laminar optical tomography: high-resolution 3d functional ......laminar optical tomography:...

Laminar optical tomography: high-resolution 3D functional imaging of superficial tissues

Elizabeth M. C Hillman*a, Anna Devora, Andrew K. Dunnb, David A. Boasa aAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital,

Harvard Medical School, CNY 149, 13th Street, Charlestown MA 02129. bDepartment of Biomedical Engineering, University of Texas at Austin, 1 University

Station, C0800, Austin, TX 78712

ABSTRACT Laminar Optical Tomography (LOT) is a new medical imaging modality for high-resolution, depth-resolved,

functional imaging of superficial tissue such as rodent cortex, skin and the retina. LOT uses visible laser light to image to depths of >2mm (far deeper than microscopy) and is highly sensitive to absorption and fluorescence contrast, enabling spectroscopic functional information such as hemoglobin oxygenation to be imaged with 100-200 micron resolution.

LOT has been used to image the hemodynamic response to stimulus in the somatosensory cortex of rats. The resulting three-dimensional (3D) images through the depth of the cortex can be used to delineate the arterial, capillary and venous responses, revealing new information about the intricacies of the oxygenation and blood flow dynamics related to neuronal activation. Additional applications of LOT are being explored, including the integration of 3D Voltage Sensitive Dye fluorescence imaging.

LOT imaging uses a system similar to a confocal microscope, quickly scanning a focused beam of light over the surface of the tissue (~8Hz frame rate). Light is detected from both the focus of the scanning beam, and also at increasing distances from the beam’s focus. This scattered light has penetrated more deeply into the tissue, and allows features at different depths to be distinguished. An algorithm that includes photon migration modeling of light scattering converts the raw data into 3D images. The motivation for functional optical imaging will be outlined, the basic principles of LOT imaging will be described, and the latest in-vivo results will be presented.

Keywords: Optical Imaging, Functional imaging, Depth-resolved, Brain, Cortex, High-resolution, hemoglobin

1. INTRODUCTION

Optical imaging provides unparalleled sensitivity to functional parameters such as hemoglobin oxygenation, membrane potential and metabolic processes. In-vivo optical imaging of superficial tissues using CCD cameras has provided valuable insights into the underlying physiology of both healthy and diseased tissues. However, CCD-based imaging allows only two-dimensional (2D) imaging of the surface of the tissue. Such 2D images constitute a superficially weighted sum of signals from the first few hundred microns of tissue depth, and provide no way to distinguish whether observed features are shallow or deep. Laminar Optical Tomography (LOT) is a new optical imaging modality which allows high-resolution, depth-resolved optical imaging of tissue to depths of >2mm, with resolution of 100-200 microns, at ~8Hz frame rate. Since LOT is a completely non-contact technique, additional imaging or point measurements can be made simultaneously, such as electrophysiology recordings or speckle-flow imaging 1.

To date, LOT has been used to image depth-resolved hemodynamic functional activation in the brains of rats undergoing somatosensory stimulus. The hemodynamic response is imaged using two wavelengths of light (473nm and 532nm) to produce 3D images of oxy-, deoxy- and total hemoglobin (HbO, HbR and HbT) concentration changes. Imaging the hemodynamic response in rat cortex allows the underlying mechanisms of brain activation in both healthy and disease-model animals to be examined in a controlled way. *[email protected] ; phone: 617 643 1917; fax 617 726 7422; www.nmr.mgh.harvard.edu/~ehillman

Invited Paper

Medical Imaging 2006: Physiology, Function, and Structure from Medical Images, edited by Armando Manduca, Amir A. Amini, Proc. of SPIE Vol. 6143,

61431M, (2006) · 1605-7422/06/$15 · doi: 10.1117/12.655876

Proc. of SPIE Vol. 6143 61431M-1

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CCD-based 2D imaging provides an important tool for basic research 2, 3. However, the cortex is an intrinsically 3D structure both in terms of its neuronal organization, and its vascular architecture. Neurons and their dendritic arborizations are organized in layers, with connections to other parts of the brain such as the thalamus at distinct depths. The vascularization of the brain consists of a network of large arteries and veins on the surface of the cortex, which branch and dive perpendicularly to the surface to deeper capillary beds. When imaging of the cortical surface is performed using a camera, there is no way to distinguish between signals originating from deeper cortical layers or superficial structures. Not only does this prevent detailed analysis of the layer-specific dynamics, it also affects the ability to quantitatively analyze the observed data. In rats, the somatosensory cortex is around 2mm thick. LOT has been used to image the hemodynamic response to stimulus of rats through the thickness of the cortex. It was found that LOT gave sufficient separation between superficial and deeper signals, that it was possible to spatio-temporally separate the contributions to the hemodynamic response from arteriolar, capillary and venous vascular compartments.

This paper begins by explaining the LOT method in terms of its hardware and image reconstruction algorithm. Performance of LOT imaging is then demonstrated using a phantom consisting of a human hair at varying depths in an absorbing and scattering solution. In-vivo imaging results are then presented, demonstrating LOT’s ability to resolve the depth-dependence of the hemodynamic response in the cortex. Finally, future directions of LOT development are discussed, including its potential for use as a fluorescence imaging tool, and also for depth-resolved functional imaging of other stratified tissues such as skin, the retina and endothelial tissues.

2. METHOD 2.1. Optical design

LOT uses a system similar in design to a confocal microscope, raster scanning a focused laser beam over the surface of the tissue being imaged. However, unlike confocal microscopy, LOT does not achieve its depth-resolution by scanning the z-position of the beam’s focus. Instead, LOT detects both confocal and multiply scattered light. Light that has been multiply scattered emerges a distance away from the focus of the scanning spot. The further away that the light emerges, the deeper on average it has traveled. LOT measures this scattered light at 7 different distances away from the scanning spot. In this way, LOT has seven different pieces of information for each spot scanned, each with a differently weighted depth-sensitivity. These measurements can be combined with an image reconstruction algorithm which incorporates a mathematical model of light propagation in scattering tissue to convert raw measurements into 3D images. The measurement geometry achieved using LOT is depicted in Figure 1.

Figure 1. LOT scanning measurement geometry. Light from successively increasing distances away from the focused spot is

measured as the spot raster scans the tissue.

The confocal-type design of the LOT system is shown in Figure 2. Light from one of two lasers is emitted from an optical fiber and collimated. This light passes through a polarizing beam splitter and onto galvanometer scanning mirrors which steer the collimated beam through a scan lens. The scan lens focuses the beam at an intermediate image plane, which is imaged onto the surface of the tissue using an objective lens. Light remitted from the tissue then passes back up through the objective, through the scan lens and is de-scanned by the galvanometers. Since the incident laser light was strongly polarized, specular reflections from optics and from the surface of the tissue will maintain this polarization. However, light which has been multiply scattered should quickly lose its original polarization. Therefore the light returning from the tissue which is reflected from the polarizing beam splitter should represent around 50% of the scattered light emerging from the sample. The use of a polarizing beam splitter not only provides substantially more


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Figure 2. Laminar Optical Tomography system for depth-resolved hemodynamic imaging of rat cortex. 50x50 image frames can

currently be acquired in 100ms.

Apart from the additional detectors and lack of z-scanning, the LOT system also differs from a confocal microscope in that it is generally operated with 1 x magnification and a low NA objective lens. Also, the focal lengths of lenses and distances between each optical element were carefully optimized using a ray-tracing model of the system written in Matlab. This was required since the off-axis light returning from the imaged tissue does not pass through the system in the same way as confocal light does. The system’s elements needed to be carefully designed to optimize the passage of the off-axis light back to the detection plane and to avoid clipping of the optics.

Optical microscopy techniques such as confocal or multi-photon imaging can provide very high-resolution depth-resolved imaging of tissues. However, such technologies rely on being able to focus a beam of light at the depth being imaged. This intrinsically limits to achievable penetration depths for scanning microscopy to <300 microns for confocal and <500 microns for two-photon imaging in-vivo. By detecting scattered light, LOT is sensitive to changes occurring beyond the scattering limit of tissue (at the expense of resolution).

The principle of LOT is similar to a method proposed by Fridolin and Lindberg 4. However their design required that a fixed source and detector fiber pair be manually translated over the surface of the tissue to be imaged.


The microscope-like design of LOT allows equivalent, but far more rapid, non-contact and versatile measurements to be made. Since LOT is non-contact, it is not necessary to consider coupling efficiencies and surface reflections often problematic when using fibers. Another technique similar to LOT is non-contact imaged diffuse optical tomography (DOT) as described in 5. This approach uses a 2D array of source and detector fibers arranged in a grid pattern that are imaged onto the surface of tissue. This allows non-contact measurements, however it is necessary to actively switch illumination from one source fiber to the next in order to acquire a full data set. This is either time consuming, or requires the expensive solution of a separate laser source to be connected to every fiber. Similarly every detector fiber must be connected to a separate detector, or multiplexed over time. As a result, only 4 detector positions and 12 source positions were measured in 2.8 minutes in5, compared to an adaptable measurement density of well over 2,500 sources and 17,500 detectors with 7 source-detector separations as is achieved by LOT in only 100ms.

The detection of off-axis scattered light for enhanced depth sensitivity in laser scanning confocal microscopy has been described by Elsner et al 6. Applied to imaging the retina, raw off-axis light images were able to reveal sub-surface changes that could not be seen with conventional confocal microscopy. However, these measurements used only a single source-detector separation, and reconstruction of the data to create a 3D image was not attempted.

Optical Coherence Tomography (OCT) is an important tool for depth-resolved imaging of living tissue, and is capable of penetrating beyond a millimeter into scattering tissue with very high resolution. Compared to LOT, OCT suffers from a poor sensitivity to absorption contrast, and cannot be used to measure fluorescence contrast. This is because OCT deliberately isolates only coherently backscattered light. The light detected in OCT has traveled only a very short pathlength through tissue, and hence does not retain significant information about the attenuation experienced. This means that while OCT produces high quality structural images of living tissue, it has not yet been shown to be capable of performing quantitative functional or molecular imaging (some contrast was seen using OCT to image cat cortex, the changes were attributed to changes in backscattering and not absorption 7). LOT can image both absorbing and fluorescent contrast and is hence strongly sensitive to parameters such as hemoglobin oxygenation and can be used to image molecular and environment-sensitive fluorescent probes.

2.2. Image reconstruction

The probable paths traveled by light in scattering tissue can be simulated using the radiative transport equation (RTE). Diffuse Optical Tomography (DOT) is an established technique for imaging large volumes of scattering tissue using near infra-red (NIR) light 8. LOT uses similar image reconstruction approaches to DOT, with the main difference that LOT cannot use the diffusion approximation to the RTE, since the length scales considered are comparable to the scattering length of tissue, and at visible wavelengths, absorption is much higher than at NIR wavelengths. Instead, LOT uses Monte Carlo simulations to predict the sensitivity of its measurements to spatial variations in absorption (or fluorescence). Particular care is also taken to account for the direction of propagation of the light in the tissue (typically ignored when Monte-Carlo of larger tissues is performed) 1, 9. Monte-Carlo simulated sensitivity functions for LOT measurements with separations between 0mm and 1.6mm are shown in Figure 3. Raw data acquired using LOT through thinned rat skull is also shown alongside a CCD camera image of the same region.

The 0mm separation raw data in Figure 3 shows contrast due to the rough surface of the thinned skull and is equivalent to a confocal image of the skull’s surface. As the separation between the source and detector position increases, the raw LOT images gradually reveal the deeper structures of the pial veins and eventually into the parenchyma of the cortex. This gradual increase in depth sensitivity is predicted by the simulated sensitivity functions. While the CCD image clearly reveals the same surface vessels, it provides only a single image from which the relative depths of the structure in the image cannot be determined.

Once the raw data has been acquired, each set of 2D images (as shown in Figure 3) contains contributions from tissue down to depths of 2mm. However, each image consists of a different combination of signals from all layers. It is therefore necessary to perform a 3D image reconstruction / deconvolution procedure to convert the raw data into a 3D distribution corresponding to the tissue’s structure.

The simulated spatial sensitivity functions in Figure 3 are based on the empirically estimated absorbing and scattering properties of the tissue at the imaging wavelength. Once calculated, these sensitivity functions Js,d(r) can be used to map the LOT raw measurements ∆Ms,d into a 3D image of absorption changes ∆µa(r). This can be achieved using Tikhonov regularization as given in equation 1:


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Figure 3. (top) Monte-Carlo-simulated measurement sensitivity distributions of LOT measurements (log scale, NA 0.05). As the effective source-detector separation increases, the sensitivity profile of the measurements becomes gradually deeper. (Bottom)

Corresponding raw LOT images of rat cortex through thinned skull at 532nm (LOT). As the source-detector separations increase, the deeper vasculature of the brain becomes more visible. A CCD image (at 580nm). of the same area is shown for comparison.

3. RESULTS 3.1. Phantom results

In order to test the imaging performance of LOT, a phantom with sub-millimeter absorbing structure was designed and imaged as shown in Figure 4. The phantom consisted of a human hair, suspended horizontally in a tissue-like scattering and absorbing liquid. LOT data was acquired as the hair was sequentially lowered into the liquid.

The raw data in Figure 4 serves to demonstrate how LOT is able to distinguish between objects at different depths. It also represents an empirical validation of the simulated sensitivity functions. A striking feature of the raw data is the apparent presence of 2 hairs at wider source-detector separations, particularly for the shallowest depth of the hair.


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As the source (the focused laser beam) scans over the hair, the light delivered into the tissue is attenuated and a dark stripe appears in the image. As the axially displaced (imaged) detector scans over the hair, the light emerging from the tissue is attenuated and a dark stripe appears. This reciprocal match between the effect of the source and detector scanning over the hair is exactly as predicted by the simulated sensitivity functions. As the hair move deeper, the shape of the two lines becomes more rounded. This matches the cross-section of the sensitivity functions at deeper depths and is due to gradually increasing scattering. At the deepest depth of 1524 microns (indicated by the black line on the sensitivity functions) the hair causes a measurable perturbation in only the wider source-detector separations. The shape of the perturbation is broader and less distinguishable as two lines, which agrees with the behavior of the sensitivity functions. Similar measurements to this using different background contrast, and different perturbation amplitudes, as well as fluorescent and absorbing contrast have shown equally good correspondence to our models of light propagation for each condition.

Figure 4 Raw LOT data acquired on a phantom consisting of a human hair lowered into absorbing and scattering liquid. The phantom

geometry is shown on the right. Far left shows simulated sensitivity functions for each separation corresponding to raw data. Each column of data shows the hair at gradually deeper depths. Each row is a wider separation between imaged source and detector.

It is easy to see that with the help of the simulated sensitivity functions, it is almost possible to estimate the depth of the hair in each case just from looking at the raw data. However, the reconstruction approach given in equation 1 allows the raw data to be converted into 3D images of the absorbing perturbation due to the hair. Figure 5 shows the image reconstruction results for a subset of the raw data shown Figure 4. Images were reconstructed with regularization parameters α that resulted in a peak µa of 0.1mm-1. These images were reconstructed on a 230 x 230 x 100 micron grid.

While LOT has lower resolution than conventional scanning microscopy methods and OCT, it has the advantage that there is no significant physical limit to its depth sensitivity. If the source-detector separations used in


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Figure 5 Human hair phantom reconstructed images of hair (~100µm diameter) positioned at 0, 400µm, 800µm and 1100µm depths

(± 50µm) in µa ~ 0.1mm-1 µs’ ~1mm-1 liquid. ∆µa = 0.075mm-1 isosurfaces shown in x-z view. (from Hillman et al, Opt Lett, 2004).

3.2. In-vivo results

Following validation via phantom imaging, LOT was applied to imaging the hemodynamic response to functional activation in rat somatosensory cortex. Animals were prepared by performing a tracheotomy and placing arterial and venous femoral catheters under isoflurane anesthesia. The scalp was then retracted and the skull thinned to translucency and then covered with a little mineral oil. Electrical stimuli were delivered to the rat’s forepaw providing a stimulus that induces a localized hemodynamic response in the somatosensory cortex. During data collection, the animals were ventilated and received intra-venous alpha-chloralose. Their arterial blood pressure and state of anesthesia were monitored throughout. All animal procedures were reviewed and approved by the Subcommittee on Research Animal Care at Massachusetts General Hospital, where these experiments were performed.

Figure 6 The LOT system illustrating the data collection geometry for rat imaging experiments. Note the 150mm working distance

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Figure 7 shows early in-vivo data acquired using the LOT system. A five second electrical forepaw stimulus was presented consisting of ~1mA 300µs pulses at 3Hz for around 5 seconds, with 27 seconds between stimuli. For this experiment, only the green 532nm laser in the LOT system was used, resulting in imaging sensitivity to total hemoglobin concentration (HbT). This LOT data was in fact acquired simultaneously with CCD data, by placing a CCD camera above the 45 degree mirror shown in figure 1, and replacing the mirror with a 550nm low-pass dichroic filter. The brain was illuminated from the side by 580nm light from a filtered white light source, which provides maps also sensitive to HbT. This set up allowed careful initial comparison of the 2D stimulus response of the brain to the LOT-recorded response. The same 43 repeated stimulus trials were block averaged for both the LOT and the CCD data. This block averaging helps to reduce the influence of physiologic and systematic noise.

The raw LOT data in Figure 7 shows the average peak stimulus response. The timecourses of raw data in the highlighted regions are adjacent. In the baseline raw LOT images from this animal (shown in Figure 3), the 0mm separation data showed structure identified as the rough surface of the skull, where we would not expect to see functional changes. Correspondingly, the 0mm-separation functional response in Figure 7 is only very small, measuring only a small contribution of signal from the very superficial cortex. As the separation between the source and detectors increases, gradually larger, and then smaller amplitude responses are observed. This suggests heterogeneity in the depth-resolved hemodynamic response. The CCD data also shown confirms that very similar signals are observed using LOT compared to the standard approach, and emphasizes the lack of depth-perspective afforded by a 2D image of the cortical surface.

Figure 8 shows a reconstructed 3D image of the raw data in Figure 7. The HbT change is shown as a 25% isosurface. This reconstructed data is in fact a 3D movie of the activation evolution. Therefore, it is also possible to extract the timecourse of the response from any region of the 3D image. The timecourse as a function of depth of the peak response area is shown, both as a scale map and as timecourse plots. These data illustrate that there may be subtle differences in the onset and decay dynamics of the hemodynamic response as a function of depth.

Figure 7 (left) shows raw LOT data acquired at 532nm during functional stimulus (at peak of response), shown as the fractional change from the initial state (see figure 2 for initial state). The timecourse of the changes in each raw data image is shown to the right. These timecourses demonstrate the differing amplitude of the signal seen by different source-detector separations.


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function of depth and time. (right) shows the timecourses of the peak x-y region for each reconstructed depth.

3.3. Dual-wavelength in-vivo results The functional response reconstructed in Figure 8 illustrates the peak HbT response to activation as a fairly

rounded region within the 2mm thick rat cortex. Following acquisition of these initial results, the LOT system was upgraded to acquire at >8 frames per second and to interleave data acquisition at two wavelengths (532nm and 473nm). Where these two laser wavelengths are utilized, LOT images can be converted into 3D maps of ∆HbO2, ∆HbR and ∆HbT (= ∆HbO2 + ∆HbR) using the known absorption spectra of HbO2 and HbR.

Figure 9 shows raw LOT data acquired using the dual wavelength system. 142 stimulus blocks were averaged. 473nm was chosen as the second wavelength in the LOT system because it offered a measurement sensitive to oxy-hemoglobin, which when combined with the HbT-sensitive measurement at 532nm could also provide HbR images. Alternatives light-source choices included red laser diodes (e.g. 635nm), which would have offered enhanced deoxy-hemoglobin sensitivity. However, red wavelengths scatter less in tissue, and are significantly less sensitive to hemoglobin absorption than lower wavelengths. This is usually considered advantageous for in-vivo diffuse optical imaging, where maximized penetration of light is necessary. However, in the case of LOT, increased scatter provides higher levels of light in reflectance geometries. While the penetration depth is slightly less for the blue light than the green, we do not require sensitivity to structures beyond 2mm in depth in this experiment. When testing measurements using a 635nm laser diode, we found very low levels of detected light (since a large amount was forward scattered into deeper tissue), and also lower % changes in measurement per unit hemoglobin concentration change (as expected from the lower absorption coefficient of hemoglobin at higher wavelengths). In addition, the optical components of the LOT system are currently optimized for visible light transmission, and suppression of reflections. For these reasons, 473nm was chosen as the second wavelength for LOT.

The raw LOT data at 473nm and 532nm shown in Figure 9 are quite similar. The most significant difference can be seen in the % change maps at the peak stimulus response. In the 473nm images, more vessel-like structures are visible around the more localized central active region.


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Conversion of multi-wavelength optical data into functional parameters such as HbO and HbR can be complicated by the need to consider the wavelength-dependent pathlength of the light traveled 10. However, using LOT, we are able to convert the data into 3D images of absorption changes at each wavelength, and separately account for the wavelength-dependent differences in the scattering behavior of tissue. We made empirically-based estimates of the baseline absorption and scattering properties of the rat brain at both of our measurement wavelengths, and generated different sensitivity function sets for each wavelength. By using these functions to explicitly convert the raw LOT data into 3D absorption maps, not only is it not necessary to incorporate estimates of pathlength, but partial volume effects are also overcome. Conversion between raw data and hemoglobin concentrations can be achieved either in two steps, (via absorption maps) or directly through a reconstruction that incorporates the calculation of hemoglobin concentrations 11-

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Figure 10 shows the result of converting the raw data in Figure 9 into 3D images of HbR, HbO and HbT. Images are shown as isosurfaces (at 40% of the maximum value over time and space) at three different timepoints after the onset of a 4 second stimulus. Note that the HbR image is showing negative data at 40% of its absolute amplitude. The HbT images look similar to the earlier result shown in Figure 8, revealing a fairly rounded response which grows and shrinks during the stimulus response. However the HbR and HbO responses reveal more interesting shapes and underlying functional changes. The HbO response begins fairly rounded, but around the peak of the response (3.3 seconds) has a protuberance to the side. This same pattern is seen in the HbR image, although the HbR response shows less central changes. Comparing with the raw absolute data, it is possible to see that this protuberance corresponds to the large surface vein leading from the active area. At later times after stimulus cessation, the HbO response is more rounded once again, but the HbR response is strongly localized to the superficial veins.

These observations are consistent with ‘venous washout’, which occurs as a result of the overshoot in the supply of oxygenated blood to the active region relative to the smaller increase in demand for oxygen. The increase in blood flow causes an increase in delivery of oxygenated blood. When this excess oxygen is not used, the more highly oxygenated blood washes into the venous side. Since the veins contain the most HbR to begin with, this results in a much larger relative (negative) change in the concentration of HbR in the venous side, so the venous washout appears most strongly in the HbR signal. The more localized and deeper changes in HbT are likely to originate more from the capillary beds, suggesting an increase in the red blood cell density. Since the in-flowing blood is coming from arterioles, where oxygenation is high, much of the HbT change corresponds to a change in HbO, which is why the HbO response consists of both a rounded and vessel-like component.

Continuing from this work, we performed a more in-depth study to investigate the depth-resolved vascular dynamics involved in the functional response to stimulus. In addition to CCD imaging and higher-resolution dual-wavelength LOT imaging on each rat, vascular casts were created to preserve the 3D microscopic vascular architecture of each rat. These vascular casts were imaged using two-photon microscopy. The results of this study are currently submitted for publication and hence cannot be shown in this abstract. However, the results revealed a means by which signals from the vascular compartments (arterioles, capillaries and veins) involved in the functional response could be independently isolated and spatially resolved using LOT data. Our results revealed organized and discrete 3D regions corresponding to each vascular compartment, and which corresponded well with the vascular architecture seen in the vascular casts from the same animals. We were also able to isolate the functional timecourses of each compartment, a step particularly significant for improving interpretation of both optical and fMRI functional hemodynamic imaging.


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Figure 10 3D LOT images of HbR, HbO and HbT changes during somatosensory stimulus reconstructed from the dual wavelength data shown in Figure 9. Each row shows a different time-point: 1.5, 3.3 and 4.9 seconds after the onset of a 4 second stimulus. 3.3 seconds was approximately the peak of the activation, and 4.9 seconds is 0.9 seconds after stimulus cessation. Superficial venous

structures can be seen in the HbR and (peak) HbO images.

3.4. Future work

We are currently extending our studies of vascular dynamics using a self-built video-rate two-photon microscopy system. In addition, we are performing studies to allow comparison of these results with those of functional Magnetic Resonance Imaging (fMRI), to investigate the impact of our observations on interpretation of the blood oxygen level dependent (BOLD) signal. These experiments involve acquiring simultaneous 2D optical imaging and fMRI of the open cortex during somatosensory stimulus.

We have previously completed detailed neurovascular coupling studies utilizing 2D optical imaging and electrophysiology to explore the relation between the hemodynamic response and the underlying neuronal activity 3, 14 .


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We plan to extend these studies by investigating neurovascular coupling in 3D. We can attempt this in 2 ways: Firstly, we can perform LOT hemodynamic imaging during electrophysiological measurements at the site of the response. Figure 11 shows data acquired using LOT during whisker stimulus. Two different whiskers were sequentially stimulated using piezo-deflectors, resulting in two different regions of the cortex being activated. An electrophysiology electrode was in place throughout the LOT imaging and acquired the neuronal response in the cortex at the same time as the LOT data. CCD data was also acquired prior to the LOT imaging. These results demonstrate that LOT is not only capable of detecting the response to tactile whisker stimulus (which is significantly smaller than forepaw stimulus responses), but can also be performed in the presence of recording electrodes.

Figure 11 Simultaneous LOT and electrophysiology of tactile whisker stimulus. Two different whiskers (δ and D1) were stimulated

sequentially. CCD data was also acquired and agreed well with the LOT localization.


However, electrophysiology recording typically does not provide reliable depth-resolved neuronal responses, since electrodes generally only measure a single site. Depth-resolved electrode arrays are currently bulky and tend to damage the cortex. An alternative approach to studying 3D neurovascular coupling is to use voltage sensitive dyes (VSDs). These dyes are commonly fluorescent, and when applied to the cortex, bind to the membranes of neurons. When a potential change occurs across the membrane, e.g. when the neuron fires, the VSDs change their fluorescence. We have performed 2D voltage sensitivity during somatosensory cortex, and preliminary 3D imaging of fluorescent phantoms using LOT. We plan to extend LOT to allow simultaneous imaging of the 3D VSD fluorescence changes corresponding to neuronal activation, and to simultaneously image the subsequent 3D hemodynamic response to the same stimulus.

4. CONCLUSIONS

LOT is a new technique for medical imaging, allowing high resolution imaging to depths much greater than possible with microscopy, and with enhanced sensitivity to absorption and fluorescence compared to OCT.

LOT has been demonstrated to provide valuable 3D images of functional activation in the rat cortex. The system will soon be extended to enable concurrent imaging of voltage sensitive dye fluorescence in the cortex, to allow the relationship between neuronal and hemodynamic activation to be explored in 3D. LOT is also being used to image 3D electrical activity in the rat heart using VSDs. In addition, future applications of LOT are likely to include retinal, dermal, cervical, endoscopic and molecular imaging.

5. REFERENCES

1. E.M.C. Hillman, D.A. Boas, A.M. Dale, and A.K. Dunn, "Laminar Optical Tomography: demonstration of millimeter-scale depth-resolved imaging in turbid media". Optics Letters, 2004. 29(14): p. 1650-1652.

2. D. Malonek and A. Grinvald, "Interactions Between Electrical Activity and Cortical Microcirculation Revealed by Imaging Spectroscopy: Implications for Functional Brain Mapping". Science, 1996. 272: p. 551-554.

3. A. Devor, A.K. Dunn, M.L. Andermann, I. Ulbert, D.A. Boas, and A.M. Dale, "Coupling of Total Hemoglobin Concentration, Oxygenation, and Neural Activity in Rat Somatosensory Cortex". Neuron, 2003. 39(353-359).

4. I. Fridolin and L.-G. Lindberg, "Optical non-invasive technique for vessel imaging: I. Experimental results". Phys. Med. Biol, 2000. 45: p. 3765–3778.

5. J.P. Culver, T. Durduran, D. Furuya, C. Cheung, J.H. Greenberg, and A.G. Yodh, "Diffuse optical tomography of cerebral blood flow, oxygenation, and metabolism in rat during focal ischemia". J Cereb Blood Flow Metab, 2003. 23(8): p. 911-924.

6. A.E. Elsner, et al., "Multiply scattered light tomography and confocal imaging: detecting neovascularization in age-related macular degeneration". Opt Express, 2000. 7(2): p. 95-106.

7. R.U. Maheswari, H. Takaoka, H. Kadono, R. Honma, and M. Tanifuji, "Novel functional imaging technique from brain surface with optical coherence tomography enabling visualization of depth resolved functional structure in vivo". J. Neurosci. Methods, 2003. 124(1): p. 83–92.

8. S.R. Arridge, "Optical tomography in medical imaging". Inverse Problems, 1999. 15: p. 41-93. 9. A.K. Dunn and D.A. Boas, "Transport-based image reconstruction in turbid media with small source–detector separations".

Optics Letters, 2000. 25(24): p. 1777-1779. 10. M. Kohl, U. Lindauer, G. Royl, M. Kuhl, L. Gold, A. Villringer, and U. Dirnagl, "Physical model for the spectroscopic

analysis of cortical intrinsic optical signals". Phys Med Biol, 2000. 45: p. 3749-3764. 11. E.M.C. Hillman, "Experimental and theoretical investigations of near infrared tomographic imaging methods and clinical

applications (PhD Thesis)", in Department of Medical Physics and Bioengineering, University College. 2002, University of London: London. p. 355.

12. A. Corlu, T. Durduran, R. Choe, M. Schweiger, E.M.C. Hillman, S.R. Arridge, and A.G. Yodh, "Uniqueness and wavelength optimization in continuous-wave multispectral diffuse optical tomography". Optics Letters, 2003. 28(23): p. 2339-2341.

13. A. Li, G. Boverman, Y. Zhang, D. Brooks, E.L. Miller, M.E. Kilmer, Q. Zhang, E.M.C. Hillman, and D.A. Boas, "An optimal linear inverse solution given multiple priors in diffuse optical tomography". Appl Opt, 2005. 44(10): p. 1948-1956.

14. A. Devor, I. Ulbert, A.K. Dunn, S.N. Narayanan, S.R. Jones, M.L. Andermann, D.A. Boas, and A.M. Dale, "Coupling of the cortical hemodynamic response to cortical and thalamic neuronal activity". Proc Nat Acad Sci, 2005. 102(10): p. 3822-3827.


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