic signals,” Jpn. J. Ophthalmol. 53(4), 345–351 (2009).JJOPA70021-5155http://dx.doi.org/10.1007/s10384-009-0689-0Google Scholar

19. 

R. D. ShonatA. C. Kight, “Oxygen tension imaging in the mouse retina,” Ann. Biomed. Eng. 31(9), 1084–1096 (2003).ABMECF0090-6964http://dx.doi.org/10.1114/1.1603256Google Scholar

20. 

R. ZuckermanJ. E. CheastyY. P. Wang, “Optical mapping of inner retinal tissue PO2,” Curr. Eye Res. 12(9), 809–825 (1993).CEYRDM0271-3683http://dx.doi.org/10.3109/02713689309020386Google Scholar

21. 

A. S. GolubM. A. TevaldR. N. Pittman, “Phosphorescence quenching microrespirometry of skeletal muscle in situ,” Am. J. Physiol. Heart Circ. Physiol. 300(1), H135–H143 (2011).0363-6135http://dx.doi.org/10.1152/ajpheart.00626.2010Google Scholar

22. 

A. G. Tsaiet al., “Microvascular and tissue oxygen gradients in the rat mesentery,” Proc. Natl. Acad. Sci. U.S.A. 95(12), 6590–6595 (1998).PNASA60027-8424http://dx.doi.org/10.1073/pnas.95.12.6590Google Scholar

23. 

L. W. LoC. J. KochD. F. Wilson, “Calibration of oxygen-dependent quenching of the phosphorescence of Pd-meso-tetra (4-carboxyphenyl) porphine: a phosphor with general application for measuring oxygen concentration in biological systems,” Anal. Biochem. 236(1), 153–160 (1996).ANBCA20003-2697http://dx.doi.org/10.1006/abio.1996.0144Google Scholar

24. 

G. Helmlingeret al., “Interstitial pH and pO2 gradients in solid tumors in vivo: high-resolution measurements reveal a lack of correlation,” Nat. Med. 3(2), 177–182 (1997).1078-8956http://dx.doi.org/10.1038/nm0297-177Google Scholar

25. 

D. Huanget al., “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).SCIEAS0036-8075http://dx.doi.org/10.1126/science.1957169Google Scholar

26. 

A. F. Fercheret al., “Measurement of intraocular distances by backscattering spectral interferometry,” Opt. Commun. 117(1–2), 43–48 (1995).OPCOB80030-4018http://dx.doi.org/10.1016/0030-4018(95)00119-SGoogle Scholar

27. 

G. HäuslerM. W. Lindner, “‘Coherence radar’ and ‘spectral radar’-new tools for dermatological diagnosis,” J. Biomed. Opt. 3(1), 21–31 (1998).JBOPFO1083-3668http://dx.doi.org/10.1117/1.429899Google Scholar

28. 

R. Huberet al., “Amplified, frequency swept lasers for frequency domain reflectometry and OCT imaging: design and scaling principles,” Opt. Express 13(9), 3513–3528 (2005).OPEXFF1094-4087http://dx.doi.org/10.1364/OPEX.13.003513Google Scholar

29. 

F. RoblesR. N. GrafA. Wax, “Dual window method for processing spectroscopic optical coherence tomography signals with simultaneously high spectral and temporal resolution,” Opt. Express 17(8), 6799–6812 (2009).OPEXFF1094-4087http://dx.doi.org/10.1364/OE.17.006799Google Scholar

30. 

R. Leitgebet al., “Spectral measurement of absorption by spectroscopic frequency-domain optical coherence tomography,” Opt. Lett. 25(11), 820–822 (2000).OPLEDP0146-9592http://dx.doi.org/10.1364/OL.25.000820Google Scholar

31. 

D. J. FaberT. G. van Leeuwen, “Are quantitative attenuation measurements of blood by optical coherence tomography feasible?,” Opt. Lett. 34(9), 1435–1437 (2009).OPLEDP0146-9592http://dx.doi.org/10.1364/OL.34.001435Google Scholar

32. 

J. YiX. Li, “Estimation of oxygen saturation from erythrocytes by high-resolution spectroscopic optical coherence tomography,” Opt. Lett. 35(12), 2094–2096 (2010).OPLEDP0146-9592http://dx.doi.org/10.1364/OL.35.002094Google Scholar

33. 

F. E. Robleset al., “Molecular imaging true-colour spectroscopic optical coherence tomography,” Nat. Photon. 5(12), 744–747 (2011).1749-4885http://dx.doi.org/10.1038/nphoton.2011.257Google Scholar

34. 

D.C. Adleret al., “Photothermal detection of gold nanoparticles using phase-sensitive optical coherence tomography,” Opt. Express 16(7), 4376–4393 (2008).OPEXFF1094-4087http://dx.doi.org/10.1364/OE.16.004376Google Scholar

35. 

M. C. Skalaet al., “Photothermal optical coherence tomography of epidermal growth factor receptor in live cells using immunotargeted gold nanospheres,” Nano Lett. 8(10), 3461–3467 (2008).NALEFD1530-6984http://dx.doi.org/10.1021/nl802351pGoogle Scholar

36. 

A. S. Paranjapeet al., “Depth resolved photothermal OCT detection of macrophages in tissue using nanorose,” Biomed. Opt. Express 1(1), 2–16 (2010).BOEICL2156-7085http://dx.doi.org/10.1364/BOE.1.000002Google Scholar

37. 

R. V. Kuranovet al., “Depth-resolved blood oxygen saturation measurement by dual-wavelength photothermal (DWP) optical coherence tomography,” Biomed. Opt. Express 2(3), 491–504 (2011).BOEICL2156-7085http://dx.doi.org/10.1364/BOE.2.000491Google Scholar

38. 

R. V. Kuranovet al., “In vivo depth-resolved oxygen saturation by dual-wavelength photothermal (DWP) OCT,” Opt. Express 19(24), 23831–23844 (2011).OPEXFF1094-4087http://dx.doi.org/10.1364/OE.19.023831Google Scholar

39. 

R. V. Kuranovet al., “Gas-cell referenced swept source phase sensitive optical coherence tomography,” IEEE Photon. Technol. Lett. 22(20), 1524–1526 (2010).IPTLEL1041-1135http://dx.doi.org/10.1109/LPT.2010.2055842Google Scholar

40. 

K. Santhanam, “Clock system design for quadrupling the frequency of reference clock for a swept source spectral domain optical coherence tomography,” Master Thesis, The University of Texas at Austin (2009).Google Scholar

41. 

A. M. RollinsJ. A. Izatt, “Optimal interferometer designs for optical coherence tomography,” Opt. Lett. 24(21), 1484–1486 (1999).OPLEDP0146-9592http://dx.doi.org/10.1364/OL.24.001484Google Scholar

42. 

B. J. Vakocet al., “Phase-resolved optical frequency domain imaging,” Opt. Express 13(14), 5483–5493 (2005).OPEXFF1094-4087http://dx.doi.org/10.1364/OPEX.13.005483Google Scholar

43. 

M. Wojtkowskiet al., “Ultrahigh-resolution, high-speed, Fourier domain optical coherence tomography and methods for dispersion compensation,” Opt. Express 12(11), 2404–2422 (2004).OPEXFF1094-4087http://dx.doi.org/10.1364/OPEX.12.002404Google Scholar

44. 

T. Wuet al., “Spectral phase based k-domain interpolation for uniform sampling in swept-source optical coherence tomography,” Opt. Express 19(19), 18430–18439 (2011).OPEXFF1094-4087http://dx.doi.org/10.1364/OE.19.018430Google Scholar

45. 

A. J. WelchM. J. C. van Gemert, Optical-Thermal Response of Laser-Irradiated Tissue, Laser, Photonics, and Electro-Optics, Plenum Press, New York, NY (1995).Google Scholar

46. 

K. Briely-SaboA. Bjornerud, “Accurate de-oxygenation of ex vivo whole blood using sodium Dithionite,” Proc. Intl. Sot. Mag. Reson. Med. 8, 2025 (2000).Google Scholar

47. 

Y. P. Maet al., “On-line measurement of the dynamic velocity of erythrocytes in the cerebral microvessels in the rat,” Microvas. Res. 8(1), 1–13 (1974).MIVRA60026-2862http://dx.doi.org/10.1016/0026-2862(74)90059-4Google Scholar

48. 

T. Binzoniet al., “Non-invasive laser Doppler perfusion measurements of large tissue volumes and human skeletal muscle blood RMS velocity,” Phys. Med. Biol. 48(15), 2527–2549 (2003).PHMBA70031-9155http://dx.doi.org/10.1088/0031-9155/48/15/318Google Scholar

49. 

W. Wieseret al., “Multi-Megahertz OCT: high quality 3D imaging at 20 million A-scans and 4.5 GVoxels per second,” Opt. Express 18(14), 14685–14704 (2010).OPEXFF1094-4087http://dx.doi.org/10.1364/OE.18.014685Google Scholar

Biwei Yin, Roman V. Kuranov, Austin B. McElroy, S. M. Shams Kazmi, Andrew K. Dunn, Timothy Q. Duong, Thomas E. Milner, "Dual-wavelength photothermal optical coherence tomography for imaging microvasculature blood oxygen saturation," Journal of Biomedical Optics 18(5), 056005 (2 May 2013). http://dx.doi.org/10.1117/1.JBO.18.5.056005
Submission: Received ; Accepted
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KEYWORDS
Blood

Optical coherence tomography

Blood circulation

Signal to noise ratio

Coherence imaging

Imaging systems

Blood vessels

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