Improving the depth sensitivity of time-resolved measurements by extracting the distribution of times-of-flight

Mamadou Diop, Keith St Lawrence, Mamadou Diop, Keith St Lawrence

Abstract

Time-resolved (TR) techniques provide a means of discriminating photons based on their time-of-flight. Since early arriving photons have a lower probability of probing deeper tissue than photons with long time-of-flight, time-windowing has been suggested as a method for improving depth sensitivity. However, TR measurements also contain instrument contributions (instrument-response-function, IRF), which cause temporal broadening of the measured temporal point-spread function (TPSF) compared to the true distribution of times-of-flight (DTOF). The purpose of this study was to investigate the influence of the IRF on the depth sensitivity of TR measurements. TPSFs were acquired on homogeneous and two-layer tissue-mimicking phantoms with varying optical properties. The measured IRF and TPSFs were deconvolved using a stable algorithm to recover the DTOFs. The microscopic Beer-Lambert law was applied to the TPSFs and DTOFs to obtain depth-resolved absorption changes. In contrast to the DTOF, the latest part of the TPSF was not the most sensitive to absorption changes in the lower layer, which was confirmed by computer simulations. The improved depth sensitivity of the DTOF was illustrated in a pig model of the adult human head. Specifically, it was shown that dynamic absorption changes obtained from the late part of the DTOFs recovered from TPSFs acquired by probes positioned on the scalp were similar to absorption changes measured directly on the brain. These results collectively demonstrate that this method improves the depth sensitivity of TR measurements by removing the effects of the IRF.

Keywords: (170.3660) Light propagation in tissues; (170.3890) Medical optics instrumentation.

Figures

Fig. 1
Fig. 1
Schematic of the two-layer phantom. Measurements for a homogeneous medium were acquired by removing the top layer and placing the emission and detection optodes 3 cm apart on the surface of the bottom layer.
Fig. 2
Fig. 2
(a) TPSFs measured with the emission and detection probes positioned 3 cm apart on the surface of the homogeneous phantom. The curves were obtained by averaging 32 individual TPSFs, each acquired for 1s. The solid black curve is the TPSF measured at baseline (acquired at 800 kHz) and the cyan curve with the open circles is the TPSF acquired at the highest absorption coefficient (step 4 in Table 1). The dashed black curve is the instrument response function (IRF). Note, the amplitude of the IRF was divided by three for visualization purpose. (b) Corresponding DTOFs obtained by deconvolving the IRF and TPSFs shown in (a). Pathlength-resolved absorption changes (Δμa) recovered from the experimental TPSFs and the deconvolved DTOFs are shown in (c) and (d), respectively. The predicted Δμa from the simulated TPSFs and DTOFs are shown in (e) and (f), respectively.
Fig. 3
Fig. 3
(a) TPSFs measured with the probes positioned on the surface of the two-layer phantom. Each curve is the average of 32 TPSFs. The black curve is the TPSF at baseline and the cyan curve with the open circles is the TPSF acquired with the largest μa value in the bottom layer (step 4 in Table 1). (b) DTOFs obtained by deconvolving the IRF and TPSFs. Absorption changes (Δμa) recovered from (c) the experimental TPSFs and (d) the corresponding DTOFs. Fig. (e) and (f) are the pathlength-resolved Δμa generated from (e) the simulated TPSFs and (f) the simulated DTOFs. The vertical dashed lines indicate the specific pathlengths used to obtain the data presented in Fig. 4.
Fig. 4
Fig. 4
The absorption changes (Δμa) recovered from the two-layer media are plotted against Δμa in the bottom layer. The Δμa values are shown from early (red), middle (green) and late time-windows (blue) for experimental (a) TPSFs and (b) DTOFs, as well as for simulated (c) TPSFs and (d) DTOFs.
Fig. 5
Fig. 5
(a) Normalized absorption changes for early (black) and late (blue) time-windows (width = 0.1 ns) obtained with the DTOFs recovered from the TPSFs measured by probes positioned on the pig’s scalp at a source-detector separation of 3 cm. (b) and (c) Comparison of the normalized absorption changes determined with the probes placed directly on the brain (red curve with circles) and the normalized Δμa(t) obtained from late time-window of the DTOFs acquired with the probes on the scalp, following two distinct ICG injections (blue and green). Note that the red curves in (b) and (c) are the same.

References

    1. Jöbsis F. F., “Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters,” Science 198(4323), 1264–1267 (1977).10.1126/science.929199
    1. Dani C., Pratesi S., Fontanelli G., Barp J., Bertini G., “Blood transfusions increase cerebral, splanchnic, and renal oxygenation in anemic preterm infants,” Transfusion 50(6), 1220–1226 (2010).10.1111/j.1537-2995.2009.02575.x
    1. Diop M., Elliott J. T., Tichauer K. M., Lee T.-Y., St. Lawrence K., “A broadband continuous-wave multichannel near-infrared system for measuring regional cerebral blood flow and oxygen consumption in newborn piglets,” Rev. Sci. Instrum. 80(5), 054302 (2009).10.1063/1.3123347
    1. Schytz H. W., Wienecke T., Jensen L. T., Selb J., Boas D. A., Ashina M., “Changes in cerebral blood flow after acetazolamide: an experimental study comparing near-infrared spectroscopy and SPECT,” Eur. J. Neurol. 16(4), 461–467 (2009).10.1111/j.1468-1331.2008.02398.x
    1. Belau M., Ninck M., Hering G., Spinelli L., Contini D., Torricelli A., Gisler T., “Noninvasive observation of skeletal muscle contraction using near-infrared time-resolved reflectance and diffusing-wave spectroscopy,” J. Biomed. Opt. 15(5), 057007 (2010).10.1117/1.3503398
    1. Brown D. W., Hadway J., Lee T.-Y., “Near-infrared spectroscopy measurement of oxygen extraction fraction and cerebral metabolic rate of oxygen in newborn piglets,” Pediatr. Res. 54(6), 861–867 (2003).10.1203/
    1. Boas D. A., Gaudette T., Strangman G., Cheng X., Marota J. J., Mandeville J. B., “The accuracy of near infrared spectroscopy and imaging during focal changes in cerebral hemodynamics,” Neuroimage 13(1), 76–90 (2001).10.1006/nimg.2000.0674
    1. Liebert A., Wabnitz H., Steinbrink J., Obrig H., Möller M., Macdonald R., Villringer A., Rinneberg H., “Time-resolved multidistance near-infrared spectroscopy of the adult head: intracerebral and extracerebral absorption changes from moments of distribution of times of flight of photons,” Appl. Opt. 43(15), 3037–3047 (2004).10.1364/AO.43.003037
    1. Gagnon L., Gauthier C., Hoge R. D., Lesage F., Selb J., Boas D. A., “Double-layer estimation of intra- and extracerebral hemoglobin concentration with a time-resolved system,” J. Biomed. Opt. 13(5), 054019 (2008).10.1117/1.2982524
    1. Steinbrink J., Wabnitz H., Obrig H., Villringer A., Rinneberg H., “Determining changes in NIR absorption using a layered model of the human head,” Phys. Med. Biol. 46(3), 879–896 (2001).10.1088/0031-9155/46/3/320
    1. Elliott J. T., Diop M., Tichauer K. M., Lee T.-Y., St. Lawrence K., “Quantitative measurement of cerebral blood flow in a juvenile porcine model by depth-resolved near-infrared spectroscopy,” J. Biomed. Opt. 15(3), 037014 (2010).10.1117/1.3449579
    1. Delpy D. T., Cope M., van der Zee P., Arridge S., Wray S., Wyatt J., “Estimation of optical pathlength through tissue from direct time of flight measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).10.1088/0031-9155/33/12/008
    1. Patterson M. S., Chance B., Wilson B. C., “Time resolved reflectance and transmittance for the non-invasive measurement of tissue optical properties,” Appl. Opt. 28(12), 2331–2336 (1989).10.1364/AO.28.002331
    1. Montcel B., Chabrier R., Poulet P., “Detection of cortical activation with time-resolved diffuse optical methods,” Appl. Opt. 44(10), 1942–1947 (2005).10.1364/AO.44.001942
    1. Kienle A., Patterson M. S., Dögnitz N., Bays R., Wagniνres G., van den Bergh H., “Noninvasive determination of the optical properties of two-layered turbid media,” Appl. Opt. 37(4), 779–791 (1998).10.1364/AO.37.000779
    1. Selb J., Stott J. J., Franceschini M. A., Sorensen A. G., Boas D. A., “Improved sensitivity to cerebral hemodynamics during brain activation with a time-gated optical system: analytical model and experimental validation,” J. Biomed. Opt. 10(1), 011013 (2005).10.1117/1.1852553
    1. Montcel B., Chabrier R., Poulet P., “Time-resolved absorption and hemoglobin concentration difference maps: a method to retrieve depth-related information on cerebral hemodynamics,” Opt. Express 14(25), 12271–12287 (2006).10.1364/OE.14.012271
    1. Hiraoka M., Firbank M., Essenpreis M., Cope M., Arridge S. R., van der Zee P., Delpy D. T., “A Monte Carlo investigation of optical pathlength in inhomogeneous tissue and its application to near-infrared spectroscopy,” Phys. Med. Biol. 38(12), 1859–1876 (1993).10.1088/0031-9155/38/12/011
    1. Contini D., Torricelli A., Pifferi A., Spinelli L., Paglia F., Cubeddu R., “Multi-channel time-resolved system for functional near infrared spectroscopy,” Opt. Express 14(12), 5418–5432 (2006).10.1364/OE.14.005418
    1. Liebert A., Wabnitz H., Grosenick D., Macdonald R., “Fiber dispersion in time domain measurements compromising the accuracy of determination of optical properties of strongly scattering media,” J. Biomed. Opt. 8(3), 512–516 (2003).10.1117/1.1578088
    1. P. C. Hansen, Discrete Inverse Problems: Insight and Algorithms (Society for Industrial and Applied Mathematics, 2010).
    1. G. Bodi and Y. Bérubé-Lauzière, “A new deconvolution technique for time-domain signals in diffuse optical tomography without a priori information,” in European Conference on Biomedical Optics (ECBO), R. Cubeddu, and A. Hielscher, eds. (Optical Society of America, Munich, Germany, 2009), p. 7369.
    1. Diop M., St. Lawrence K., “Deconvolution method for recovering the photon time-of-flight distribution from time-resolved measurements,” Opt. Lett. 37(12), 2358–2360 (2012).10.1364/OL.37.002358
    1. Diop M., Tichauer K. M., Elliott J. T., Migueis M., Lee T.-Y., St. Lawrence K., “Comparison of time-resolved and continuous-wave near-infrared techniques for measuring cerebral blood flow in piglets,” J. Biomed. Opt. 15(5), 057004 (2010).10.1117/1.3488626
    1. Dehghani H., Eames M. E., Yalavarthy P. K., Davis S. C., Srinivasan S., Carpenter C. M., Pogue B. W., Paulsen K. D., “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Methods Eng. 25(6), 711–732 (2009).10.1002/cnm.1162
    1. Elliott J. T., Milej D., Gerega A., Weigl W., Diop M., Morrison L. B., Lee T.-Y., Liebert A., St. Lawrence K., “Variance of time-of-flight distribution is sensitive to cerebral blood flow as demonstrated by ICG bolus-tracking measurements in adult pigs,” Biomed. Opt. Express 4(2), 206–218 (2013).10.1364/BOE.4.000206
    1. Kienle A., Patterson M. S., “Improved solutions of the steady-state and the time-resolved diffusion equations for reflectance from a semi-infinite turbid medium,” J. Opt. Soc. Am. A 14(1), 246–254 (1997).10.1364/JOSAA.14.000246
    1. Diop M., Tichauer K. M., Elliott J. T., Migueis M., Lee T.-Y., St. Lawrence K., “Time-resolved near-infrared technique for bedside monitoring of absolute cerebral blood flow,” Proc. SPIE 7555, 75550Z (2010).10.1117/12.842521
    1. Ntziachristos V., Ma X., Yodh A. G., Chance B., “Multichannel photon counting instrument for spatially resolved near infrared spectroscopy,” Rev. Sci. Instrum. 70(1), 193–201 (1999).10.1063/1.1149565
    1. Mäkitalo M., Foi A., “Optimal inversion of the Anscombe transformation in low-count Poisson image denoising,” IEEE Trans. Image Process. 20(1), 99–109 (2011).10.1109/TIP.2010.2056693
    1. Hansen P. C., “Regularization Tools Version 4.0 for Matlab 7.3,” Numer. Algorithms 46(2), 189–194 (2007).10.1007/s11075-007-9136-9
    1. Ntziachristos V., Chance B., “Accuracy limits in the determination of absolute optical properties using time-resolved NIR spectroscopy,” Med. Phys. 28(6), 1115–1124 (2001).10.1118/1.1373674
    1. Hervé L., Puszka A., Planat-Chrétien A., Dinten J. M., “Time-domain diffuse optical tomography processing by using the Mellin-Laplace transform,” Appl. Opt. 51(25), 5978–5988 (2012).10.1364/AO.51.005978
    1. H. Wabnitz, A. Liebert, D. Contini, L. Spinelli, and A. Torricelli, “Depth selectivity in time-domain optical brain imaging based on time windows and moments of time-of-flight distributions,” in Biomedical Optics, OSA Technical Digest (Optical Society of America, St. Petersburg, Florida, 2008), p. BMD9.
    1. H. Wabnitz, A. Jelzow, M. Mazurenka, O. Steinkellner, R. Macdonald, A. Pifferi, A. Torricelli, D. Contini, L. Zucchelli, L. Spinelli, R. Cubeddu, D. Milej, N. Zolek, M. Kacprzak, A. Liebert, S. Magazov, J. Hebden, F. Martelli, P. Di Ninni, and G. Zaccanti, “Performance Assessment of Time-Domain Optical Brain Imagers: The nEUROPt Protocol,” in Biomedical Optics and 3D Imaging (Miami, 2012).
    1. M. Diop and K. St. Lawrence, “A deconvolution method for recovering tissue impulse response from time-resolved measurements,” in Biomedical Optics (BIOMED) (Miami, 2012).
    1. Davis S. L., Fadel P. J., Cui J., Thomas G. D., Crandall C. G., “Skin blood flow influences near-infrared spectroscopy-derived measurements of tissue oxygenation during heat stress,” J. Appl. Physiol. 100(1), 221–224 (2006).10.1152/japplphysiol.00867.2005

Source: PubMed

Sponsor e collaboratori

Condizioni mediche

Interventi farmacologici

3
Sottoscrivi