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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
Near-infrared spectroscopy (NIRS) is a rapidly evolving non-invasive technique applied to study oxygenation-related processes in biological tissue [1–3] and to facilitate the diagnosis of diseases associated with abnormal oxygen supply to tissue, such as stroke [4], cancer [5], peripheral arterial disease [6], or traumatic brain injury [7]. However, despite the recent impressive growth of NIRS applications - there are 42 NIRS clinical studies currently underway around the world [8] - there are still some drawbacks compared to other imaging modalities (e.g. Computed Tomography, Magnetic Resonance Imaging), i.e. inadequate spatial resolution, limited depth sensitivity, imprecise anatomical localization, contamination by superficial changes. To improve on these shortcomings, NIRS undergoes a transition from a single-point technique employing one or several source-detector pairs towards an optical mapping technique by applying, first of all, large arrays of source-detector pairs [9]. Secondly, two-dimensional (2D) imaging approaches are realized in a variety of different methods, ranging from structured illumination combined with frequency-domain NIRS [10–13] to flying-spot scanning methods [14]. These imaging approaches have already been applied to a variety of tasks, such as measuring optical properties of different types of phantoms and biological tissue [10,12,15,16]. There is a general trend towards non-contact techniques, thereby eliminating all sensor-tissue contact problems characteristic of the conventional fiber-optode based NIRS equipment. Non-contact methods are particularly useful where physical contact causes additional pain to the patient, as in the case of the diagnosis of burn wound severity [17].
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 [18,19] or changes of hemoglobin concentrations during brain activation [20]. Non-contact probes were also developed for diffuse correlation spectroscopy (DCS) [21,22], e.g. to monitor blood flow responses during photodynamic therapy [21], and with combined recording of oxygenation changes [23].
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 [24,25] or by scanning the sample [26].
The recently reported Null Source-Detector Separation (NSDS) NIRS approach [27,28] for the extraction of long-lived deep travelling photons, based on a single-photon avalanche diode (SPAD) operated in fast-gated mode [29,30], has found first applications. A method for interstitial time-of-flight spectroscopy with a single fiber was developed and demonstrated on phantoms [31]. Moreover, improvements in diffuse optical tomography by using the NSDS approach have been recently demonstrated [32]. Successful in-vivo measurements to detect brain activation were performed with short interfiber distance [33]. The NSDS approach has also inspired the development of a non-contact system [34]. 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 non-contact time-domain scanning imager is schematically shown in Fig. 1. It is an evolution of the non-contact single-point system described earlier [34] enabling real-time acquisition of a whole image of late photons at two wavelengths over a large scanning area. Similar to the previous system, the current optical setup was built for polarization-selective detection. For this purpose the linearly polarized light generated by a pulsed supercontinuum (SC) laser source with an acousto-optic tunable filter (AOTF), described in detail below, was additionally cleaned with the help of a polarizer (P, Thorlabs, in Fig. 1). It passed the polarization splitting cube (PSC, CVI Melles Griot) adjusted for maximum transmission of the incident polarization. The lens L1 (f = 200 mm, Thorlabs) defined the spot size on the tissue.
Fig. 1 Schematic of the non-contact setup: GS – galvo scanner; PSC – polarization splitting cube; SC – super continuum laser; L1, L2, L3 – lenses of focal lengths of 200 mm, 300 mm and 35 mm, respectively; F – detection fiber; AOTF – acousto-optic tunable filter; SPAD – single-photon avalanche diode; P – polarizer; M – turning mirror; DG – delay generator; SPC – time-correlated single photon counting module; GVD – galvano controller card.
The photons, diffusely scattered back by the tissue, are randomly polarized [35] 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 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.
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).
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] 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]. We also reduced the influence of the afterpulse-like effect known as “memory effect” [37], 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].
Times of flight of the photons detected by the SPAD were measured by an imaging SPC-150 card and arrays of histograms were accumulated in its internal memory according to the position of the laser beam on the tissue and the wavelength. The wavelength information was transferred from the GVD-120 card via the ‘Laser Routing’ channel, while the beam position was defined by the ‘Line Clock’ and ‘Pixel Clock’ signals (see Fig. 1). Only half of the SPC-150 card memory was allocated for a single image. When the image was completed the SPC-150 card received a ‘Frame Clock’ signal and started recording a new image within the other half of the memory, while the previous image was copied to the PC2 hard drive [39]. This ensured strict timing in the system operation, independent of delays caused by hard drive speed and PC to SPC card handshaking times. The parameters of the GVD-120 and SPC-150 cards, as well as scan parameters, such as scanning speed, area and resolution were set on PC 2 via SPCM software (Becker&Hickl, Germany) [39].
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]
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 equationswhere and are the mean values over the respective wavelength intervals of the molar absorption coefficients for oxy- and deoxyhemoglobin [41], 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.
As the result of this procedure we received two 32x16 pixel images, one for oxy- and one for deoxyhemoglobin, to follow task-related hemodynamic changes in an area of 4 × 4 cm2 on the tissue. To improve the signal-to-noise ratio, we optionally performed a 4x2 pixel binning to obtain 8x8 pixel maps of the block-averaged time traces.
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].
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).
The in-vivo tests were performed in agreement with the regulations for safety and health protection following the principles expressed in the Declaration of Helsinki. Informed consent was obtained from all subjects.
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. 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.
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.
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] – 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.
After successful initial tests on muscle tissue we attempted to measure task-related changes in oxy- and deoxyhemoglobin in the brain. Two well-studied NIRS tasks were chosen, i.e. motor and cognitive stimulation.
Figure 4 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.
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.
The results of cognitive activation in a single subject (female, 24 yr) are presented in Fig. 5. 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.
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.
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 [44,45]. 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 [46,47]. 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.
A significant advantage of the present approach, compared to continuous-wave and even “standard” time-domain brain imaging, is the time-gated recording of late photons at very high count rate which enormously facilitates the detection of deep absorption changes. The successful detection of brain activation indicates that the results on depth sensitivity found in previous phantom measurements [34] can be transferred to the in-vivo situation. However, since late photons travel through deep but also superficial tissue the related signal is not completely free from superficial changes. To achieve depth selectivity, that is to separate deep from superficial variations, detection of early photons would be needed in addition, to probe hemodynamic changes in the skin. Hence, further development of the scanning system is necessary to detect not only late, but also, in a separate measurement channel, early diffusely scattered photons.
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.
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