Abstract

Acoustic resolution photoacoustic Doppler velocimetry promises to overcome the spatial resolution and depth penetration limitations of current blood flow measuring methods. Despite successful implementation using blood-mimicking fluids, measurements in blood have proved challenging, thus preventing in vivo application. A common explanation for this difficulty is that whole blood is insufficiently heterogeneous relative to detector frequencies of tens of MHz compatible with deep tissue photoacoustic measurements. Through rigorous experimental measurements we provide new insight that refutes this assertion. We show for the first time that, by careful choice of the detector frequency and field-of-view, and by employing novel signal processing methods, it is possible to make velocity measurements in whole blood using transducers with frequencies in the tens of MHz range. These findings have important implications for the prospects of making deep tissue measurements of blood flow relevant to the study of microcirculatory abnormalities associated with cancer, diabetes, atherosclerosis and other conditions.

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References

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    [Crossref]

2016 (2)

J. Brunker and P. Beard, “Acoustic resolution photoacoustic Doppler velocimetry in blood-mimicking fluids,” Sci. Rep. 6, 20902 (2016).
[Crossref] [PubMed]

P. J. van den Berg, K. Daoudi, and W. Steenbergen, “Pulsed photoacoustic flow imaging with a handheld system,” J. Biomed. Opt. 21(2), 026004 (2016).
[Crossref] [PubMed]

2015 (2)

T. E. de Carlo, A. Romano, N. K. Waheed, and J. S. Duker, “A review of optical coherence tomography angiography (OCTA),” Int. J. Retin. Vitr. 1(1), 5 (2015).
[Crossref]

P. J. van den Berg, K. Daoudi, and W. Steenbergen, “Review of photoacoustic flow imaging: its current state and its promises,” Photoacoustics 3(3), 89–99 (2015).
[Crossref] [PubMed]

2014 (3)

E. Cinotti, L. Gergelé, J. L. Perrot, A. Dominé, B. Labeille, P. Borelli, and F. Cambazard, “Quantification of capillary blood cell flow using reflectance confocal microscopy,” Skin Res. Technol. 20(3), 373–378 (2014).
[Crossref] [PubMed]

J. Yao, R. C. Gilson, K. I. Maslov, L. Wang, and L. V. Wang, “Calibration-free structured-illumination photoacoustic flowgraphy of transverse flow in scattering media,” J. Biomed. Opt. 19(4), 046007 (2014).
[Crossref] [PubMed]

R. Zhang, L. Wang, J. Yao, C.-H. Yeh, and L. V. Wang, “In vivo optically encoded photoacoustic flowgraphy,” Opt. Lett. 39(13), 3814–3817 (2014).
[Crossref] [PubMed]

2013 (4)

2012 (4)

E. Hysi, R. K. Saha, and M. C. Kolios, “Photoacoustic ultrasound spectroscopy for assessing red blood cell aggregation and oxygenation,” J. Biomed. Opt. 17(12), 125006 (2012).
[Crossref] [PubMed]

J. Brunker and P. Beard, “Pulsed photoacoustic Doppler flowmetry using time-domain cross-correlation: accuracy, resolution and scalability,” J. Acoust. Soc. Am. 132(3), 1780–1791 (2012).
[Crossref] [PubMed]

L. Golan, D. Yeheskely-Hayon, L. Minai, E. J. Dann, and D. Yelin, “Noninvasive imaging of flowing blood cells using label-free spectrally encoded flow cytometry,” Biomed. Opt. Express 3(6), 1455–1464 (2012).
[Crossref] [PubMed]

G. Liu, A. J. Lin, B. J. Tromberg, and Z. Chen, “A comparison of Doppler optical coherence tomography methods,” Biomed. Opt. Express 3(10), 2669–2680 (2012).
[Crossref] [PubMed]

2011 (1)

2010 (5)

A. Sheinfeld, S. Gilead, and A. Eyal, “Simultaneous spatial and spectral mapping of flow using photoacoustic Doppler measurement,” J. Biomed. Opt. 15(6), 066010 (2010).
[Crossref] [PubMed]

J. Yao and L. V. Wang, “Transverse flow imaging based on photoacoustic Doppler bandwidth broadening,” J. Biomed. Opt. 15(2), 021304 (2010).
[Crossref] [PubMed]

J. Laufer, E. Zhang, and P. Beard, “Evaluation of Absorbing Chromophores Used in Tissue Phantoms for Quantitative Photoacoustic Spectroscopy and Imaging,” IEEE J. Sel. Top. Quantum Electron. 16(3), 600–607 (2010).
[Crossref]

A. Sheinfeld, S. Gilead, and A. Eyal, “Photoacoustic Doppler measurement of flow using tone burst excitation,” Opt. Express 18(5), 4212–4221 (2010).
[Crossref] [PubMed]

S. L. Chen, T. Ling, S. W. Huang, H. Won Baac, and L. J. Guo, “Photoacoustic correlation spectroscopy and its application to low-speed flow measurement,” Opt. Lett. 35(8), 1200–1202 (2010).
[Crossref] [PubMed]

2008 (1)

2007 (1)

P. Vennemann, R. Lindken, and J. Westerweel, “In vivo whole-field blood velocity measurement techniques,” Exp. Fluids 42(4), 495–511 (2007).
[Crossref]

1993 (1)

S. M. Sagar, G. A. Klassen, K. D. Barclay, and J. E. Aldrich, “Tumour blood flow: measurement and manipulation for therapeutic gain,” Cancer Treat. Rev. 19(4), 299–349 (1993).
[Crossref] [PubMed]

Aldrich, J. E.

S. M. Sagar, G. A. Klassen, K. D. Barclay, and J. E. Aldrich, “Tumour blood flow: measurement and manipulation for therapeutic gain,” Cancer Treat. Rev. 19(4), 299–349 (1993).
[Crossref] [PubMed]

Barclay, K. D.

S. M. Sagar, G. A. Klassen, K. D. Barclay, and J. E. Aldrich, “Tumour blood flow: measurement and manipulation for therapeutic gain,” Cancer Treat. Rev. 19(4), 299–349 (1993).
[Crossref] [PubMed]

Beard, P.

J. Brunker and P. Beard, “Acoustic resolution photoacoustic Doppler velocimetry in blood-mimicking fluids,” Sci. Rep. 6, 20902 (2016).
[Crossref] [PubMed]

J. Brunker and P. Beard, “Pulsed photoacoustic Doppler flowmetry using time-domain cross-correlation: accuracy, resolution and scalability,” J. Acoust. Soc. Am. 132(3), 1780–1791 (2012).
[Crossref] [PubMed]

J. Laufer, E. Zhang, and P. Beard, “Evaluation of Absorbing Chromophores Used in Tissue Phantoms for Quantitative Photoacoustic Spectroscopy and Imaging,” IEEE J. Sel. Top. Quantum Electron. 16(3), 600–607 (2010).
[Crossref]

E. Zhang, J. Laufer, and P. Beard, “Backward-mode multiwavelength photoacoustic scanner using a planar Fabry-Perot polymer film ultrasound sensor for high-resolution three-dimensional imaging of biological tissues,” Appl. Opt. 47(4), 561–577 (2008).
[Crossref] [PubMed]

Berndl, E. S. L.

E. M. Strohm, E. S. L. Berndl, and M. C. Kolios, “Probing red blood cell morphology using high-frequency photoacoustics,” Biophys. J. 105(1), 59–67 (2013).
[Crossref] [PubMed]

Borelli, P.

E. Cinotti, L. Gergelé, J. L. Perrot, A. Dominé, B. Labeille, P. Borelli, and F. Cambazard, “Quantification of capillary blood cell flow using reflectance confocal microscopy,” Skin Res. Technol. 20(3), 373–378 (2014).
[Crossref] [PubMed]

Brunker, J.

J. Brunker and P. Beard, “Acoustic resolution photoacoustic Doppler velocimetry in blood-mimicking fluids,” Sci. Rep. 6, 20902 (2016).
[Crossref] [PubMed]

J. Brunker and P. Beard, “Pulsed photoacoustic Doppler flowmetry using time-domain cross-correlation: accuracy, resolution and scalability,” J. Acoust. Soc. Am. 132(3), 1780–1791 (2012).
[Crossref] [PubMed]

Cambazard, F.

E. Cinotti, L. Gergelé, J. L. Perrot, A. Dominé, B. Labeille, P. Borelli, and F. Cambazard, “Quantification of capillary blood cell flow using reflectance confocal microscopy,” Skin Res. Technol. 20(3), 373–378 (2014).
[Crossref] [PubMed]

Carson, P. L.

Chen, S. L.

Chen, S.-L.

Chen, Z.

Cinotti, E.

E. Cinotti, L. Gergelé, J. L. Perrot, A. Dominé, B. Labeille, P. Borelli, and F. Cambazard, “Quantification of capillary blood cell flow using reflectance confocal microscopy,” Skin Res. Technol. 20(3), 373–378 (2014).
[Crossref] [PubMed]

Dann, E. J.

Daoudi, K.

P. J. van den Berg, K. Daoudi, and W. Steenbergen, “Pulsed photoacoustic flow imaging with a handheld system,” J. Biomed. Opt. 21(2), 026004 (2016).
[Crossref] [PubMed]

P. J. van den Berg, K. Daoudi, and W. Steenbergen, “Review of photoacoustic flow imaging: its current state and its promises,” Photoacoustics 3(3), 89–99 (2015).
[Crossref] [PubMed]

de Carlo, T. E.

T. E. de Carlo, A. Romano, N. K. Waheed, and J. S. Duker, “A review of optical coherence tomography angiography (OCTA),” Int. J. Retin. Vitr. 1(1), 5 (2015).
[Crossref]

Dominé, A.

E. Cinotti, L. Gergelé, J. L. Perrot, A. Dominé, B. Labeille, P. Borelli, and F. Cambazard, “Quantification of capillary blood cell flow using reflectance confocal microscopy,” Skin Res. Technol. 20(3), 373–378 (2014).
[Crossref] [PubMed]

Duker, J. S.

T. E. de Carlo, A. Romano, N. K. Waheed, and J. S. Duker, “A review of optical coherence tomography angiography (OCTA),” Int. J. Retin. Vitr. 1(1), 5 (2015).
[Crossref]

Eyal, A.

A. Sheinfeld, S. Gilead, and A. Eyal, “Photoacoustic Doppler measurement of flow using tone burst excitation,” Opt. Express 18(5), 4212–4221 (2010).
[Crossref] [PubMed]

A. Sheinfeld, S. Gilead, and A. Eyal, “Simultaneous spatial and spectral mapping of flow using photoacoustic Doppler measurement,” J. Biomed. Opt. 15(6), 066010 (2010).
[Crossref] [PubMed]

Gergelé, L.

E. Cinotti, L. Gergelé, J. L. Perrot, A. Dominé, B. Labeille, P. Borelli, and F. Cambazard, “Quantification of capillary blood cell flow using reflectance confocal microscopy,” Skin Res. Technol. 20(3), 373–378 (2014).
[Crossref] [PubMed]

Gilead, S.

A. Sheinfeld, S. Gilead, and A. Eyal, “Photoacoustic Doppler measurement of flow using tone burst excitation,” Opt. Express 18(5), 4212–4221 (2010).
[Crossref] [PubMed]

A. Sheinfeld, S. Gilead, and A. Eyal, “Simultaneous spatial and spectral mapping of flow using photoacoustic Doppler measurement,” J. Biomed. Opt. 15(6), 066010 (2010).
[Crossref] [PubMed]

Gilson, R. C.

J. Yao, R. C. Gilson, K. I. Maslov, L. Wang, and L. V. Wang, “Calibration-free structured-illumination photoacoustic flowgraphy of transverse flow in scattering media,” J. Biomed. Opt. 19(4), 046007 (2014).
[Crossref] [PubMed]

Golan, L.

Guo, L. J.

Huang, S. W.

Hysi, E.

E. Hysi, R. K. Saha, and M. C. Kolios, “Photoacoustic ultrasound spectroscopy for assessing red blood cell aggregation and oxygenation,” J. Biomed. Opt. 17(12), 125006 (2012).
[Crossref] [PubMed]

Klassen, G. A.

S. M. Sagar, G. A. Klassen, K. D. Barclay, and J. E. Aldrich, “Tumour blood flow: measurement and manipulation for therapeutic gain,” Cancer Treat. Rev. 19(4), 299–349 (1993).
[Crossref] [PubMed]

Kolios, M. C.

E. M. Strohm, E. S. L. Berndl, and M. C. Kolios, “Probing red blood cell morphology using high-frequency photoacoustics,” Biophys. J. 105(1), 59–67 (2013).
[Crossref] [PubMed]

E. Hysi, R. K. Saha, and M. C. Kolios, “Photoacoustic ultrasound spectroscopy for assessing red blood cell aggregation and oxygenation,” J. Biomed. Opt. 17(12), 125006 (2012).
[Crossref] [PubMed]

Labeille, B.

E. Cinotti, L. Gergelé, J. L. Perrot, A. Dominé, B. Labeille, P. Borelli, and F. Cambazard, “Quantification of capillary blood cell flow using reflectance confocal microscopy,” Skin Res. Technol. 20(3), 373–378 (2014).
[Crossref] [PubMed]

Laufer, J.

J. Laufer, E. Zhang, and P. Beard, “Evaluation of Absorbing Chromophores Used in Tissue Phantoms for Quantitative Photoacoustic Spectroscopy and Imaging,” IEEE J. Sel. Top. Quantum Electron. 16(3), 600–607 (2010).
[Crossref]

E. Zhang, J. Laufer, and P. Beard, “Backward-mode multiwavelength photoacoustic scanner using a planar Fabry-Perot polymer film ultrasound sensor for high-resolution three-dimensional imaging of biological tissues,” Appl. Opt. 47(4), 561–577 (2008).
[Crossref] [PubMed]

Li, C.

Liang, J.

Lin, A. J.

Lindken, R.

P. Vennemann, R. Lindken, and J. Westerweel, “In vivo whole-field blood velocity measurement techniques,” Exp. Fluids 42(4), 495–511 (2007).
[Crossref]

Ling, T.

Liu, G.

Maslov, K.

L. Wang, K. Maslov, and L. V. Wang, “Single-cell label-free photoacoustic flowoxigraphy in vivo,” Proc. Natl. Acad. Sci. U.S.A. 110(15), 5759–5764 (2013).
[Crossref] [PubMed]

Maslov, K. I.

Minai, L.

Perrot, J. L.

E. Cinotti, L. Gergelé, J. L. Perrot, A. Dominé, B. Labeille, P. Borelli, and F. Cambazard, “Quantification of capillary blood cell flow using reflectance confocal microscopy,” Skin Res. Technol. 20(3), 373–378 (2014).
[Crossref] [PubMed]

Romano, A.

T. E. de Carlo, A. Romano, N. K. Waheed, and J. S. Duker, “A review of optical coherence tomography angiography (OCTA),” Int. J. Retin. Vitr. 1(1), 5 (2015).
[Crossref]

Sagar, S. M.

S. M. Sagar, G. A. Klassen, K. D. Barclay, and J. E. Aldrich, “Tumour blood flow: measurement and manipulation for therapeutic gain,” Cancer Treat. Rev. 19(4), 299–349 (1993).
[Crossref] [PubMed]

Saha, R. K.

E. Hysi, R. K. Saha, and M. C. Kolios, “Photoacoustic ultrasound spectroscopy for assessing red blood cell aggregation and oxygenation,” J. Biomed. Opt. 17(12), 125006 (2012).
[Crossref] [PubMed]

Sheinfeld, A.

A. Sheinfeld, S. Gilead, and A. Eyal, “Photoacoustic Doppler measurement of flow using tone burst excitation,” Opt. Express 18(5), 4212–4221 (2010).
[Crossref] [PubMed]

A. Sheinfeld, S. Gilead, and A. Eyal, “Simultaneous spatial and spectral mapping of flow using photoacoustic Doppler measurement,” J. Biomed. Opt. 15(6), 066010 (2010).
[Crossref] [PubMed]

Steenbergen, W.

P. J. van den Berg, K. Daoudi, and W. Steenbergen, “Pulsed photoacoustic flow imaging with a handheld system,” J. Biomed. Opt. 21(2), 026004 (2016).
[Crossref] [PubMed]

P. J. van den Berg, K. Daoudi, and W. Steenbergen, “Review of photoacoustic flow imaging: its current state and its promises,” Photoacoustics 3(3), 89–99 (2015).
[Crossref] [PubMed]

Strohm, E. M.

E. M. Strohm, E. S. L. Berndl, and M. C. Kolios, “Probing red blood cell morphology using high-frequency photoacoustics,” Biophys. J. 105(1), 59–67 (2013).
[Crossref] [PubMed]

Tromberg, B. J.

van den Berg, P. J.

P. J. van den Berg, K. Daoudi, and W. Steenbergen, “Pulsed photoacoustic flow imaging with a handheld system,” J. Biomed. Opt. 21(2), 026004 (2016).
[Crossref] [PubMed]

P. J. van den Berg, K. Daoudi, and W. Steenbergen, “Review of photoacoustic flow imaging: its current state and its promises,” Photoacoustics 3(3), 89–99 (2015).
[Crossref] [PubMed]

Vennemann, P.

P. Vennemann, R. Lindken, and J. Westerweel, “In vivo whole-field blood velocity measurement techniques,” Exp. Fluids 42(4), 495–511 (2007).
[Crossref]

Waheed, N. K.

T. E. de Carlo, A. Romano, N. K. Waheed, and J. S. Duker, “A review of optical coherence tomography angiography (OCTA),” Int. J. Retin. Vitr. 1(1), 5 (2015).
[Crossref]

Wang, L.

J. Yao, R. C. Gilson, K. I. Maslov, L. Wang, and L. V. Wang, “Calibration-free structured-illumination photoacoustic flowgraphy of transverse flow in scattering media,” J. Biomed. Opt. 19(4), 046007 (2014).
[Crossref] [PubMed]

R. Zhang, L. Wang, J. Yao, C.-H. Yeh, and L. V. Wang, “In vivo optically encoded photoacoustic flowgraphy,” Opt. Lett. 39(13), 3814–3817 (2014).
[Crossref] [PubMed]

L. Wang, K. Maslov, and L. V. Wang, “Single-cell label-free photoacoustic flowoxigraphy in vivo,” Proc. Natl. Acad. Sci. U.S.A. 110(15), 5759–5764 (2013).
[Crossref] [PubMed]

J. Liang, Y. Zhou, A. W. Winkler, L. Wang, K. I. Maslov, C. Li, and L. V. Wang, “Random-access optical-resolution photoacoustic microscopy using a digital micromirror device,” Opt. Lett. 38(15), 2683–2686 (2013).
[Crossref] [PubMed]

Wang, L. V.

R. Zhang, L. Wang, J. Yao, C.-H. Yeh, and L. V. Wang, “In vivo optically encoded photoacoustic flowgraphy,” Opt. Lett. 39(13), 3814–3817 (2014).
[Crossref] [PubMed]

J. Yao, R. C. Gilson, K. I. Maslov, L. Wang, and L. V. Wang, “Calibration-free structured-illumination photoacoustic flowgraphy of transverse flow in scattering media,” J. Biomed. Opt. 19(4), 046007 (2014).
[Crossref] [PubMed]

L. Wang, K. Maslov, and L. V. Wang, “Single-cell label-free photoacoustic flowoxigraphy in vivo,” Proc. Natl. Acad. Sci. U.S.A. 110(15), 5759–5764 (2013).
[Crossref] [PubMed]

J. Liang, Y. Zhou, A. W. Winkler, L. Wang, K. I. Maslov, C. Li, and L. V. Wang, “Random-access optical-resolution photoacoustic microscopy using a digital micromirror device,” Opt. Lett. 38(15), 2683–2686 (2013).
[Crossref] [PubMed]

J. Yao and L. V. Wang, “Transverse flow imaging based on photoacoustic Doppler bandwidth broadening,” J. Biomed. Opt. 15(2), 021304 (2010).
[Crossref] [PubMed]

Wang, L. V. L.

Wang, X.

Westerweel, J.

P. Vennemann, R. Lindken, and J. Westerweel, “In vivo whole-field blood velocity measurement techniques,” Exp. Fluids 42(4), 495–511 (2007).
[Crossref]

Winkler, A. W.

Won Baac, H.

Xie, Z.

Yao, J.

J. Yao, R. C. Gilson, K. I. Maslov, L. Wang, and L. V. Wang, “Calibration-free structured-illumination photoacoustic flowgraphy of transverse flow in scattering media,” J. Biomed. Opt. 19(4), 046007 (2014).
[Crossref] [PubMed]

R. Zhang, L. Wang, J. Yao, C.-H. Yeh, and L. V. Wang, “In vivo optically encoded photoacoustic flowgraphy,” Opt. Lett. 39(13), 3814–3817 (2014).
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Figures (11)

Fig. 1
Fig. 1 Schematic illustrating the principle of time correlation AR-PAF. The two signals are generated from clusters of moving red blood cells (RBCs) when illuminated by a pair of laser pulses separated by a time T. The inset shows the distribution of RBCs (represented by solid ellipses) when the first laser pulse is fired, and the new positions (unfilled ellipses) coincident with the firing of the second laser pulse a time T later. Between the two pulses the RBCs have moved from A to B, a distance l along the blood vessel. The waveforms show sections of the two photoacoustic signals p1(t) and p2(t) that correspond to the location of the blood vessel and illustrate the time shift ts between the two due to the motion of the RBCs along the vessel. It is also possible to calculate the velocity in specific regions of the vessel by time-windowing to select appropriate signal segments such as p1,seg(t) and p2,seg(t) in a manner analagous to range-gating in Doppler ultrasound.
Fig. 2
Fig. 2 Experimental setup for pulsed photoacoustic Doppler blood flow measurements. Laser pulses separated by a time T are used to generate pairs of photoacoustic waveforms which are detected by an ultrasound receiver positioned at an angle θ to the flow axis. This angle was measured to the nearest degree using a turntable with angular markings at 1° intervals, and verified by horizontally translating the tube and comparing the measured distances with those calculated from cross-correlation of photoacoustic signals acquired before and after translation. The inset shows that whilst a large area (at least 5 mm diameter) of the red blood cells (RBCs) is illuminated, photoacoustic signals are collected from a smaller region defined by the transducer focal spot in order to be representative of the acoustic resolution mode of photoacoustic detection.
Fig. 3
Fig. 3 Comparison of the accuracy of velocity measurements made using transducers with centre frequencies ranging from 5 MHz to 50 MHz (see Table 1). For each transducer the measurements were made for red blood cells (5% of a normal physiological haematocrit) flowing in a 390 μm tube, and illuminated with 532 nm laser pulses separated by T = 0.5 ms. The accuracies are the fractional errors of measurements V’ made for known velocities V < 50 mm/s. The error bars represent the standard error on the mean fractional error.
Fig. 4
Fig. 4 Illustration of bandlimiting due to spatial averaging over the detector field-of-view. At low concentrations, the absorbers within the tube are relatively sparsely distributed; there are rapid time-varying fluctuations in the detected photoacoustic (PA) waveform, and the acoustic frequency spectrum is broad. This permits accurate velocity measurements to be made. At high concentrations, the fluctuations in the PA waveform are smoothed out and the frequency spectrum is downshifted; the absorber distribution is perceived to be less heterogeneous and thus it is difficult to track the absorber motion and accurately measure the flow velocity.
Fig. 5
Fig. 5 Frequency downshifting due to spatial averaging arising from the detector FOV. (a) Comparison of normalised photoacoustic signal frequency content for a high red blood cell (RBC) concentration (100% of a physiologically normal haematocrit of Ht = 0.41) and a low RBC concentration (Ht = 0.01, which corresponds to about 3% of the physiologically normal value). The normalised frequency spectra are the means of normalised fast Fourier transforms (FFTs) calculated for over 5000 PA signals, and the dotted line shows the normalised frequency response of the detector. (b) Weighted mean frequencies (WMFs) calculated from the fast Fourier transforms (FFTs) of photoacoustic (PA) signals acquired for different red blood cell (RBC) haematocrits. The WMF was calculated by summing the product of the amplitudes and the frequencies of the FFT and normalising by the sum of the amplitudes (Eq. (3)) . The data points show the mean WMF of over 1500 FFTs calculated for a set of the same number of PA signals acquired for each of the relevant RBC concentrations, and the error bars represent the standard deviation. The blood samples were taken from human volunteers, centrifuged and washed with PBS three times before diluting with PBS to give the range of haematocrits shown. In both (a) and (b) the PA signals were generated using 532 nm laser pulses and acquired using the 30 MHz focussed transducer positioned at an angle of θ = 45° relative to a 390 μm tube containing the blood suspensions.
Fig. 6
Fig. 6 The effect of high red blood cell (RBC) concentration and RBC lysis on the accuracy of the time correlation velocity measurement. Velocity measurements were made for whole blood at a normal physiological haematocrit (grey crosses), a suspension of intact red blood cells (12.5% of a normal physiological haematocrit) in phosphate buffered saline (filled data points) and for haemolysed red blood cells (also 12.5%) in distilled water (unfilled data points). In each case, the fluid was flowing through a 400 µm tube, and the measurements were acquired using 540 nm laser pulses separated by T = 0.5 ms and the 30 MHz focussed transducer (θ = 45°). The length of the range gate was set such that it was greater than the tube diameter so the velocity measurement is integrated over the tube cross section. Each data point is the mean of three measurements, with a zero offset correction applied, and the vertical error bars represent the standard deviation. Microscopy images of the red blood cells are shown on the right.
Fig. 7
Fig. 7 Comparison of velocity measurements made for two different red blood cell concentrations, 12.5% and 25% relative to whole blood, and for two different excitation wavelengths: 540 nm (a) and 590 nm (b). The length of the range gate was set such that it was greater than the tube diameter so the velocity measurement is integrated over the tube cross section. At 590 nm, the deeper penetration of light into the tube yields more accurate measurements. In each case, the suspensions were flowing in a 400 µm diameter tube, and the photoacoustic signals were acquired with a 30 MHz focussed transducer (θ = 45°). The measurements were acquired using laser pulses separated by T = 0.5 ms. Each data point is the mean of three measurements, with a zero offset correction applied, and the vertical error bars represent the standard deviation.
Fig. 8
Fig. 8 Variation of measurement accuracy with the wavelength of the excitation lasers. The length of the range gate was set such that it was greater than the tube diameter so the velocity measurement is integrated over the tube cross section. The solid line represents the percentage of light (right axis) that penetrates into the centre of the tube (diameter: 390 µm), calculated from the absorption coefficient spectrum of oxyhaemoglobin (see Appendix, Fig. 11). The data points correspond to the accuracy (left axis) of velocity measurements made for a blood suspension (25% relative to whole blood) flowing at 15 mm/s. For each wavelength, the accuracy (1-fractional error) was calculated for the mean of five velocity measurements, and the vertical error bars correspond to the standard error.
Fig. 9
Fig. 9 Comparison of the accuracy of velocity measurements made for two different red blood cell concentrations, 12.5% and 25% relative to whole blood, and a wavelength of 540 nm. The data are those shown in Fig. 7(a) but after time-windowing to remove the bias towards low velocities due to the greater absorption at the edge of the tube.
Fig. 10
Fig. 10 Velocities measured for fresh, whole blood at a physiologically realistic concentration flowing in a tube of diameter 390 μm, and illuminated first with 610 nm light (a,b) and second with 532 nm light (c,d). The data were acquired with a laser pulse separation of T = 0.5 ms and a 30 MHz focused transducer (θ = 45°). In (a) and (c) the velocities were calculated by cross-correlating entire photoacoustic waveforms, whereas the velocities in (b) and (d) correspond to a single waveform segment selected by time-windowing. In (a) and (b) the data points are the mean of 5 velocity measurements and the vertical error bars represent the standard error; in (c) and (d) the data points are the mean of 20 velocity measurements and the vertical error bars represent the standard error.
Fig. 11
Fig. 11 Absorption coefficients for oxyhaemoglobin (solid line) plotted over a wavelength range of 450 nm to 680 nm [28]. The data points were obtained by fitting an exponential function [29] to photoacoustic waveforms detected in whole blood. There is excellent agreement between the measured and literature values for wavelengths below about 590 nm; for longer wavelengths the exponential fit becomes less reliable due to the low amplitude photoacoustic signal.

Tables (1)

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Table 1 Spherically focussed transducers used variously to detect photoacoustic signals generated in red blood cells a

Equations (3)

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V ' = c t s T cos θ
Mean fractional error =  1 N V ( V V ' V )                             ( V 0 )
Weighted mean frequency = i a i f i i a i

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