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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 4, Iss. 10 — Oct. 1, 2013
  • pp: 2257–2268
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Non-contact in vivo diffuse optical imaging using a time-gated scanning system

M. Mazurenka, L. Di Sieno, G. Boso, D. Contini, A. Pifferi, A. Dalla Mora, A. Tosi, H. Wabnitz, and R. Macdonald  »View Author Affiliations


Biomedical Optics Express, Vol. 4, Issue 10, pp. 2257-2268 (2013)
http://dx.doi.org/10.1364/BOE.4.002257


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Abstract

We report on the design and first in vivo tests of a novel non-contact scanning imaging system for time-domain near-infrared spectroscopy. Our system is based on a null source-detector separation approach and utilizes polarization-selective detection and a fast-gated single-photon avalanche diode to record late photons only. The in-vivo tests included the recording of hemodynamics during arm occlusion and two brain activation tasks. Localized and non-localized changes in oxy- and deoxyhemoglobin concentration were detected for motor and cognitive tasks, respectively. The tests demonstrate the feasibility of non-contact imaging of absorption changes in deeper tissues.

© 2013 Optical Society of America

1. Introduction

Recent developments of non-contact approaches did not only include the above mentioned 2D imaging methods, but also various single channel techniques. Non-contact techniques were applied, e.g. to study oxygen saturation in skin or muscle tissue [18

18. A. A. Stratonnikov, N. V. Ermishova, and V. B. Loschenov, “Influence of red laser irradiation on hemoglobin oxygen saturation and blood volume in human skin in vivo,” Proc. SPIE 4257, 57–64 (2001). [CrossRef]

,19

19. M. Niwayama, H. Murata, and S. Shinohara, “Noncontact tissue oxygenation measurement using near-infrared spectroscopy,” Rev. Sci. Instrum. 77(7), 073102 (2006). [CrossRef]

] or changes of hemoglobin concentrations during brain activation [20

20. T. Funane, H. Atsumori, A. Suzuki, and M. Kiguchi, “Noncontact brain activity measurement system based on near-infrared spectroscopy,” Appl. Phys. Lett. 96(12), 123701 (2010). [CrossRef]

]. Non-contact probes were also developed for diffuse correlation spectroscopy (DCS) [21

21. T. L. Becker, A. D. Paquette, K. R. Keymel, B. W. Henderson, and U. Sunar, “Monitoring blood flow responses during topical ALA-PDT,” Biomed. Opt. Express 2(1), 123–130 (2011). [CrossRef] [PubMed]

,22

22. Y. Lin, L. He, Y. Shang, and G. Yu, “Noncontact diffuse correlation spectroscopy for noninvasive deep tissue blood flow measurement,” J. Biomed. Opt. 17(1), 010502 (2012). [CrossRef] [PubMed]

], e.g. to monitor blood flow responses during photodynamic therapy [21

21. T. L. Becker, A. D. Paquette, K. R. Keymel, B. W. Henderson, and U. Sunar, “Monitoring blood flow responses during topical ALA-PDT,” Biomed. Opt. Express 2(1), 123–130 (2011). [CrossRef] [PubMed]

], and with combined recording of oxygenation changes [23

23. T. Li, Y. Lin, Y. Shang, L. He, C. Huang, M. Szabunio, and G. Yu, “Simultaneous measurement of deep tissue blood flow and oxygenation using noncontact diffuse correlation spectroscopy flow-oximeter,” Sci Rep 3, 1358 (2013). [CrossRef] [PubMed]

].

Human functional brain imaging in the non-contact mode requires an enhanced sensitivity to deep absorption changes which can be gained from time-resolved measurements. Several groups employed time-gated intensified CCD cameras to record images, in particular, for late photons, with switching between a number of fixed sources [24

24. I. Sase, A. Takatsuki, J. Seki, T. Yanagida, and A. Seiyama, “Noncontact backscatter-mode near-infrared time-resolved imaging system: preliminary study for functional brain mapping,” J. Biomed. Opt. 11(5), 054006 (2006). [CrossRef] [PubMed]

,25

25. P. Sawosz, M. Kacprzak, N. Zolek, W. Weigl, S. Wojtkiewicz, R. Maniewski, and A. Liebert, “Optical system based on time-gated, intensified charge-coupled device camera for brain imaging studies,” J. Biomed. Opt. 15(6), 066025 (2010). [CrossRef] [PubMed]

] or by scanning the sample [26

26. P. Sawosz, N. Zolek, M. Kacprzak, R. Maniewski, and A. Liebert, “Application of time-gated CCD camera with image intensifier in contactless detection of absorbing inclusions buried in optically turbid medium which mimics local changes in oxygenation of the brain tissue,” Opto-Electron. Rev. 20(4), 309–314 (2012). [CrossRef]

].

The recently reported Null Source-Detector Separation (NSDS) NIRS approach [27

27. A. Torricelli, A. Pifferi, L. Spinelli, R. Cubeddu, F. Martelli, S. Del Bianco, and G. Zaccanti, “Time-resolved reflectance at null source-detector separation: improving contrast and resolution in diffuse optical imaging,” Phys. Rev. Lett. 95(7), 078101 (2005). [CrossRef] [PubMed]

,28

28. A. Pifferi, A. Torricelli, L. Spinelli, D. Contini, R. Cubeddu, F. Martelli, G. Zaccanti, A. Tosi, A. Dalla Mora, F. Zappa, and S. Cova, “Time-resolved diffuse reflectance using small source-detector separation and fast single-photon gating,” Phys. Rev. Lett. 100(13), 138101 (2008). [CrossRef] [PubMed]

] for the extraction of long-lived deep travelling photons, based on a single-photon avalanche diode (SPAD) operated in fast-gated mode [29

29. A. Dalla Mora, A. Tosi, F. Zappa, S. Cova, D. Contini, A. Pifferi, L. Spinelli, A. Torricelli, and R. Cubeddu, “Fast-Gated Single-Photon Avalanche Diode for Wide Dynamic Range Near Infrared Spectroscopy,” J. Sel. Top. Quantum Electron. 16(4), 1023–1030 (2010). [CrossRef]

,30

30. A. Tosi, A. Dalla Mora, F. Zappa, A. Gulinatti, D. Contini, A. Pifferi, L. Spinelli, A. Torricelli, and R. Cubeddu, “Fast-gated single-photon counting technique widens dynamic range and speeds up acquisition time in time-resolved measurements,” Opt. Express 19(11), 10735–10746 (2011). [CrossRef] [PubMed]

], has found first applications. A method for interstitial time-of-flight spectroscopy with a single fiber was developed and demonstrated on phantoms [31

31. E. Alerstam, T. Svensson, S. Andersson-Engels, L. Spinelli, D. Contini, A. Dalla Mora, A. Tosi, F. Zappa, and A. Pifferi, “Single-fiber diffuse optical time-of-flight spectroscopy,” Opt. Lett. 37(14), 2877–2879 (2012). [CrossRef] [PubMed]

]. Moreover, improvements in diffuse optical tomography by using the NSDS approach have been recently demonstrated [32

32. A. Puszka, L. Di Sieno, A. D. Mora, A. Pifferi, D. Contini, G. Boso, A. Tosi, L. Hervé, A. Planat-Chrétien, A. Koenig, and J.-M. Dinten, “Time-resolved diffuse optical tomography using fast-gated single-photon avalanche diodes,” Biomed. Opt. Express 4(8), 1351–1365 (2013). [CrossRef] [PubMed]

]. Successful in-vivo measurements to detect brain activation were performed with short interfiber distance [33

33. L. Di Sieno, D. Contini, A. Dalla Mora, A. Torricelli, L. Spinelli, R. Cubeddu, A. Tosi, G. Boso, and A. Pifferi, “Functional near-infrared spectroscopy at small source-detector distance by means of high dynamic-range fast-gated SPAD acquisitions: first in-vivo measurements,” Proc. SPIE 8804, 880402, 880402-6 (2013). [CrossRef]

]. The NSDS approach has also inspired the development of a non-contact system [34

34. M. Mazurenka, A. Jelzow, H. Wabnitz, D. Contini, L. Spinelli, A. Pifferi, R. Cubeddu, A. D. Mora, A. Tosi, F. Zappa, and R. Macdonald, “Non-contact time-resolved diffuse reflectance imaging at null source-detector separation,” Opt. Express 20(1), 283–290 (2012). [CrossRef] [PubMed]

]. In this paper we describe an instrument based on this approach, capable of real-time image acquisition using a scanning modality and present the results of the first in vivo tests.

2. Experimental setup

Optical setup

The photons, diffusely scattered back by the tissue, are randomly polarized [35

35. V. Sankaran, J. T. Walsh Jr, and D. J. Maitland, “Comparative study of polarized light propagation in biologic tissues,” J. Biomed. Opt. 7(3), 300–306 (2002). [CrossRef] [PubMed]

] and part of them, polarized perpendicular to the incident light, was deflected by the PSC into the detection arm of the system (light green color in Fig. 1). It should be noted that this polarization-sensitive detection efficiently eliminated incident light, directly reflected on the sample or in the optical setup, but also decreased the detection efficiency of diffuse light from the sample by about a factor of 2. The use of separate lenses in both arms (L1, L2) was an additional measure to avoid the occurrence of parasitic laser light in the detection path. However, even if the rejection of early reflections was not complete, the fast-gated detector would strongly reduce their impact on measurements by detecting longer-lived photons only. For detection of diffusely reflected light we imaged a spot of the surface of the tissue directly onto the entrance face of a multimode optical fiber (∅200 µm, 2 m long, NA 0.22, Thorlabs) by means of an image transfer optics consisting of two lenses L2 and L3 (f = 300 mm and f = 35 mm, respectively; Thorlabs). Ray-tracing simulations (WinLensBasic 3D, Qioptic) showed that we image a spot of 1.35 mm in diameter with an effective numerical aperture (NA) of 0.027, limited by the 7 mm size of the galvo scanner mirrors. The output face of the detection fiber was imaged onto the active area (100 µm diameter) of the SPAD by a pair of lenses (f = 4 mm and f = 3.1 mm, Thorlabs).

Laser module

In order to calculate changes in oxy- and deoxyhemoglobin (HbO2, Hb) concentrations, measurements must be performed at two or more different wavelengths and thus require several light sources or a multi-wavelength source. We opted for a SC laser (SC500-6, Fianium Ltd, UK) equipped with an 8-channel AOTF for the NIR spectral range (650 nm to 1100 nm), for switching between two different wavelengths on the µs to ms time scale. Multiplexing of several trains of picoseconds pulses at different wavelengths on the ns time scale which is often applied in time-domain brain imaging is not feasible with detection by a single time-gated detector. Compared to picosecond diode lasers, the pulse width of the SC laser (< 100 ps) is shorter and the achievable output power considerably larger (see below). The SC laser delivers 10.5 W of supercontinuum radiation within a spectrum ranging from 557 nm to >2000 nm.

The AOTF can be programmed for the simultaneous transmission of eight wavelengths (one wavelength per channel). We used this property to maximize the transmitted laser power by stacking eight channels together, thus obtaining wavelength bands of about 30 nm width centered at 760 nm and 860 nm, as shown in Fig. 2
Fig. 2 Spectra of oxy- and deoxyhemoglobin [41] (red and blue curves, respectively); incident power on the sample surface for stacked 8 channels (cyan curve); actual AOTF output spectra stacked for 760 nm and 860 nm, combined (olive curve, arbitrary units).
by the olive-colored curve. The wavelengths around 760 nm and 860 nm were chosen because they are near the maximum of the SC output spectral power curve (Fig. 2, cyan curve). The differences in the molecular absorption coefficient of oxy- and deoxyhemoglobin (see red and blue spectra in Fig. 2) are still large enough at these two center wavelengths to retrieve oxy- and deoxyhemoglobin concentration changes. The AOTF enables fast switching (< 3 μs) between two wavelengths in all eight channels simultaneously. The working wavelengths for the AOTF driver were set using the device software on PC 1 (Fig. 1) before the measurements started. The wavelength switching was triggered by a scanner control PCI card (GVD-120, Becker&Hickl, Germany), set for line-by-line wavelength multiplexing. The SC laser also provided sync pulses for the single photon counting (SPC) module (see Fig. 1). The maximum laser power reaching the medium surface was ~32 mW. Since the pixel dwell time was approximately 1 ms and the step width from pixel to pixel comparable to the diameter of the laser spot, the power density remained far below the maximum permissible exposure for skin.

Scanning module

For simultaneous scanning of the incident beam and the detection spot across the X-Y plane on the surface of the biological tissue we used a galvo scanner (2-axis laser beam deflection unit, Superscan-7, Raylase, Germany, aperture of 7 mm). Each image consisted of 32 × 32 pixels, half of them recorded at 760 nm and half at 860 nm. Due to line-by-line wavelength multiplexing this resulted in recording virtually two 32 × 16 pixel images, one for each wavelength, in one scan.

The mirrors of the galvo scanner were driven by the ramp signals generated by the GVD-120 card. In parallel, the actual frame, line and pixel information was communicated to the imaging time-correlated single photon counting module SPC-150 (Becker&Hickl, Germany) via Pixel Clock, Line Clock and Frame Clock signals (shown in Fig. 1 as P,L,F clock).

Scanning was performed on an area of 4 × 4 cm2 at a rate of one frame per second. The collection time per pixel was ~1 ms. The switching time between both wavelength bands after each line was less than 3 µs.

Detection module, data acquisition and storage

A second generation compact fast-gated SPAD module (Politecnico di Milano, Italy) with embedded gating and signal conditioning circuitry [36

36. G. Boso, A. Dalla Mora, A. Della Frera, and A. Tosi, “Fast-gating of single-photon avalanche diodes with 200 ps transitions and 30 ps timing jitter,” Sens. Actuators A Phys. 191, 61–67 (2013). [CrossRef]

] was used for detection of late photons. The gate delay was adjusted by a home-built transmission-line based delay generator (DG in Fig. 1) with 25 ps steps. To select an appropriate delay, it was first set to zero and the full distribution of times of flight (DTOF) was recorded at reduced laser power. Then, the gate delay was set to 1.5 ns with respect to the maximum of the DTOF and subsequently decreased in small steps, until the count rate of SPC-150 card approached 3⋅106 s−1, e.g. at ~1.3 ns gate delay, at full laser power. In our experiments we did not exceed this limit to avoid substantial non-linear effects, such as e.g. caused by pileup [36

36. G. Boso, A. Dalla Mora, A. Della Frera, and A. Tosi, “Fast-gating of single-photon avalanche diodes with 200 ps transitions and 30 ps timing jitter,” Sens. Actuators A Phys. 191, 61–67 (2013). [CrossRef]

]. We also reduced the influence of the afterpulse-like effect known as “memory effect” [37

37. A. Dalla Mora, D. Contini, A. Pifferi, R. Cubeddu, A. Tosi, and F. Zappa, “Afterpulse-like noise limits dynamic range in time-gated applications of thin-junction silicon single-photon avalanche diode,” Appl. Phys. Lett. 100(24), 241111 (2012). [CrossRef]

], inherent to thin-junction silicon SPADs, by separating source and detector spots on the tissue by 4 mm. In this way we reduced the relative amount of early photons causing this effect. Such source-detector separation is still small enough for the advantages of the NSDS to remain valid [38

38. L. Spinelli, F. Martelli, S. Del Bianco, A. Pifferi, A. Torricelli, R. Cubeddu, and G. Zaccanti, “Absorption and scattering perturbations in homogeneous and layered diffusive media probed by time-resolved reflectance at null source-detector separation,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 74(2), 021919 (2006). [CrossRef] [PubMed]

].

Each frame was stored as a separate file, thus preventing any possible data loss during long-time in vivo measurements, which typically lasted between 20 min and 40 min.

Data analysis

The files, saved for each recorded frame, consisted of 1024 gated DTOFs with a wavelength flag. The data processing was based on a time-window (TW) analysis within the late-photon part of the DTOF that was selected by the electronic gate (width 6 ns). The analysis was performed in MATLAB®.

For each experiment a TW was chosen for which the photons detected were integrated for each DTOF. The selection of this particular time window within the whole gated DTOF was motivated by the aim to represent late photons with a good signal-to-noise ratio and to avoid the influence of residual reflections in the optical path resulting from early photons. The result was a time series of 32x32 pixel intensity images (photon counts) with encoded wavelength information. For further analysis we separated these images into two series of 32x16 pixel images for 760 nm and 860 nm, respectively.

All data were rearranged to yield a time series (on the time scale T of seconds to minutes) for each pixel. Then signals of all trials were added together (block averaging) to improve the signal-to-noise ratio. This averaging is especially important for the brain measurements, where the signal changes are small. The concentration changes of oxygenated and deoxygenated hemoglobin in each pixel were estimated on the basis of the time-resolved (or microscopic) Beer-Lambert law [40

40. Y. Nomura, O. Hazeki, and M. Tamura, “Relationship between time-resolved and non-time-resolved Beer-Lambert law in turbid media,” Phys. Med. Biol. 42(6), 1009–1022 (1997). [CrossRef] [PubMed]

]
IT(t)I0(t)=exp(Δμavt)
(1)
where IT and I0 are the intensities in the activated and baseline states, respectively. In our analysis IT and I0 were obtained as total photon count in the time window under consideration. The time-dependent pathlength is L = vt where v is the speed of light in the medium. Its refractive index was assumed to be 1.4. The time t (on the picosecond time scale) was approximated by taking the time at the center of the TW. Time zero was determined as the maximum position of the DTOF in a reference measurement without gate delay. The absorption change Δμa was assumed to be small. From the absorption changes at the two wavelengths (1, 2), the changes in oxy- and deoxyhemoglobin concentrations (ΔcHbO2, ΔcHb) were retrieved by solving the system of equations
Δμa,1,2=(ε1,2HbO2ΔcHbO2+ε1,2HbΔcHb)ln(10)
(2)
where ε1,2HbO2and ε1,2Hb are the mean values over the respective wavelength intervals of the molar absorption coefficients for oxy- and deoxyhemoglobin [41

41. M. Cope, The development of a near infrared spectroscopy system and its application for non invasive monitoring of cerebral blood and tissue oxygenation in the newborn infant, PhD Thesis, University College London (1991).

], respectively (see Fig. 2).

This simplified approach provides quantitative concentration changes (in µM), however, it involves a number of approximations. Notably, the absorption change is assumed to be homogeneous. In particular, a separation between absorption changes in brain and superficial tissue cannot be achieved, and an absorption change in the brain is underestimated due to the partial pathlength effect.

Measurement paradigms

We performed three types of in vivo experiments with induced changes in oxy- and deoxyhemoglobin concentrations. We started with changes in the skin (Valsalva maneuver), then went deeper to muscle tissue (arterial and venous occlusions), and even deeper to the brain (motor and cognitive tasks). All in vivo measurements were performed on healthy adult subjects. In all three types of tissue we were able to detect oxy- and deoxyhemoglobin concentration changes. In the present paper we focus on the results of arterial occlusion and brain activation. Results of the Valsalva maneuver and venous occlusion were presented elsewhere [42

42. M. Mazurenka, L. Di Sieno, G. Boso, D. Contini, A. Pifferi, A. Dalla Mora, A. Tosi, H. Wabnitz, and R. Macdonald, “A non-contact time-domain scanning brain imaging system: first in-vivo results,” Proc. SPIE 8799, 87990L, 87990L-7 (2013). [CrossRef]

].

To emulate hemodynamic changes in muscle tissue we performed occlusion experiments. The measurements were done on the upper inner side of the forearm while cuff pressure was applied to the upper arm to disturb the blood flow to and from the forearm. The paradigm for arterial occlusion consisted of 128 s of baseline measurements, 96 s of occlusion (250 mmHg), and 128 s of recovery, with no repetitions.

As examples of brain activation, two types of experiments were performed, (i) activation of the left motor cortex during a motor task, (ii) activation of the left frontal lobe by a cognitive task. The motor paradigm consisted of 20 trials of 32 s of right hand finger tapping followed by 32 s of rest. The scan area was centered at the C3 position according to the 10-20 system. The paradigm of the cognitive task was as follows, 32 s of background measurements, then 32 s of brain activation by solving simple math problems, followed by 32 s of rest, the whole cycle repeated 20 times. For this task, the scan area was centered about 5 cm to the left from the center of the forehead. The subjects were resting in supine position, and their head was fixed by a vacuum cushion (B.u.W. Schmidt GmbH, Germany).

3. Results and discussion of the in vivo tests

The results of the arterial occlusion experiment on a female subject (24 yr) recorded at the upper inner side of the forearm are shown in Fig. 3
Fig. 3 Results of an arterial occlusion measurement: Top row – time courses of HbO2 (red line) and Hb (blue line) for three different regions of binned (4 × 2) pixels, marked by white squares on the images below. A sliding average of 5 s was applied. Grey shaded areas mark the time of occlusion. Bottom row: 32 × 16 pixel images of HbO2 (top) and Hb (bottom) recorded at selected times (shown by green lines on time courses), averaged over 5 frames (5 s). The scanned area was a 4 cm x 4 cm square.
. The upper row displays the time courses of the Hb and HbO2 concentration changes for three regions of interest while the lower part of Fig. 3 presents the time-dependent changes of oxy- and deoxyhemoglobin in the whole area scanned, by means of 2D maps at relevant time points. The Hb images display a slightly curved line coming from the top of the image to the bottom, left of the midline of the images, showing elevated values compared to the surrounding tissue (cf. Hb at 140 s, 175 s, 210 s, and 250 s). The raw intensity images (not shown here) displayed this structure even more clearly which we attributed to a superficial vein that was also visible underneath the skin by eye.

First, we discuss the time traces of HbO2 and Hb in the upper part of Fig. 3. The changes in the total photon count (not shown) typically dropped to 40% of the initial level at 760 nm and 50% at 860 nm. Such a large change enabled the retrieval of a signal with reasonable signal-to-noise ratio on a single-trial level. The grey shaded area represents the time of occlusion. As pressure was not built up instantly, but during several seconds (using a hand pump) the behavior of the oxy- and deoxyhemoglobin between 130 s and 140 s resembles the behavior of venous occlusion [43

43. R. Re, D. Contini, M. Caffini, R. Cubeddu, L. Spinelli, and A. Torricelli, “A compact time-resolved system for near infrared spectroscopy based on wavelength space multiplexing,” Rev. Sci. Instrum. 81(11), 113101 (2010). [CrossRef] [PubMed]

] – the concentrations of HbO2 and Hb rise together due to blocked outflow of blood. When the pressure reached a value large enough to occlude the artery, the concentration of oxyhemoglobin started decreasing (panels a and c) while the concentration of deoxyhemoglobin further increased. This behavior is exactly what is expected when there is no inflow of oxyhemoglobin via the artery and the oxyhemoglobin present in the tissue is getting converted into deoxyhemoglobin. In the area of the vein (panel b) the behavior is different. HbO2 was more or less constant whereas Hb increased. At the end of occlusion, the cuff is deflated again during several seconds. While Hb quickly returns to baseline, HbO2 exhibits a marked overshoot before returning at a slower pace.

The 2D images in the lower part of Fig. 3 contain 32 pixels per line but 16 lines per image only since two subsequent lines corresponding to different wavelengths were combined in the analysis. With a square scanned area of 4 cm x 4 cm, the pixel separation is 1.25 mm in X and 2.5 mm in Y direction. No spatial filtering or smoothing was applied.

Both, Hb and HbO2 concentrations are elevated during the whole experiment more or less everywhere. Note that the dark spots on the upper left and lower right of the images are due to black markers fixed to the skin. During occlusion (between 140 s and 210 s) the Hb change is generally larger than the HbO2 change and particularly pronounced in the area of the vein. At 250 s a sudden rise in HbO2 is already visible while Hb is still remaining on a high level. However, a comparison of the Hb images at 210 s and 250 s reveals different dynamics in the regions of the vein (b) and right of it (c). Such local differences might be due to the presence of superficial, but also deeper and thus less resolved vessel structures.

Summarizing the results for arterial occlusion, we observed that the time evolution of the 2D maps exhibits a variety of different features. Their detailed interpretation would require deeper insight into vascular and muscle physiology which is beyond the scope of this work. This example demonstrates the advantages of an imaging approach with high lateral spatial resolution. The clearly heterogeneous behavior of the hemoglobin changes would affect results of NIRS techniques based on a few single optodes or even optode arrays with separations in the centimeter range in an unknown manner.

Figure 4
Fig. 4 Results of motor activation of the brain (for T from 32 s to 64 s). Map of block-averaged time traces of changes of HbO2 (red) and Hb (blue), centered on the left motor cortex (C3). Each pixel of the 4x4 cm2 image corresponds to an area of 5x5 mm2. A sliding average of 5 s was applied to the block-averaged traces. Error bars illustrate the variability over the repetitions (see text). The magenta square shows the localization of the response.
illustrates the results of the motor activation experiment for a subject with an almost bald head (male, 52 yr). The primary data was binned for 4 pixels in X and 2 pixels in Y direction, resulting in 8x8 pixel images. The error bars correspond to the standard deviation of the mean values obtained by block averaging, i.e. they characterize the variance of the response across the 20 repetitions, for each pixel and time T independently. In an area above the center of the image (approximate C3 position) a pattern of the time traces is observed as is expected for a cerebral activation, i.e. an increase in oxy- and a (smaller) decrease in deoxyhemoglobin. No such response is visible in the upper left part of the image which also shows traces with good signal-to-noise ratio. The identification of a localized response is another indication that the signal is indeed of cerebral origin, while systemic changes would exhibit a more global behavior. The lower right part of the image is impaired by the presence of noise. The count rate in this area was lower by a factor of four compared to the top area of the image, due to the presence of very short hair. The experience with motor activation measurements on other subjects showed that a useful signal is detectable only if there is absolutely no hair present in the area of detection. Even hair of only a few mm length absorb and scatter too many photons and impede signal levels compared to those from a hairless area.

The results of cognitive activation in a single subject (female, 24 yr) are presented in Fig. 5
Fig. 5 Results of cognitive brain activation by solving simple math tasks (for T from 32 s to 64 s). Map of time traces of changes of HbO2 (red) and Hb (blue) on the left forehead. Each pixel of the 4x4 cm2 image corresponds to an area of 5x5 mm2. Error bars illustrate the variability over the repetitions. A sliding average of 5 s was applied to the block-averaged traces.
. An activation pattern with increased HbO2 and decreased Hb concentration due to the stimulation can be discerned throughout the whole area scanned, with a smaller magnitude of changes in the upper left part of the image. The most pronounced Hb response is found in the upper right part. The signal-to-noise ratio is good apart from the lower row which touched the region of the black eye shield. The comparison between the changes induced by the cognitive task and the standard deviation shows that a significant activation was detected. It should be noted that the major component of variance is photon noise.

By recording late photons only it cannot be excluded that the signals are also affected by a superficial response which is known to be particularly strong with cognitive paradigms and in the HbO2 response [44

44. E. Molteni, D. Contini, M. Caffini, G. Baselli, L. Spinelli, R. Cubeddu, S. Cerutti, A. M. Bianchi, and A. Torricelli, “Load-dependent brain activation assessed by time-domain functional near-infrared spectroscopy during a working memory task with graded levels of difficulty,” J. Biomed. Opt. 17(5), 056005 (2012). [CrossRef] [PubMed]

,45

45. E. Kirilina, A. Jelzow, A. Heine, M. Niessing, H. Wabnitz, R. Brühl, B. Ittermann, A. M. Jacobs, and I. Tachtsidis, “The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy,” Neuroimage 61(1), 70–81 (2012). [CrossRef] [PubMed]

]. Selective sensitivity to changes in the brain could be achieved by combining the different information carried by late and early photons, thus eliminating superficial signals [46

46. J. Selb, J. J. Stott, M. A. Franceschini, A. G. Sorensen, and D. A. Boas, “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). [CrossRef] [PubMed]

,47

47. D. Contini, L. Spinelli, A. Torricelli, A. Pifferi, and R. Cubeddu, “Novel method for depth-resolved brain functional imaging by time-domain NIRS,” Proc. SPIE 6629, 662908, 662908-7 (2007). [CrossRef]

]. Such option was not yet realized in the present setup.

4. Conclusions

We have presented the instrumental setup of our novel time-domain non-contact scanning imager and first results of its successful in-vivo testing. The scanning scheme with a frame rate of 1 s−1 over a 4x4 cm2 area and the quasi-simultaneous acquisition at two wavelengths enabled to record physiological changes in the oxy- and deoxyhemoglobin concentrations. It should be noted that the scanning approach with its inherently sequential measurement and a low duty cycle at each pixel has a potential handicap with respect to signal-to-noise ratio. Nevertheless, the results show a rather good signal quality. In case of large changes as in the arterial occlusion experiment, even single trial data exhibited sufficient signal-to-noise ratio. Typically, brain activation exhibits considerably smaller Hb and HbO2 changes compared to peripheral occlusion. Yet, we could demonstrate that both motor and cognitive activation were clearly detectable on a single-subject level, after block averaging (20 repetitions). Moreover, the results of the measurements were not compromised by involuntary movements of the head fixed in a vacuum cushion. In general, the non-contact scanning technique is capable of tracking movements when markers are attached to the skin within the scan area.

Despite the restriction of applicability to hairless parts of the body only, the non-contact scanning mode is envisaged for applications where high density optical mapping of deep tissues is required or helpful, e.g. if the exact localization of functional activity is not known a priori, like in investigations of cortical plasticity. Moreover, our approach seems promising for the study of peripheral vascular pathologies. Further applications in the growing field of intraoperative diffuse imaging can also be envisaged.

Acknowledgments

The research leading to these results has received funding from the European Community's Seventh Framework Programme [FP7/2007-2013] under grant agreement n° FP7-HEALTH-F5-2008-201076.

References and links

1.

M. Wolf, M. Ferrari, and V. Quaresima, “Progress of near-infrared spectroscopy and topography for brain and muscle clinical applications,” J. Biomed. Opt. 12(6), 062104 (2007). [CrossRef] [PubMed]

2.

J. M. Murkin and M. Arango, “Near-infrared spectroscopy as an index of brain and tissue oxygenation,” Br. J. Anaesth. 103(Suppl 1), i3–i13 (2009). [CrossRef] [PubMed]

3.

M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” Neuroimage 63(2), 921–935 (2012). [CrossRef] [PubMed]

4.

O. Steinkellner, C. Gruber, H. Wabnitz, A. Jelzow, J. Steinbrink, J. B. Fiebach, R. Macdonald, and H. Obrig, “Optical bedside monitoring of cerebral perfusion: technological and methodological advances applied in a study on acute ischemic stroke,” J. Biomed. Opt. 15(6), 061708 (2010). [CrossRef] [PubMed]

5.

H. Rinneberg, D. Grosenick, K. T. Moesta, J. Mucke, B. Gebauer, C. Stroszczynski, H. Wabnitz, M. Moeller, B. Wassermann, and P. M. Schlag, “Scanning time-domain optical mammography: detection and characterization of breast tumors in vivo,” Technol. Cancer Res. Treat. 4(5), 483–496 (2005). [PubMed]

6.

M. A. Khalil, H. K. Kim, I.-K. Kim, M. Flexman, R. Dayal, G. Shrikhande, and A. H. Hielscher, “Dynamic diffuse optical tomography imaging of peripheral arterial disease,” Biomed. Opt. Express 3(9), 2288–2298 (2012). [CrossRef] [PubMed]

7.

I. K. Haitsma and A. I. R. Maas, “Monitoring cerebral oxygenation in traumatic brain injury,” Prog. Brain Res. 161, 207–216 (2007). [CrossRef] [PubMed]

8.

http://clinicaltrials.gov/.

9.

J. C. Hebden, “Advances in optical imaging of the newborn infant brain,” Psychophysiology 40(4), 501–510 (2003). [CrossRef] [PubMed]

10.

D. J. Cuccia, F. Bevilacqua, A. J. Durkin, F. R. Ayers, and B. J. Tromberg, “Quantitation and mapping of tissue optical properties using modulated imaging,” J. Biomed. Opt. 14(2), 024012 (2009). [CrossRef] [PubMed]

11.

J. R. Weber, D. J. Cuccia, A. J. Durkin, and B. J. Tromberg, “Noncontact imaging of absorption and scattering in layered tissue using spatially modulated structured light,” J. Appl. Phys. 105(10), 102028 (2009). [CrossRef]

12.

S. D. Konecky, A. Mazhar, D. Cuccia, A. J. Durkin, J. C. Schotland, and B. J. Tromberg, “Quantitative optical tomography of sub-surface heterogeneities using spatially modulated structured light,” Opt. Express 17(17), 14780–14790 (2009). [CrossRef] [PubMed]

13.

A. Mazhar, D. J. Cuccia, S. Gioux, A. J. Durkin, J. V. Frangioni, and B. J. Tromberg, “Structured illumination enhances resolution and contrast in thick tissue fluorescence imaging,” J. Biomed. Opt. 15(1), 010506 (2010). [CrossRef] [PubMed]

14.

X. Wang, Z. Zhao, W. Becker, T. Troxler, and B. Chance, “Flying spot remote sensing of ICG kinetics of undeformed tissues,” Proc. SPIE 5693, 28–33 (2005). [CrossRef]

15.

R. A. Bolt and J. J. Ten Bosch, “Method for measuring position-dependent volume reflection,” Appl. Opt. 32(24), 4641–4645 (1993). [CrossRef] [PubMed]

16.

A. Kienle, L. Lilge, M. S. Patterson, R. Hibst, R. Steiner, and B. C. Wilson, “Spatially resolved absolute diffuse reflectance measurements for noninvasive determination of the optical scattering and absorption coefficients of biological tissue,” Appl. Opt. 35(13), 2304–2314 (1996). [CrossRef] [PubMed]

17.

M. Kaiser, A. Yafi, M. Cinat, B. Choi, and A. J. Durkin, “Noninvasive assessment of burn wound severity using optical technology: a review of current and future modalities,” Burns 37(3), 377–386 (2011). [CrossRef] [PubMed]

18.

A. A. Stratonnikov, N. V. Ermishova, and V. B. Loschenov, “Influence of red laser irradiation on hemoglobin oxygen saturation and blood volume in human skin in vivo,” Proc. SPIE 4257, 57–64 (2001). [CrossRef]

19.

M. Niwayama, H. Murata, and S. Shinohara, “Noncontact tissue oxygenation measurement using near-infrared spectroscopy,” Rev. Sci. Instrum. 77(7), 073102 (2006). [CrossRef]

20.

T. Funane, H. Atsumori, A. Suzuki, and M. Kiguchi, “Noncontact brain activity measurement system based on near-infrared spectroscopy,” Appl. Phys. Lett. 96(12), 123701 (2010). [CrossRef]

21.

T. L. Becker, A. D. Paquette, K. R. Keymel, B. W. Henderson, and U. Sunar, “Monitoring blood flow responses during topical ALA-PDT,” Biomed. Opt. Express 2(1), 123–130 (2011). [CrossRef] [PubMed]

22.

Y. Lin, L. He, Y. Shang, and G. Yu, “Noncontact diffuse correlation spectroscopy for noninvasive deep tissue blood flow measurement,” J. Biomed. Opt. 17(1), 010502 (2012). [CrossRef] [PubMed]

23.

T. Li, Y. Lin, Y. Shang, L. He, C. Huang, M. Szabunio, and G. Yu, “Simultaneous measurement of deep tissue blood flow and oxygenation using noncontact diffuse correlation spectroscopy flow-oximeter,” Sci Rep 3, 1358 (2013). [CrossRef] [PubMed]

24.

I. Sase, A. Takatsuki, J. Seki, T. Yanagida, and A. Seiyama, “Noncontact backscatter-mode near-infrared time-resolved imaging system: preliminary study for functional brain mapping,” J. Biomed. Opt. 11(5), 054006 (2006). [CrossRef] [PubMed]

25.

P. Sawosz, M. Kacprzak, N. Zolek, W. Weigl, S. Wojtkiewicz, R. Maniewski, and A. Liebert, “Optical system based on time-gated, intensified charge-coupled device camera for brain imaging studies,” J. Biomed. Opt. 15(6), 066025 (2010). [CrossRef] [PubMed]

26.

P. Sawosz, N. Zolek, M. Kacprzak, R. Maniewski, and A. Liebert, “Application of time-gated CCD camera with image intensifier in contactless detection of absorbing inclusions buried in optically turbid medium which mimics local changes in oxygenation of the brain tissue,” Opto-Electron. Rev. 20(4), 309–314 (2012). [CrossRef]

27.

A. Torricelli, A. Pifferi, L. Spinelli, R. Cubeddu, F. Martelli, S. Del Bianco, and G. Zaccanti, “Time-resolved reflectance at null source-detector separation: improving contrast and resolution in diffuse optical imaging,” Phys. Rev. Lett. 95(7), 078101 (2005). [CrossRef] [PubMed]

28.

A. Pifferi, A. Torricelli, L. Spinelli, D. Contini, R. Cubeddu, F. Martelli, G. Zaccanti, A. Tosi, A. Dalla Mora, F. Zappa, and S. Cova, “Time-resolved diffuse reflectance using small source-detector separation and fast single-photon gating,” Phys. Rev. Lett. 100(13), 138101 (2008). [CrossRef] [PubMed]

29.

A. Dalla Mora, A. Tosi, F. Zappa, S. Cova, D. Contini, A. Pifferi, L. Spinelli, A. Torricelli, and R. Cubeddu, “Fast-Gated Single-Photon Avalanche Diode for Wide Dynamic Range Near Infrared Spectroscopy,” J. Sel. Top. Quantum Electron. 16(4), 1023–1030 (2010). [CrossRef]

30.

A. Tosi, A. Dalla Mora, F. Zappa, A. Gulinatti, D. Contini, A. Pifferi, L. Spinelli, A. Torricelli, and R. Cubeddu, “Fast-gated single-photon counting technique widens dynamic range and speeds up acquisition time in time-resolved measurements,” Opt. Express 19(11), 10735–10746 (2011). [CrossRef] [PubMed]

31.

E. Alerstam, T. Svensson, S. Andersson-Engels, L. Spinelli, D. Contini, A. Dalla Mora, A. Tosi, F. Zappa, and A. Pifferi, “Single-fiber diffuse optical time-of-flight spectroscopy,” Opt. Lett. 37(14), 2877–2879 (2012). [CrossRef] [PubMed]

32.

A. Puszka, L. Di Sieno, A. D. Mora, A. Pifferi, D. Contini, G. Boso, A. Tosi, L. Hervé, A. Planat-Chrétien, A. Koenig, and J.-M. Dinten, “Time-resolved diffuse optical tomography using fast-gated single-photon avalanche diodes,” Biomed. Opt. Express 4(8), 1351–1365 (2013). [CrossRef] [PubMed]

33.

L. Di Sieno, D. Contini, A. Dalla Mora, A. Torricelli, L. Spinelli, R. Cubeddu, A. Tosi, G. Boso, and A. Pifferi, “Functional near-infrared spectroscopy at small source-detector distance by means of high dynamic-range fast-gated SPAD acquisitions: first in-vivo measurements,” Proc. SPIE 8804, 880402, 880402-6 (2013). [CrossRef]

34.

M. Mazurenka, A. Jelzow, H. Wabnitz, D. Contini, L. Spinelli, A. Pifferi, R. Cubeddu, A. D. Mora, A. Tosi, F. Zappa, and R. Macdonald, “Non-contact time-resolved diffuse reflectance imaging at null source-detector separation,” Opt. Express 20(1), 283–290 (2012). [CrossRef] [PubMed]

35.

V. Sankaran, J. T. Walsh Jr, and D. J. Maitland, “Comparative study of polarized light propagation in biologic tissues,” J. Biomed. Opt. 7(3), 300–306 (2002). [CrossRef] [PubMed]

36.

G. Boso, A. Dalla Mora, A. Della Frera, and A. Tosi, “Fast-gating of single-photon avalanche diodes with 200 ps transitions and 30 ps timing jitter,” Sens. Actuators A Phys. 191, 61–67 (2013). [CrossRef]

37.

A. Dalla Mora, D. Contini, A. Pifferi, R. Cubeddu, A. Tosi, and F. Zappa, “Afterpulse-like noise limits dynamic range in time-gated applications of thin-junction silicon single-photon avalanche diode,” Appl. Phys. Lett. 100(24), 241111 (2012). [CrossRef]

38.

L. Spinelli, F. Martelli, S. Del Bianco, A. Pifferi, A. Torricelli, R. Cubeddu, and G. Zaccanti, “Absorption and scattering perturbations in homogeneous and layered diffusive media probed by time-resolved reflectance at null source-detector separation,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 74(2), 021919 (2006). [CrossRef] [PubMed]

39.

W. Becker, The bh TCSPC Handbook (Becker & Hickl GmbH, 2012).

40.

Y. Nomura, O. Hazeki, and M. Tamura, “Relationship between time-resolved and non-time-resolved Beer-Lambert law in turbid media,” Phys. Med. Biol. 42(6), 1009–1022 (1997). [CrossRef] [PubMed]

41.

M. Cope, The development of a near infrared spectroscopy system and its application for non invasive monitoring of cerebral blood and tissue oxygenation in the newborn infant, PhD Thesis, University College London (1991).

42.

M. Mazurenka, L. Di Sieno, G. Boso, D. Contini, A. Pifferi, A. Dalla Mora, A. Tosi, H. Wabnitz, and R. Macdonald, “A non-contact time-domain scanning brain imaging system: first in-vivo results,” Proc. SPIE 8799, 87990L, 87990L-7 (2013). [CrossRef]

43.

R. Re, D. Contini, M. Caffini, R. Cubeddu, L. Spinelli, and A. Torricelli, “A compact time-resolved system for near infrared spectroscopy based on wavelength space multiplexing,” Rev. Sci. Instrum. 81(11), 113101 (2010). [CrossRef] [PubMed]

44.

E. Molteni, D. Contini, M. Caffini, G. Baselli, L. Spinelli, R. Cubeddu, S. Cerutti, A. M. Bianchi, and A. Torricelli, “Load-dependent brain activation assessed by time-domain functional near-infrared spectroscopy during a working memory task with graded levels of difficulty,” J. Biomed. Opt. 17(5), 056005 (2012). [CrossRef] [PubMed]

45.

E. Kirilina, A. Jelzow, A. Heine, M. Niessing, H. Wabnitz, R. Brühl, B. Ittermann, A. M. Jacobs, and I. Tachtsidis, “The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy,” Neuroimage 61(1), 70–81 (2012). [CrossRef] [PubMed]

46.

J. Selb, J. J. Stott, M. A. Franceschini, A. G. Sorensen, and D. A. Boas, “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). [CrossRef] [PubMed]

47.

D. Contini, L. Spinelli, A. Torricelli, A. Pifferi, and R. Cubeddu, “Novel method for depth-resolved brain functional imaging by time-domain NIRS,” Proc. SPIE 6629, 662908, 662908-7 (2007). [CrossRef]

OCIS Codes
(120.3890) Instrumentation, measurement, and metrology : Medical optics instrumentation
(170.3880) Medical optics and biotechnology : Medical and biological imaging
(170.5280) Medical optics and biotechnology : Photon migration
(170.6510) Medical optics and biotechnology : Spectroscopy, tissue diagnostics
(170.6920) Medical optics and biotechnology : Time-resolved imaging

ToC Category:
Diffuse Optical Imaging

History
Original Manuscript: August 7, 2013
Revised Manuscript: September 21, 2013
Manuscript Accepted: September 22, 2013
Published: September 26, 2013

Citation
M. Mazurenka, L. Di Sieno, G. Boso, D. Contini, A. Pifferi, A. Dalla Mora, A. Tosi, H. Wabnitz, and R. Macdonald, "Non-contact in vivo diffuse optical imaging using a time-gated scanning system," Biomed. Opt. Express 4, 2257-2268 (2013)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-4-10-2257


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References

  1. M. Wolf, M. Ferrari, and V. Quaresima, “Progress of near-infrared spectroscopy and topography for brain and muscle clinical applications,” J. Biomed. Opt.12(6), 062104 (2007). [CrossRef] [PubMed]
  2. J. M. Murkin and M. Arango, “Near-infrared spectroscopy as an index of brain and tissue oxygenation,” Br. J. Anaesth.103(Suppl 1), i3–i13 (2009). [CrossRef] [PubMed]
  3. M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” Neuroimage63(2), 921–935 (2012). [CrossRef] [PubMed]
  4. O. Steinkellner, C. Gruber, H. Wabnitz, A. Jelzow, J. Steinbrink, J. B. Fiebach, R. Macdonald, and H. Obrig, “Optical bedside monitoring of cerebral perfusion: technological and methodological advances applied in a study on acute ischemic stroke,” J. Biomed. Opt.15(6), 061708 (2010). [CrossRef] [PubMed]
  5. H. Rinneberg, D. Grosenick, K. T. Moesta, J. Mucke, B. Gebauer, C. Stroszczynski, H. Wabnitz, M. Moeller, B. Wassermann, and P. M. Schlag, “Scanning time-domain optical mammography: detection and characterization of breast tumors in vivo,” Technol. Cancer Res. Treat.4(5), 483–496 (2005). [PubMed]
  6. M. A. Khalil, H. K. Kim, I.-K. Kim, M. Flexman, R. Dayal, G. Shrikhande, and A. H. Hielscher, “Dynamic diffuse optical tomography imaging of peripheral arterial disease,” Biomed. Opt. Express3(9), 2288–2298 (2012). [CrossRef] [PubMed]
  7. I. K. Haitsma and A. I. R. Maas, “Monitoring cerebral oxygenation in traumatic brain injury,” Prog. Brain Res.161, 207–216 (2007). [CrossRef] [PubMed]
  8. http://clinicaltrials.gov/ .
  9. J. C. Hebden, “Advances in optical imaging of the newborn infant brain,” Psychophysiology40(4), 501–510 (2003). [CrossRef] [PubMed]
  10. D. J. Cuccia, F. Bevilacqua, A. J. Durkin, F. R. Ayers, and B. J. Tromberg, “Quantitation and mapping of tissue optical properties using modulated imaging,” J. Biomed. Opt.14(2), 024012 (2009). [CrossRef] [PubMed]
  11. J. R. Weber, D. J. Cuccia, A. J. Durkin, and B. J. Tromberg, “Noncontact imaging of absorption and scattering in layered tissue using spatially modulated structured light,” J. Appl. Phys.105(10), 102028 (2009). [CrossRef]
  12. S. D. Konecky, A. Mazhar, D. Cuccia, A. J. Durkin, J. C. Schotland, and B. J. Tromberg, “Quantitative optical tomography of sub-surface heterogeneities using spatially modulated structured light,” Opt. Express17(17), 14780–14790 (2009). [CrossRef] [PubMed]
  13. A. Mazhar, D. J. Cuccia, S. Gioux, A. J. Durkin, J. V. Frangioni, and B. J. Tromberg, “Structured illumination enhances resolution and contrast in thick tissue fluorescence imaging,” J. Biomed. Opt.15(1), 010506 (2010). [CrossRef] [PubMed]
  14. X. Wang, Z. Zhao, W. Becker, T. Troxler, and B. Chance, “Flying spot remote sensing of ICG kinetics of undeformed tissues,” Proc. SPIE5693, 28–33 (2005). [CrossRef]
  15. R. A. Bolt and J. J. Ten Bosch, “Method for measuring position-dependent volume reflection,” Appl. Opt.32(24), 4641–4645 (1993). [CrossRef] [PubMed]
  16. A. Kienle, L. Lilge, M. S. Patterson, R. Hibst, R. Steiner, and B. C. Wilson, “Spatially resolved absolute diffuse reflectance measurements for noninvasive determination of the optical scattering and absorption coefficients of biological tissue,” Appl. Opt.35(13), 2304–2314 (1996). [CrossRef] [PubMed]
  17. M. Kaiser, A. Yafi, M. Cinat, B. Choi, and A. J. Durkin, “Noninvasive assessment of burn wound severity using optical technology: a review of current and future modalities,” Burns37(3), 377–386 (2011). [CrossRef] [PubMed]
  18. A. A. Stratonnikov, N. V. Ermishova, and V. B. Loschenov, “Influence of red laser irradiation on hemoglobin oxygen saturation and blood volume in human skin in vivo,” Proc. SPIE4257, 57–64 (2001). [CrossRef]
  19. M. Niwayama, H. Murata, and S. Shinohara, “Noncontact tissue oxygenation measurement using near-infrared spectroscopy,” Rev. Sci. Instrum.77(7), 073102 (2006). [CrossRef]
  20. T. Funane, H. Atsumori, A. Suzuki, and M. Kiguchi, “Noncontact brain activity measurement system based on near-infrared spectroscopy,” Appl. Phys. Lett.96(12), 123701 (2010). [CrossRef]
  21. T. L. Becker, A. D. Paquette, K. R. Keymel, B. W. Henderson, and U. Sunar, “Monitoring blood flow responses during topical ALA-PDT,” Biomed. Opt. Express2(1), 123–130 (2011). [CrossRef] [PubMed]
  22. Y. Lin, L. He, Y. Shang, and G. Yu, “Noncontact diffuse correlation spectroscopy for noninvasive deep tissue blood flow measurement,” J. Biomed. Opt.17(1), 010502 (2012). [CrossRef] [PubMed]
  23. T. Li, Y. Lin, Y. Shang, L. He, C. Huang, M. Szabunio, and G. Yu, “Simultaneous measurement of deep tissue blood flow and oxygenation using noncontact diffuse correlation spectroscopy flow-oximeter,” Sci Rep3, 1358 (2013). [CrossRef] [PubMed]
  24. I. Sase, A. Takatsuki, J. Seki, T. Yanagida, and A. Seiyama, “Noncontact backscatter-mode near-infrared time-resolved imaging system: preliminary study for functional brain mapping,” J. Biomed. Opt.11(5), 054006 (2006). [CrossRef] [PubMed]
  25. P. Sawosz, M. Kacprzak, N. Zolek, W. Weigl, S. Wojtkiewicz, R. Maniewski, and A. Liebert, “Optical system based on time-gated, intensified charge-coupled device camera for brain imaging studies,” J. Biomed. Opt.15(6), 066025 (2010). [CrossRef] [PubMed]
  26. P. Sawosz, N. Zolek, M. Kacprzak, R. Maniewski, and A. Liebert, “Application of time-gated CCD camera with image intensifier in contactless detection of absorbing inclusions buried in optically turbid medium which mimics local changes in oxygenation of the brain tissue,” Opto-Electron. Rev.20(4), 309–314 (2012). [CrossRef]
  27. A. Torricelli, A. Pifferi, L. Spinelli, R. Cubeddu, F. Martelli, S. Del Bianco, and G. Zaccanti, “Time-resolved reflectance at null source-detector separation: improving contrast and resolution in diffuse optical imaging,” Phys. Rev. Lett.95(7), 078101 (2005). [CrossRef] [PubMed]
  28. A. Pifferi, A. Torricelli, L. Spinelli, D. Contini, R. Cubeddu, F. Martelli, G. Zaccanti, A. Tosi, A. Dalla Mora, F. Zappa, and S. Cova, “Time-resolved diffuse reflectance using small source-detector separation and fast single-photon gating,” Phys. Rev. Lett.100(13), 138101 (2008). [CrossRef] [PubMed]
  29. A. Dalla Mora, A. Tosi, F. Zappa, S. Cova, D. Contini, A. Pifferi, L. Spinelli, A. Torricelli, and R. Cubeddu, “Fast-Gated Single-Photon Avalanche Diode for Wide Dynamic Range Near Infrared Spectroscopy,” J. Sel. Top. Quantum Electron.16(4), 1023–1030 (2010). [CrossRef]
  30. A. Tosi, A. Dalla Mora, F. Zappa, A. Gulinatti, D. Contini, A. Pifferi, L. Spinelli, A. Torricelli, and R. Cubeddu, “Fast-gated single-photon counting technique widens dynamic range and speeds up acquisition time in time-resolved measurements,” Opt. Express19(11), 10735–10746 (2011). [CrossRef] [PubMed]
  31. E. Alerstam, T. Svensson, S. Andersson-Engels, L. Spinelli, D. Contini, A. Dalla Mora, A. Tosi, F. Zappa, and A. Pifferi, “Single-fiber diffuse optical time-of-flight spectroscopy,” Opt. Lett.37(14), 2877–2879 (2012). [CrossRef] [PubMed]
  32. A. Puszka, L. Di Sieno, A. D. Mora, A. Pifferi, D. Contini, G. Boso, A. Tosi, L. Hervé, A. Planat-Chrétien, A. Koenig, and J.-M. Dinten, “Time-resolved diffuse optical tomography using fast-gated single-photon avalanche diodes,” Biomed. Opt. Express4(8), 1351–1365 (2013). [CrossRef] [PubMed]
  33. L. Di Sieno, D. Contini, A. Dalla Mora, A. Torricelli, L. Spinelli, R. Cubeddu, A. Tosi, G. Boso, and A. Pifferi, “Functional near-infrared spectroscopy at small source-detector distance by means of high dynamic-range fast-gated SPAD acquisitions: first in-vivo measurements,” Proc. SPIE8804, 880402, 880402-6 (2013). [CrossRef]
  34. M. Mazurenka, A. Jelzow, H. Wabnitz, D. Contini, L. Spinelli, A. Pifferi, R. Cubeddu, A. D. Mora, A. Tosi, F. Zappa, and R. Macdonald, “Non-contact time-resolved diffuse reflectance imaging at null source-detector separation,” Opt. Express20(1), 283–290 (2012). [CrossRef] [PubMed]
  35. V. Sankaran, J. T. Walsh, and D. J. Maitland, “Comparative study of polarized light propagation in biologic tissues,” J. Biomed. Opt.7(3), 300–306 (2002). [CrossRef] [PubMed]
  36. G. Boso, A. Dalla Mora, A. Della Frera, and A. Tosi, “Fast-gating of single-photon avalanche diodes with 200 ps transitions and 30 ps timing jitter,” Sens. Actuators A Phys.191, 61–67 (2013). [CrossRef]
  37. A. Dalla Mora, D. Contini, A. Pifferi, R. Cubeddu, A. Tosi, and F. Zappa, “Afterpulse-like noise limits dynamic range in time-gated applications of thin-junction silicon single-photon avalanche diode,” Appl. Phys. Lett.100(24), 241111 (2012). [CrossRef]
  38. L. Spinelli, F. Martelli, S. Del Bianco, A. Pifferi, A. Torricelli, R. Cubeddu, and G. Zaccanti, “Absorption and scattering perturbations in homogeneous and layered diffusive media probed by time-resolved reflectance at null source-detector separation,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys.74(2), 021919 (2006). [CrossRef] [PubMed]
  39. W. Becker, The bh TCSPC Handbook (Becker & Hickl GmbH, 2012).
  40. Y. Nomura, O. Hazeki, and M. Tamura, “Relationship between time-resolved and non-time-resolved Beer-Lambert law in turbid media,” Phys. Med. Biol.42(6), 1009–1022 (1997). [CrossRef] [PubMed]
  41. M. Cope, The development of a near infrared spectroscopy system and its application for non invasive monitoring of cerebral blood and tissue oxygenation in the newborn infant, PhD Thesis, University College London (1991).
  42. M. Mazurenka, L. Di Sieno, G. Boso, D. Contini, A. Pifferi, A. Dalla Mora, A. Tosi, H. Wabnitz, and R. Macdonald, “A non-contact time-domain scanning brain imaging system: first in-vivo results,” Proc. SPIE8799, 87990L, 87990L-7 (2013). [CrossRef]
  43. R. Re, D. Contini, M. Caffini, R. Cubeddu, L. Spinelli, and A. Torricelli, “A compact time-resolved system for near infrared spectroscopy based on wavelength space multiplexing,” Rev. Sci. Instrum.81(11), 113101 (2010). [CrossRef] [PubMed]
  44. E. Molteni, D. Contini, M. Caffini, G. Baselli, L. Spinelli, R. Cubeddu, S. Cerutti, A. M. Bianchi, and A. Torricelli, “Load-dependent brain activation assessed by time-domain functional near-infrared spectroscopy during a working memory task with graded levels of difficulty,” J. Biomed. Opt.17(5), 056005 (2012). [CrossRef] [PubMed]
  45. E. Kirilina, A. Jelzow, A. Heine, M. Niessing, H. Wabnitz, R. Brühl, B. Ittermann, A. M. Jacobs, and I. Tachtsidis, “The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy,” Neuroimage61(1), 70–81 (2012). [CrossRef] [PubMed]
  46. J. Selb, J. J. Stott, M. A. Franceschini, A. G. Sorensen, and D. A. Boas, “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). [CrossRef] [PubMed]
  47. D. Contini, L. Spinelli, A. Torricelli, A. Pifferi, and R. Cubeddu, “Novel method for depth-resolved brain functional imaging by time-domain NIRS,” Proc. SPIE6629, 662908, 662908-7 (2007). [CrossRef]

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