Optical tomographic imaging of dynamic features of dense-scattering media

Diffuse optical tomography in the human brain: A briefly review from the neurophysiology to its applications

The present work describes the use of noninvasive diffuse optical tomography (DOT) technology to measure hemodynamic changes, providing relevant information which helps to understand the basis of neurophysiology in the human brain. Advantages such as portability, direct measurements of hemoglobin state, temporal resolution, non‐restricted movements as occurs in magnetic resonance imaging (MRI) devices mean that DOT technology can be used in research and clinical fields. In this review we covered the neurophysiology, physical principles underlying optical imaging during tissue‐light interactions, and technology commonly used during the construction of a DOT device including the source‐detector requirements to improve the image quality. DOT provides 3D cerebral activation images due to complex mathematical models which describe the light propagation inside the tissue head. Moreover, we describe briefly the use of Bayesian methods for raw DOT data filtering as an alternative to linear filters widely used in signal processing, avoiding common problems such as the filter selection or a false interpretation of the results which is sometimes due to the interference of background physiological noise with neural activity.

Diffuse optical tomography to measure functional changes during motor tasks: a motor imagery study

The present work shows the spatial reliability of the diffuse optical tomography (DOT) system in a group of healthy subjects during a motor imagery task. Prior to imagery task performance, the subjects executed a motor task based on the finger to thumb opposition for motor training, and to corroborate the DOT spatial localization during the motor execution. DOT technology and data treatment allows us to distinguish oxy- and deoxyhemoglobin at the cerebral gyri level unlike the cerebral activations provided by fMRI series that were processed using different approaches. Here we show the DOT reliability showing functional activations at the cerebral gyri level during motor execution and motor imagery, which provide subtler cerebral activations than the motor execution. These results will allow the use of the DOT system as a monitoring device in a brain computer interface.

Approach to optimize 3-dimensional brain functional activation image with high resolution: a study on functional near-infrared spectroscopy

In this study, 3-dimensional (3-D) enhanced brain-function-map generation and estimation methodology is presented. Optical signals were modelled in the form of numerical optimization problem to infer the best existing waveform of canonical hemodynamic response function. Inter-channel activity patterns were also estimated. The estimation of activation of inter-channel gap depends on the minimization of generalized cross-validation. 3-D brain activation maps were produced through inverse discrete cosine transform. The proposed algorithm acquired significant results for 3-D functional maps with high resolution, in comparison with that of 2-D functional t-maps. A comprehensive analysis by exhibiting images corresponding to several layers has also been appended.

Sparsity-regularized approaches to directly reconstructing hemodynamic response in brain functional diffuse optical tomography

In brain functional diffuse optical tomography, conventional indirect approaches first separately reconstruct the spatial changes in the absorption coefficients at every time point and then calculate the spatial excited levels in terms of hemodynamic models. Direct approaches combine the two steps necessary in the indirect approaches and obtain the spatial excited levels directly. Although reconstruction quality has been improved by the direct approaches to some extent, they still lack sharp edges and suffer from low spatial resolution because of the ill-posedness of the inverse problems. In this paper, a priori sparsity is introduced to obtain the sparse solutions and further improve reconstruction quality. Simulation experiments are conducted to illustrate the expected performance improvements of the proposed approaches.

Is it possible to measure hemodynamic changes in the prefrontal cortex through the frontal sinus using continuous wave DOT systems?

The present work shows the capability of near infrared (NIR) light to reach the cerebral cortex through the frontal sinus using continuous-wave techniques (CW-DOT) in a dual study. On the one hand, changes in time during the tracking of a blood dye in the prefrontal cortex were monitored. On the other hand, hemodynamic changes induced by low frequency of transcranial magnetic stimulation applied on the prefrontal cortex were recorded. The results show how NIR light projected through the frontal sinus reaches the cerebral cortex target, providing enough information to have a reliable measurement of cortical hemodynamic changes using CW-DOT.

Hemoglobin state-flux: A finite-state model representation of the hemoglobin signal for evaluation of the resting state and the influence of disease

SUMMARY

In this report we introduce a weak-model approach for examination of the intrinsic time-varying properties of the hemoglobin signal, with the aim of advancing the application of functional near infrared spectroscopy (fNIRS) for the detection of breast cancer, among other potential uses. The developed methodology integrates concepts from stochastic network theory with known modulatory features of the vascular bed, and in doing so provides access to a previously unrecognized dense feature space that is shown to have promising diagnostic potential. Notable features of the methodology include access to this information solely from measures acquired in the resting state, and analysis of these by treating the various components of the hemoglobin (Hb) signal as a co-varying interacting system.

APPROACH

The principal data-transform kernel projects Hb state-space trajectories onto a coordinate system that constitutes a finite-state representation of covariations among the principal elements of the Hb signal (i.e., its oxygenated (ΔoxyHb) and deoxygenated (ΔdeoxyHb) forms and the associated dependent quantities: total hemoglobin (ΔtotalHb = ΔoxyHb + ΔdeoxyHb), hemoglobin oxygen saturation (ΔHbO2Sat = 100Δ(oxyHb/totalHb)), and tissue-hemoglobin oxygen exchange (ΔHbO2Exc = ΔdeoxyHb-ΔoxyHb)). The resulting ten-state representation treats the evolution of this signal as a one-space, spatiotemporal network that undergoes transitions from one state to another. States of the network are defined by the algebraic signs of the amplitudes of the time-varying components of the Hb signal relative to their temporal mean values. This assignment produces several classes of coefficient arrays, most with a dimension of 10×10.

BIOLOGICAL MOTIVATION

Motivating our approach is the understanding that effector mechanisms that modulate blood delivery to tissue operate on macroscopic scales, in a spatially and temporally varying manner. Also recognized is that this behavior is sensitive to nonlinear actions of these effectors, which include the binding properties of hemoglobin. Accessible phenomenology includes measures of the kinetics and probabilities of network dynamics, which we treat as surrogates for the actions of feedback mechanisms that modulate tissue-vascular coupling.

FINDINGS

Qualitative and quantitative features of this space, and their potential to serve as markers of disease, have been explored by examining continuous-wave fNIRS 3D tomographic time series obtained from the breasts of women who do and do not have breast cancer. Inspection of the coefficient arrays reveals that they are governed predominantly by first-order rate processes, and that each array class exhibits preferred structure that is mainly independent of the others. Discussed are strategies that may serve to extend evaluation of the accessible feature space and how the character of this information holds potential for development of novel clinical and preclinical uses.

Comparing diffuse optical tomography and functional magnetic resonance imaging signals during a cognitive task: pilot study

Diffuse optical tomography (DOT) measures concentration changes in both oxy- and deoxyhemoglobin providing three-dimensional images of local brain activations. A pilot study, which compares both DOT and functional magnetic resonance imaging (fMRI) volumes through t-maps given by canonical statistical parametric mapping (SPM) processing for both data modalities, is presented. The DOT series were processed using a method that is based on a Bayesian filter application on raw DOT data to remove physiological changes and minimum description length application index to select a number of singular values, which reduce the data dimensionality during image reconstruction and adaptation of DOT volume series to normalized standard space. Therefore, statistical analysis is performed with canonical SPM software in the same way as fMRI analysis is done, accepting DOT volumes as if they were fMRI volumes. The results show the reproducibility and ruggedness of the method to process DOT series on group analysis using cognitive paradigms on the prefrontal cortex. Difficulties such as the fact that scalp-brain distances vary between subjects or cerebral activations are difficult to reproduce due to strategies used by the subjects to solve arithmetic problems are considered. T-images given by fMRI and DOT volume series analyzed in SPM show that at the functional level, both DOT and fMRI measures detect the same areas, although DOT provides complementary information to fMRI signals about cerebral activity.

Enhanced resting-state dynamics of the hemoglobin signal as a novel biomarker for detection of breast cancer

PURPOSE

The work presented here demonstrates an application of diffuse optical tomography (DOT) to the problem of breast-cancer diagnosis. The potential for using spatial and temporal variability measures of the hemoglobin signal to identify useful biomarkers was studied.

METHODS

DOT imaging data were collected using two instrumentation platforms the authors developed, which were suitable for exploring tissue dynamics while performing a simultaneous bilateral exam. For each component of the hemoglobin signal (e.g., total, oxygenated), the image time series was reduced to eight scalar metrics that were affected by one or more dynamic properties of the breast microvasculature (e.g., average amplitude, amplitude heterogeneity, strength of spatial coordination). Receiver-operator characteristic (ROC) analyses, comparing groups of subjects with breast cancer to various control groups (i.e., all noncancer subjects, only those with diagnosed benign breast pathology, and only those with no known breast pathology), were performed to evaluate the effect of cancer on the magnitudes of the metrics and of their interbreast differences and ratios.

RESULTS

For women with known breast cancer, simultaneous bilateral DOT breast measures reveal a marked increase in the resting-state amplitude of the vasomotor response in the hemoglobin signal for the affected breast, compared to the contralateral, noncancer breast. Reconstructed 3D spatial maps of observed dynamics also show that this behavior extends well beyond the tumor border. In an effort to identify biomarkers that have the potential to support clinical aims, a group of scalar quantities extracted from the time series measures was systematically examined. This analysis showed that many of the quantities obtained by computing paired responses from the bilateral scans (e.g., interbreast differences, ratios) reveal statistically significant differences between the cancer-positive and -negative subject groups, while the corresponding measures derived from individual breast scans do not. ROC analyses yield area-under-curve values in the 77%-87% range, depending on the metric, with sensitivity and specificity values ranging from 66% to 91%. An interesting result is the initially unexpected finding that the hemodynamic-image metrics are only weakly dependent on the tumor burden, implying that the DOT technique employed is sensitive to tumor-induced changes in the vascular dynamics of the surrounding breast tissue as well. Computational modeling studies serve to identify which properties of the vasomotor response (e.g., average amplitude, amplitude heterogeneity, and phase heterogeneity) principally determine the values of the metrics and their codependences. Findings from the modeling studies also serve to clarify the influence of spatial-response heterogeneity and of system-design limitations, and they reveal the impact that a complex dependence of metric values on the modeled behaviors has on the success in distinguishing between cancer-positive and -negative subjects.

CONCLUSIONS

The authors identified promising hemoglobin-based biomarkers for breast cancer from measures of the resting-state dynamics of the vascular bed. A notable feature of these biomarkers is that their spatial extent encompasses a large fraction of the breast volume, which is mainly independent of tumor size. Tumor-induced induction of nitric oxide synthesis, a well-established concomitant of many breast cancers, is offered as a plausible biological causal factor for the reported findings.

Spatial and Spectral Information in Optical Mammography

This article reviews our research activities in the area of optical mammography and relates them to the historical developments and the current state and trends in the field. The guiding threads for this article are the roles played in optical mammography by spatial and spectral information. The first feature, spatial information, is limited by the diffusive nature of light propagation but can take advantage of the exceptionally high optical contrast featured by blood vessels and blood-rich areas in the breast. We describe a method to correct for edge effects, a spatial second-derivative algorithm, and a two-dimensional phased-array approach that enhance the image contrast, the spatial resolution, and the depth discrimination in optical mammograms. The second feature, spectral information, is the most powerful and unique capability of optical mammography, and allows for functional measurements associated with hemoglobin concentration and oxygenation, water concentration, lipids content, and the wavelength dependence of tissue scattering. We present oxygenation-index images obtained from multi-wavelength optical data that point to the diagnostic potential of oxygenation information in optical mammography. The optimization of the spatial and spectral information in optical mammography has the potential to create a role for this imaging modality in the detection and monitoring of breast cancer.

Nonlinear approach to difference imaging in diffuse optical tomography

Difference imaging aims at recovery of the change in the optical properties of a body based on measurements before and after the change. Conventionally, the image reconstruction is based on using difference of the measurements and a linear approximation of the observation model. One of the main benefits of the linearized difference reconstruction is that the approach has a good tolerance to modeling errors, which cancel out partially in the subtraction of the measurements. However, a drawback of the approach is that the difference images are usually only qualitative in nature and their spatial resolution can be weak because they rely on the global linearization of the nonlinear observation model. To overcome the limitations of the linear approach, we investigate a nonlinear approach for difference imaging where the images of the optical parameters before and after the change are reconstructed simultaneously based on the two datasets. We tested the feasibility of the method with simulations and experimental data from a phantom and studied how the approach tolerates modeling errors like domain truncation, optode coupling errors, and domain shape errors.

Single-trial lie detection using a combined fNIRS-polygraph system

Deception is a human behavior that many people experience in daily life. It involves complex neuronal activities in addition to several physiological changes in the body. A polygraph, which can measure some of the physiological responses from the body, has been widely employed in lie-detection. Many researchers, however, believe that lie detection can become more precise if the neuronal changes that occur in the process of deception can be isolated and measured. In this study, we combine both measures (i.e., physiological and neuronal changes) for enhanced lie-detection. Specifically, to investigate the deception-related hemodynamic response, functional near-infrared spectroscopy (fNIRS) is applied at the prefrontal cortex besides a commercially available polygraph system. A mock crime scenario with a single-trial stimulus is set up as a deception protocol. The acquired data are classified into “true” and “lie” classes based on the fNIRS-based hemoglobin-concentration changes and polygraph-based physiological signal changes. Linear discriminant analysis is utilized as a classifier. The results indicate that the combined fNIRS-polygraph system delivers much higher classification accuracy than that of a singular system. This study demonstrates a plausible solution toward single-trial lie-detection by combining fNIRS and the polygraph.

Comparison of motion correction techniques applied to functional near-infrared spectroscopy data from children

Motion artifacts are the most significant sources of noise in the context of pediatric brain imaging designs and data analyses, especially in applications of functional near-infrared spectroscopy (fNIRS), in which it can completely affect the quality of the data acquired. Different methods have been developed to correct motion artifacts in fNIRS data, but the relative effectiveness of these methods for data from child and infant subjects (which is often found to be significantly noisier than adult data) remains largely unexplored. The issue is further complicated by the heterogeneity of fNIRS data artifacts. We compared the efficacy of the six most prevalent motion artifact correction techniques with fNIRS data acquired from children participating in a language acquisition task, including wavelet, spline interpolation, principal component analysis, moving average (MA), correlation-based signal improvement, and combination of wavelet and MA. The evaluation of five predefined metrics suggests that the MA and wavelet methods yield the best outcomes. These findings elucidate the varied nature of fNIRS data artifacts and the efficacy of artifact correction methods with pediatric populations, as well as help inform both the theory and practice of optical brain imaging analysis.

Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography

Functional near-infrared spectroscopy (fNIRS) is an optical method for noninvasively determining brain activation by estimating changes in the absorption of near-infrared light. Diffuse optical tomography (DOT) extends fNIRS by applying overlapping “high density” measurements, and thus providing a three-dimensional imaging with an improved spatial resolution. Reconstructing brain activation images with DOT requires solving an underdetermined inverse problem with far more unknowns in the volume than in the surface measurements. All methods of solving this type of inverse problem rely on regularization and the choice of corresponding regularization or convergence criteria. While several regularization methods are available, it is unclear how well suited they are for cerebral functional DOT in a semi-infinite geometry. Furthermore, the regularization parameter is often chosen without an independent evaluation, and it may be tempting to choose the solution that matches a hypothesis and rejects the other. In this simulation study, we start out by demonstrating how the quality of cerebral DOT reconstructions is altered with the choice of the regularization parameter for different methods. To independently select the regularization parameter, we propose a cross-validation procedure which achieves a reconstruction quality close to the optimum. Additionally, we compare the outcome of seven different image reconstruction methods for cerebral functional DOT. The methods selected include reconstruction procedures that are already widely used for cerebral DOT [minimum l2-norm estimate (l2MNE) and truncated singular value decomposition], recently proposed sparse reconstruction algorithms [minimum l1- and a smooth minimum l0-norm estimate (l1MNE, l0MNE, respectively)] and a depth- and noise-weighted minimum norm (wMNE). Furthermore, we expand the range of algorithms for DOT by adapting two EEG-source localization algorithms [sparse basis field expansions and linearly constrained minimum variance (LCMV) beamforming]. Independent of the applied noise level, we find that the LCMV beamformer is best for single spot activations with perfect location and focality of the results, whereas the minimum l1-norm estimate succeeds with multiple targets.

Fast optical signals in the sensorimotor cortex: General Linear Convolution Model applied to multiple source-detector distance-based data

In this study, we applied the General Linear Convolution Model to detect fast optical signals (FOS) in the somatosensory cortex, and to study their dependence on the source-detector separation distance (2.0 to 3.5 cm) and irradiated light wavelength (690 and 830 nm). We modeled the impulse response function as a rectangular function that lasted 30 ms, with variable time delay with respect to the stimulus onset. The model was tested in a cohort of 20 healthy volunteers who underwent supra-motor threshold electrical stimulation of the median nerve. The impulse response function quantified the time delay for the maximal response at 70 ms to 110 ms after stimulus onset, in agreement with classical somatosensory-evoked potentials in the literature, previous optical imaging studies based on a grand-average approach, and grand-average based processing. Phase signals at longer wavelength were used to identify FOS for all the source-detector separation distances, but the shortest one. Intensity signals only detected FOS at the greatest distance; i.e., for the largest channel depth. There was no activation for the shorter wavelength light. Correlational analysis between the phase and intensity of FOS further confirmed diffusive rather than optical absorption changes associated with neuronal activity in the activated cortical volume. Our study demonstrates the reliability of our method based on the General Linear Convolution Model for the detection of fast cortical activation through FOS.

Fast optical signal in visual cortex: Improving detection by General Linear Convolution Model

In this study we applied the General Linear Convolution Model to fast optical signals (FOS). We modeled the Impulse Response Function (IRF) as a rectangular function lasting 30ms, with variable time delay with respect to the stimulus onset. Simulated data confirmed the feasibility of this approach and its capability of detecting simulated activations in case of very unfavorable Signal to Noise Ratio (SNR), providing better results than the grand average method. The model was tested in a cohort of 10 healthy volunteers who underwent to hemi-field visual stimulation. Experimental data quantified the IRF time delay at 80-100ms after the stimulus onset, in agreement with classical visual evoked potential literature and previous optical imaging studies based on grand average approach and a larger number of trails. FOS confirmed the expected contralateral activation in the occipital region. Correlational analysis between hemodynamic intensity signal, phase and intensity FOS supports diffusive rather than optical absorption changes associated with neuronal activity in the activated cortical volume. Our study provides a feasible method for detecting fast cortical activations by means of FOS.

Dynamically repairing and replacing neural networks: using hybrid computational and biological tools

The debilitating effects of injury to the nervous system can have a profound effect on daily life activities of the injured person. In this article, we present a project overview in which we are utilizing computational and biological principles, along with simulation and experimentation, to create a realistic computational model of natural and injured sensorimotor control systems. Through the development of hybrid in silico/biological coadaptive symbiotic systems, the goal is to create new technologies that yield transformative neuroprosthetic rehabilitative solutions and a new test bed for the development of integrative medical devices for the repair and enhancement of biological systems.

Diffuser-aided diffuse optical imaging for breast tumor: a feasibility study based on time-resolved three-dimensional Monte Carlo modeling

This study proposed diffuser-aided diffuse optical imaging (DADOI) as a new approach to improve the performance of the conventional diffuse optical tomography (DOT) approach for breast imaging. The 3-D breast model for Monte Carlo simulation is remodeled from clinical MRI image. The modified Beer-Lambert's law is adopted with the DADOI approach to substitute the complex algorithms of inverse problem for mapping of spatial distribution, and the depth information is obtained based on the time-of-flight estimation. The simulation results demonstrate that the time-resolved Monte Carlo method can be capable of performing source-detector separations analysis. The dynamics of photon migration with various source-detector separations are analyzed for the characterization of breast tissue and estimation of optode arrangement. The source-detector separations should be less than 4 cm for breast imaging in DOT system. Meanwhile, the feasibility of DADOI was manifested in this study. In the results, DADOI approach can provide better imaging contrast and faster imaging than conventional DOT measurement. The DADOI approach possesses great potential to detect the breast tumor in early stage and chemotherapy monitoring that implies a good feasibility for clinical application.

fNIRS-based online deception decoding

Deception involves complex neural processes in the brain. Different techniques have been used to study and understand brain mechanisms during deception. Moreover, efforts have been made to develop schemes that can detect and differentiate deception and truth-telling. In this paper, a functional near-infrared spectroscopy (fNIRS)-based online brain deception decoding framework is developed. Deploying dual-wavelength fNIRS, we interrogate 16 locations in the forehead when eight able-bodied adults perform deception and truth-telling scenarios separately. By combining preprocessed oxy-hemoglobin and deoxy-hemoglobin signals, we develop subject-specific classifiers using the support vector machine. Deception and truth-telling states are classified correctly in seven out of eight subjects. A control experiment is also conducted to verify the deception-related hemodynamic response. The average classification accuracy is over 83.44% from these seven subjects. The obtained result suggests that the applicability of fNIRS as a brain imaging technique for online deception detection is very promising.

Optomechanical imaging system for breast cancer detection

Imaging studies of the breast comprise three principal sensing domains: structural, mechanical, and functional. Combinations of these domains can yield either additive or wholly new information, depending on whether one domain interacts with the other. In this report, we describe a new approach to breast imaging based on the interaction between controlled applied mechanical force and tissue hemodynamics. Presented is a description of the system design, performance characteristics, and representative clinical findings for a second-generation dynamic near-infrared optical tomographic breast imager that examines both breasts simultaneously, under conditions of rest and controlled mechanical provocation. The expected capabilities and limitations of the developed system are described in relation to the various sensing domains for breast imaging.

Somatosensory activation of two fingers can be discriminated with ultrahigh-density diffuse optical tomography

Topographic non-invasive near infrared spectroscopy (NIRS) has become a well-established tool for functional brain imaging. Applying up to 100 optodes over the head of a subject, allows achieving a spatial resolution in the centimeter range. This resolution is poor compared to other functional imaging tools. However, recently it was shown that diffuse optical tomography (DOT) as an extension of NIRS based on high-density (HD) probe arrays and supplemented by an advanced image reconstruction procedure allows describing activation patterns with a spatial resolution in the millimeter range. Building on these findings, we hypothesize that HD-DOT may render very focal activations accessible which would be missed by the traditionally used sparse arrays. We examined activation patterns in the primary somatosensory cortex, since its somatotopic organization is very fine-grained. We performed a vibrotactile stimulation study of the first and fifth finger in eight human subjects, using a 900-channel continuous-wave DOT imaging system for achieving a higher resolution than conventional topographic NIRS. To compare the results to a well-established high-resolution imaging technique, the same paradigm was investigated in the same subjects by means of functional magnetic resonance imaging (fMRI). In this work, we tested the advantage of ultrahigh-density probe arrays and show that highly focal activations would be missed by classical next-nearest neighbor NIRS approach, but also by DOT, when using a sparse probe array. Distinct activation patterns for both fingers correlated well with the expected neuroanatomy in five of eight subjects. Additionally we show that activation for different fingers is projected to different tissue depths in the DOT image. Comparison to the fMRI data yielded similar activation foci in seven out of ten finger representations in these five subjects when comparing the lateral localization of DOT and fMRI results.

Contrast enhanced high-resolution diffuse optical tomography of the human brain using ICG

Non-invasive diffuse optical tomography (DOT) of the adult brain has recently been shown to improve the spatial resolution for functional brain imaging applications. Here we show that high-resolution (HR) DOT is also advantageous for clinical perfusion imaging using an optical contrast agent. We present the first HR-DOT results with a continuous wave near infrared spectroscopy setup using a dense grid of optical fibers and indocyanine green (ICG) as an exogenic contrast agent. We find an early arrival of the ICG bolus in the intracerebral tissue and a delayed arrival of the bolus in the extracerebral tissue, achieving the separation of both layers. This demonstrates the method's potential for brain perfusion monitoring in neurointensive care patients.

Hyperspectral image reconstruction for diffuse optical tomography

We explore the development and performance of algorithms for hyperspectral diffuse optical tomography (DOT) for which data from hundreds of wavelengths are collected and used to determine the concentration distribution of chromophores in the medium under investigation. An efficient method is detailed for forming the images using iterative algorithms applied to a linearized Born approximation model assuming the scattering coefficient is spatially constant and known. The L-surface framework is employed to select optimal regularization parameters for the inverse problem. We report image reconstructions using 126 wavelengths with estimation error in simulations as low as 0.05 and mean square error of experimental data of 0.18 and 0.29 for ink and dye concentrations, respectively, an improvement over reconstructions using fewer specifically chosen wavelengths.

Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy

BACKGROUND

Near-infrared spectroscopy (NIRS) is a non-invasive neuroimaging technique that recently has been developed to measure the changes of cerebral blood oxygenation associated with brain activities. To date, for functional brain mapping applications, there is no standard on-line method for analysing NIRS data.

METHODS

In this paper, a novel on-line NIRS data analysis framework taking advantages of both the general linear model (GLM) and the Kalman estimator is devised. The Kalman estimator is used to update the GLM coefficients recursively, and one critical coefficient regarding brain activities is then passed to a t-statistical test. The t-statistical test result is used to update a topographic brain activation map. Meanwhile, a set of high-pass filters is plugged into the GLM to prevent very low-frequency noises, and an autoregressive (AR) model is used to prevent the temporal correlation caused by physiological noises in NIRS time series. A set of data recorded in finger tapping experiments is studied using the proposed framework.

RESULTS

The obtained results suggest that the method can effectively track the task related brain activation areas, and prevent the noise distortion in the estimation while the experiment is running. Thereby, the potential of the proposed method for real-time NIRS-based brain imaging was demonstrated.

CONCLUSIONS

This paper presents a novel on-line approach for analysing NIRS data for functional brain mapping applications. This approach demonstrates the potential of a real-time-updating topographic brain activation map.

High-resolution optical functional mapping of the human somatosensory cortex

Non-invasive optical imaging of brain function has been promoted in a number of fields in which functional magnetic resonance imaging (fMRI) is limited due to constraints induced by the scanning environment. Beyond physiological and psychological research, bedside monitoring and neurorehabilitation may be relevant clinical applications that are yet little explored. A major obstacle to advocate the tool in clinical research is insufficient spatial resolution. Based on a multi-distance high-density optical imaging setup, we here demonstrate a dramatic increase in sensitivity of the method. We show that optical imaging allows for the differentiation between activations of single finger representations in the primary somatosensory cortex (SI). Methodologically our findings confirm results in a pioneering study by Zeff et al. (2007) and extend them to the homuncular organization of SI. After performing a motor task, eight subjects underwent vibrotactile stimulation of the little finger and the thumb. We used a high-density diffuse-optical sensing array in conjunction with optical tomographic reconstruction. Optical imaging disclosed three discrete activation foci one for motor and two discrete foci for vibrotactile stimulation of the first and fifth finger, respectively. The results were co-registered to the individual anatomical brain anatomy (MRI) which confirmed the localization in the expected cortical gyri in four subjects. This advance in spatial resolution opens new perspectives to apply optical imaging in the research on plasticity notably in patients undergoing neurorehabilitation.

Investigation of the prefrontal cortex in response to duration-variable anagram tasks using functional near-infrared spectroscopy

We hypothesize that nonlinearity between short-term anagram tasks and corresponding hemodynamic responses can be observed by functional near-infrared spectroscopy (fNIRS) in the prefrontal cortex (PFC). The PFC of six human subjects in response to anagram tasks is investigated using multichannel fNIRS. Concentration changes of oxyhemoglobin and deoxyhemoglobin in the PFC are measured with variable anagram durations and at two difficulty levels (four- and six-letter anagrams). The durations to perform the selected anagram tasks range from several seconds to more than one minute. The dorsolateral PFC areas exhibit consistent and strong hemodynamic deactivation during and shortly after task execution. The superposition principle of a linear system is employed to investigate nonlinear hemodynamic features among three task duration subgroups: D1 = 2.0 sec, D2 = 4.0 sec, and D3 = 8.0 sec. Such analysis shows clear nonlinearity in hemodynamic responses on the PFC with task durations shorter than 4 sec. Our observation of significant deactivation in early hemodynamic responses in the PFC is consistent with multiple fNIRS studies and several reports given in the field of functional magnetic resonance imaging. A better understanding of nonlinearity in fNIRS signals will have potential for us to investigate brain adaptation and to extrapolate neuronal activities from hemodynamic signals.

Fabrication of indocyanine green encapsulated biodegradable microbubbles for structural and functional imaging of cancer

We developed a novel dual-modal contrast agent for the structural and functional imaging of cancer. The contrast agent was fabricated by encapsulating indocyanine green (ICG) in poly(lactic-co-glycolic acid) (PLGA) microbubbles using a modified double-emulsion method. More stabilized absorption and fluorescence emission characteristics were observed for aqueous and plasma suspensions of ICG-encapsulated microbubbles. The technical feasibility of concurrent structural and functional imaging was demonstrated through a series of benchtop tests in which the aqueous suspension of ICG-encapsulated microbubbles was injected into a transparent tube embedded in an Intralipid phantom at different flow rates and concentrations. Concurrent fluorescence imaging and B-mode ultrasound imaging successfully captured the changes of microbubble flow rate and concentration with high linearity and accuracy. One potential application of the proposed ICG-encapsulated PLGA microbubbles is for the identification and characterization of peritumoral neovasculature for enhanced coregistration between tumor structural and functional boundaries in ultrasound-guided near-infrared diffuse optical tomography.

Higher-order perturbation theory for the diffusion equation in heterogeneous media: application to layered and slab geometries

We apply a previously proposed perturbation theory of the diffusion equation for studying light propagation through heterogeneous media in the presence of absorbing defects. The theory is based on the knowledge of (a) the geometric characteristics of a focal inclusion, (b) the mean optical path length inside the inclusion, and (c) the optical properties of the inclusion. The potential of this method is shown in the layered and slab geometries, where calculations are carried out up to the fourth order. The relative changes of intensity with respect to the unperturbed (heterogeneous) medium are predicted by the theory to within 10% for a wide range of contrasts dDeltamu(a) (up to dDeltamu(a) approximately 0.4-0.8), where d is the effective diameter of the defect and Deltamu(a) the absorption contrast between defect and local background. We also show how the method of Padé approximants can be used to extend the validity of the theory for a larger range of absorption contrasts. Finally, we study the possibility of using the proposed method for calculating the effect of a colocalized scattering and absorbing perturbation.

Near-infrared spectral imaging of the female breast for quantitative oximetry in optical mammography

We present a hybrid continuous-wave, frequency-domain instrument for near-infrared spectral imaging of the female breast based on a tandem, planar scanning of one illumination optical fiber and one collection optical fiber configured in a transmission geometry. The spatial sampling rate of 25 points/cm(2) is increased to 400 points/cm(2) by postprocessing the data with a 2D cubic spline interpolation. We then apply a previously developed spatial second-derivative algorithm to an edge-corrected intensity image (N-image) to enhance the visibility and resolution of optical inhomogeneities in breast tissue such as blood vessels and tumors. The spectral data at each image pixel consist of 515-point spectra over the 650-900 nm wavelength range, thus featuring a spectral density of two data points per nanometer. We process the measured spectra with a paired-wavelength spectral analysis method to quantify the oxygen saturation of detected optical inhomogeneities, under the assumption that they feature a locally higher hemoglobin concentration. Our initial measurements on two healthy human subjects have generated high-resolution optical mammograms displaying a network of blood vessels with values of hemoglobin saturation typically falling within the 60%-95% range, which is physiologically reasonable. This approach to spectral imaging and oximetry of the breast has the potential to efficiently exploit the high intrinsic contrast provided by hemoglobin in breast tissue and to contribute a useful tool in the detection, diagnosis, and monitoring of breast pathologies.

Event-related fast optical signal in a rapid object recognition task: improving detection by the independent component analysis

Noninvasive recording of fast optical signals presumably reflecting neuronal activity is a challenging task because of a relatively low signal-to-noise ratio. To improve detection of those signals in rapid object recognition tasks, we used the independent component analysis (ICA) to reduce "global interference" (heartbeat and contribution of superficial layers). We recorded optical signals from the left prefrontal cortex in 10 right-handed participants with a continuous-wave instrument (DYNOT, NIRx, Brooklyn, NY). Visual stimuli were pictures of urban, landscape and seashore scenes with various vehicles as targets (target-to-non-target ratio 1:6) presented at ISI=166 ms or 250 ms. Subjects mentally counted targets. Data were filtered at 2-30 Hz and artifactual components were identified visually (for heartbeat) and using the ICA weight matrix (for superficial layers). Optical signals were restored from the ICA components with artifactual components removed and then averaged over target and non-target epochs. After ICA processing, the event-related response was detected in 70%-100% of subjects. The refined signal showed a significant decrease from baseline within 200-300 ms after targets and a slight increase after non-targets. The temporal profile of the optical signal corresponded well to the profile of a "differential ERP response", the difference between targets and non-targets which peaks at 200 ms in similar object detection tasks. These results demonstrate that the detection of fast optical responses with continuous-wave instruments can be improved through the ICA method capable to remove noise, global interference and the activity of superficial layers. Fast optical signals may provide further information on brain processing during higher-order cognitive tasks such as rapid categorization of objects.

Dynamic functional and mechanical response of breast tissue to compression

Physiological tissue dynamics following breast compression offer new contrast mechanisms for evaluating breast health and disease with near infrared spectroscopy. We monitored the total hemoglobin concentration and hemoglobin oxygen saturation in 28 healthy female volunteers subject to repeated fractional mammographic compression. The compression induces a reduction in blood flow, in turn causing a reduction in hemoglobin oxygen saturation. At the same time, a two phase tissue viscoelastic relaxation results in a reduction and redistribution of pressure within the tissue and correspondingly modulates the tissue total hemoglobin concentration and oxygen saturation. We observed a strong correlation between the relaxing pressure and changes in the total hemoglobin concentration bearing evidence of the involvement of different vascular compartments. Consequently, we have developed a model that enables us to disentangle these effects and obtain robust estimates of the tissue oxygen consumption and blood flow. We obtain estimates of 1.9+/-1.3 micromol/100 mL/min for OC and 2.8+/-1.7 mL/100 mL/min for blood flow, consistent with other published values.

Inspiratory contrast for in vivo optical imaging

We demonstrate the use of inspired oxygen and carbon dioxide as a possible route to increase contrast in optical imaging of cancerous tissue. Differential imaging in human xenograft rodent models of cancer exhibits significant variation in signal between normal and cancerous tissue. This differential cancer-specific contrast is stronger and more consistent than the conventional static contrast. This differential technique exploits the response of abnormal tumor vasculature to inhaled gases and could provide a promising alternative to supplement mainstream cancer imaging modalities such as x-rays and MRI.

Functional optical signal analysis: a software tool for near-infrared spectroscopy data processing incorporating statistical parametric mapping

Optical topography (OT) relies on the near infrared spectroscopy (NIRS) technique to provide noninvasively a spatial map of functional brain activity. OT has advantages over conventional fMRI in terms of its simple approach to measuring the hemodynamic response, its ability to distinguish between changes in oxy- and deoxy-hemoglobin and the range of human participants that can be readily investigated. We offer a new software tool, functional optical signal analysis (fOSA), for analyzing the spatially resolved optical signals that provides statistical inference capabilities about the distribution of brain activity in space and time and by experimental condition. It does this by mapping the signal into a standard functional neuroimaging analysis software, statistical parametric mapping (SPM), and forms, in effect, a new SPM toolbox specifically designed for NIRS in an OT configuration. The validity of the program has been tested using synthetic data, and its applicability is demonstrated with experimental data.

Adaptive filtering to reduce global interference in evoked brain activity detection: a human subject case study

Following previous Monte Carlo simulations, we describe in detail an example of detecting evoked visual hemodynamic responses in a human subject as a preliminary demonstration of the novel global interference cancellation technology. The raw time series of oxyhemoglobin (O(2)Hb) and deoxyhemoglobin (HHb) changes, their block averaged results before and after adaptive filtering, together with the power spectral density analysis are presented. Simultaneous respiration and EKG recordings suggested that spontaneous low-frequency oscillation and cardiac activity were the major sources of global interference in our test. When global interference dominates (e.g., for O(2)Hb in our data, judged by power spectral density analysis), adaptive filtering effectively reduced this interference, doubling the contrast-to-noise ratio (CNR) for evoked visual response detection. When global interference is not obvious (e.g., in our HHb data), adaptive filtering provided no CNR improvement. The results also showed that the hemodynamic changes in the superficial layers and the estimated total global interference in the target measurement were highly correlated (r=0.96), which explains why this global interference cancellation method should work well when global interference is dominating. In addition, the results suggested that association between the superficial layer hemodynamics and the total global interference is time-varying.

Time-resolved diffuse optical tomography and its application to in vitro and in vivo imaging

This work reviews our research during the past several years on time-resolved (TR) near-infrared diffuse optical tomography (DOT). Following an introduction of the measuring modes, two proposed schemes of image reconstruction in TR-DOT are described: one utilizes the full TR data, and the other, referred to as the modified generalized pulse spectrum technique (GPST), uses the featured data extracted from the TR measurement. The performances of the two algorithms in quantitativeness and spatial resolution are comparatively investigated with 2-D simulated data. TR-DOT images are then presented for phantom experiments, which are obtained by using a 16-channel time-correlated single photon counting system, and the factors affecting the quantification of the reconstruction are discussed. Finally, in vitro and in vivo imaging examples are illustrated for validating the capibility of TR-DOT to provide not only the anatomical but also the physiological information of the objects.

Digital-signal-processor-based dynamic imaging system for optical tomography

In this article, we introduce a dynamic optical tomography system that is, unlike currently available analog instrumentation, based on digital data acquisition and filtering techniques. At the core of this continuous wave instrument is a digital signal processor (DSP) that collects, collates, processes, and filters the digitized data set. The processor is also responsible for managing system timing and the imaging routines which can acquire real-time data at rates as high as 150 Hz. Many of the synchronously timed processes are controlled by a complex programmable logic device that is also used in conjunction with the DSP to orchestrate data flow. The operation of the system is implemented through a comprehensive graphical user interface designed with LABVIEW software which integrates automated calibration, data acquisition, data organization, and signal postprocessing. Performance analysis demonstrates very low system noise (approximately 1 pW rms noise equivalent power), excellent signal precision (<0.04%-0.2%) and long term system stability (<1% over 40 min). A large dynamic range (approximately 190 dB) accommodates a wide scope of measurement geometries and tissue types. First experiments on tissue phantoms show that dynamic behavior is accurately captured and spatial location can be correctly tracked using this system.

Interstitial fluid pressure in soft tissue as a result of an externally applied contact pressure

Manipulation of interstitial fluid pressure (IFP) has a clinical potential when used in conjunction with near-infrared spectroscopy for the detection of breast cancer. In order to better interpret how the applied pressure alters the vascular space and interstitial water volumes in breast tissue, a study on tissue-mimicking, gelatin phantoms was carried out to mimic the translation of external force into internal pressures. A complete set of three-dimensional (3D) pressure maps were obtained for the interior volumes of phantoms as an external force of 10 mmHg was applied, using mixtures of elastic moduli 19 and 33 kPa to simulate adipose and fibroglandular values of breast tissue. Corresponding linear elastic finite element analysis (FEA) cases were formulated. Shear stress, nonlinear mechanical properties, gravity and tissue geometry were all observed to contribute to internal pressure distribution, with surface shear stresses increasing internal pressures near the surface to greater than twice the applied external pressure. Average pressures by depth were predicted by the linear elastic FEA models. FEA models were run for cases mimicking a 93 kPa tumor inclusion within regions of adipose, fibroglandular tissue, and a composite of the two tissue types to illustrate the localized high fluid pressures caused by a tumor when an external force is applied. The conclusion was that external contact forces can generate potentially clinically useful fluid pressure magnitudes in regions of sharp effective elastic modulus gradients, such as tumor boundaries.

Spatio-temporal imaging of the hemoglobin in the compressed breast with diffuse optical tomography

We develop algorithms for imaging the time-varying optical absorption within the breast given diffuse optical tomographic data collected over a time span that is long compared to the dynamics of the medium. Multispectral measurements allow for the determination of the time-varying total hemoglobin concentration and of oxygen saturation. To facilitate the image reconstruction, we decompose the hemodynamics in time into a linear combination of spatio-temporal basis functions, the coefficients of which are estimated using all of the data simultaneously, making use of a Newton-based nonlinear optimization algorithm. The solution of the extremely large least-squares problem which arises in computing the Newton update is obtained iteratively using the LSQR algorithm. A Laplacian spatial regularization operator is applied, and, in addition, we make use of temporal regularization which tends to encourage similarity between the images of the spatio-temporal coefficients. Results are shown for an extensive simulation, in which we are able to image and quantify localized changes in both total hemoglobin concentration and oxygen saturation. Finally, a breast compression study has been performed for a normal breast cancer screening subject, using an instrument which allows for highly accurate co-registration of multispectral diffuse optical measurements with an x-ray tomosynthesis image of the breast. We are able to quantify the global return of blood to the breast following compression, and, in addition, localized changes are observed which correspond to the glandular region of the breast.

All-optical anatomical co-registration for molecular imaging of small animals using dynamic contrast

Optical molecular imaging in small animals harnesses the power of highly specific and biocompatible contrast agents for drug development and disease research1-7. However, the widespread adoption of in vivo optical imaging has been inhibited by its inability to clearly resolve and identify targeted internal organs. Optical tomography8-11 and combined X-ray and micro-computed tomography (micro-CT)12 approaches developed to address this problem are generally expensive, complex or incapable of true anatomical co-registration. Here, we present a remarkably simple all-optical method that can generate co-registered anatomical maps of a mouse's internal organs, while also acquiring in vivo molecular imaging data. The technique uses a time series of images acquired after injection of an inert dye. Differences in the dye's in vivo biodistribution dynamics allow precise delineation and identification of major organs. Such co-registered anatomical maps permit longitudinal organ identification irrespective of repositioning or weight gain, thereby promising greatly improved accuracy and versatility for studies of orthotopic disease, diagnostics and therapies.

Compression-induced changes in the physiological state of the breast as observed through frequency domain photon migration measurements

We use optical spectroscopy to characterize the influence of mammographic-like compression on the physiology of the breast. We note a reduction in total hemoglobin content, tissue oxygen saturation, and optical scattering under compression. We also note a hyperemic effect during repeated compression cycles. By modeling the time course of the tissue oxygen saturation, we are able to obtain estimates for the volumetric blood flow (1.64+/-0.6 mL/100 mL/min) and the oxygen consumption (1.97+/-0.6 micromol/100 mL/min) of compressed breast tissue. These values are comparable to estimates obtained from previously published positron emission tomography (PET) measurements. We conclude that compression-induced changes in breast physiological properties are significant and should be accounted for when performing optical breast imaging. Additionally, the dynamic characteristics of the changes in breast physiological parameters, together with the ability to probe the tissue metabolic state, may prove useful for breast cancer detection.

Level-set algorithm for the reconstruction of functional activation in near-infrared spectroscopic imaging

We introduce a new algorithm for the reconstruction of functional brain activations from near-infrared spectroscopic imaging (NIRSI) data. While NIRSI offers remarkable biochemical specificity, the attainable spatial resolution with this technique is rather limited, mainly due to the highly scattering nature of brain tissue and the low number of measurement channels. Our approach exploits the support-limited (spatially concentrated) nature of the activations to make the reconstruction problem well-posed. The new algorithm considers both the support and the function values of the activations as unknowns and estimates them from the data. The support of the activations is represented using a level-set scheme. We use a two-step alternating iterative scheme to solve for the activations. Since our approach uses the inherent nature of functional activations to make the problem well-posed, it provides reconstructions with better spatial resolution, fewer artifacts, and is more robust to noise than existing techniques. Numerical simulations and experimental data indicate a significant improvement in the quality (resolution and robustness to noise) over standard techniques such as truncated conjugate gradients (TCG) and simultaneous iterative reconstruction technique (SIRT) algorithms. Furthermore, results on experimental data obtained from simultaneous functional magnetic resonance imaging (fMRI) and optical measurements show much closer agreement of the optical reconstruction using the new approach with fMRI images than TCG and SIRT.

Perturbation theory for the diffusion equation by use of the moments of the generalized temporal point-spread function. I. Theory

We approach the perturbative solution to the diffusion equation for the case of absorbing inclusions embedded in a heterogeneous scattering medium by using general properties of the radiative transfer equation and the solution of the Fredholm equation of the second kind given by the Neumann series. The terms of the Neumann series are used to obtain the expression of the moments of the generalized temporal point-spread function derived in transport theory. The moments are calculated independently by using Monte Carlo simulations for validation of the theory. While the mixed moments are correctly derived from the theory by using the solution of the diffusion equation in the geometry of interest, in order to obtain the self moments we should reframe the problem in transport theory and use a suitable solution of the radiative transfer equation for the calculation of the multiple integrals of the corresponding Neumann series. Since the rigorous theory leads to impractical formulas, in order to simplify and speed up the calculation of the self moments, we propose a heuristic method based on the calculation of only a single integral and some scaling parameters. We also propose simple quadrature rules for the calculation of the mixed moments for speeding up the computation of perturbations due to multiple defects. The theory can be developed in the continuous-wave domain, the time domain, and the frequency domain. In a companion paper [J. Opt. Soc. Am. A23, 2119 (2006)] we discuss the conditions of applicability of the theory in practical cases found in diffuse optical imaging of biological tissues.

Spectral derivative based image reconstruction provides inherent insensitivity to coupling and geometric errors

A reconstruction algorithm for multiwavelength diffuse optical tomography is presented, where instead of using data at each wavelength separately or even simultaneously, the difference in data for multiple wavelength pairs is used to reconstruct absolute concentration maps of chromophores. The results indicate a dramatic improvement in image reconstruction and the elimination of image artifacts, which are often associated with unknown measurement errors such as coupling coefficients and external boundary variations, because these errors are often less dependent on wavelength, and are effectively removed from the data set of the first derivative of intensity with respect to wavelength.

A haemodynamic response function model in spatio-temporal diffuse optical tomography

Diffuse optical tomography (DOT) is a new and effective technique for functional brain imaging. It can detect local changes in both oxygenated and deoxygenated haemoglobin concentrations in tissue based on differential absorption at multiple wavelengths. Traditional methods in spatio-temporal analysis of haemoglobin concentrations in diffuse optical tomography first reconstruct the spatial distribution at different time instants independently, then look at the temporal dynamics on each pixel, without incorporating any temporal information as a prior in the image reconstruction. In this work, we present a temporal haemodynamic response function model described by a basis function expansion, in a joint spatio-temporal DOT reconstruction of haemoglobin concentration changes during simulated brain activation. In this joint framework, we simultaneously employ spatial regularization, spectral information and temporal assumptions. We also present an efficient algorithm for solving the associated large-scale systems. The expected improvements in spatial resolution and contrast-to-noise ratio are illustrated with simulations of human brain activation.

Image quality improvement via spatial deconvolution in optical tomography: time-series imaging

We present the fourth in a series of studies devoted to the issue of improving image quality in diffuse optical tomography (DOT) by using a spatial deconvolution operation that seeks to compensate for the information-blurring property of first-order perturbation algorithms. Our earlier reports consider only static target media. Here we report spatial deconvolution applied to media with time-varying optical properties, as a model of tissue dynamics resulting from varying metabolic demand and modulation of the vascular bed. Issues under study include the influence of deconvolution on the accuracy of the recovered temporal and spatial information. The impact of noise is also explored, and techniques for ameliorating its information-degrading effects are examined. At low noise levels (i.e, < or = 5% of the time-varying signal amplitude), spatial deconvolution markedly improves the accuracy of recovered information. Temporal information is more seriously degraded by noise than is spatial information, and the impact of noise increases with the complexity of the time-varying signal. These effects, however, can be significantly reduced using simple noise suppression techniques (e.g., low-pass filtering). Results suggest that the deconvolution scheme should provide considerable enhancement of the quality of spatiotemporal information recovered from dynamic DOT techniques applied to tissue studies.

Recent advances in diffuse optical imaging

We review the current state-of-the-art of diffuse optical imaging, which is an emerging technique for functional imaging of biological tissue. It involves generating images using measurements of visible or near-infrared light scattered across large (greater than several centimetres) thicknesses of tissue. We discuss recent advances in experimental methods and instrumentation, and examine new theoretical techniques applied to modelling and image reconstruction. We review recent work on in vivo applications including imaging the breast and brain, and examine future challenges.

Improved accuracy of reconstructed diffuse optical tomographic images by means of spatial deconvolution: two-dimensional quantitative characterization

Systematic characterization studies are presented, relating to a previously reported spatial deconvolution operation that seeks to compensate for the information-blurring property of first-order perturbation algorithms for diffuse optical tomography (DOT) image reconstruction. In simulation results that are presented, this deconvolution operation has been applied to two-dimensional DOT images reconstructed by solving a first-order perturbation equation. Under study was the effect on algorithm performance of control parameters in the measurement (number and spatial distribution of sources and detectors, presence of noise, and presence of systematic error), target (medium shape; and number, location, size, and contrast of inclusions), and computational (number of finite-element-method mesh nodes, length of filter-generating linear system, among others) parameter spaces associated with computation and the use of the deconvolution operators. Substantial improvements in reconstructed image quality, in terms of recovered inclusion location, size, and contrast, are found in all cases. A finding of practical importance is that the method is robust to appreciable differences between the optical coefficients of the media used for filter generation and those of the target media to which the filters are subsequently applied.

Design and implementation of dynamic near-infrared optical tomographic imaging instrumentation for simultaneous dual-breast measurements

Dynamic near-infrared optical tomographic measurement instrumentation capable of simultaneous bilateral breast imaging, having a capability of four source wavelengths and 32 source-detector fibers for each breast, is described. The system records dynamic optical data simultaneously from both breasts, while verifying proper optical fiber contact with the tissue through implementation of automatic schemes for evaluating data integrity. Factors influencing system complexity and performance are discussed, and experimental measurements are provided to demonstrate the repeatability of the instrumentation. Considerations in experimental design are presented, as well as techniques for avoiding undesirable measurement artifacts, given the high sensitivity and dynamic range (1:10(9)) of the system. We present exemplary clinical results comparing the measured physiologic response of a healthy individual and of a subject with breast cancer to a Valsalva maneuver.

Optimal linear inverse solution with multiple priors in diffuse optical tomography

A general framework for incorporating single and multiple priors in diffuse optical tomography is described. We explore the use of this framework for simultaneously utilizing spatial and spectral priors in the context of imaging breast cancer. The utilization of magnetic resonance images of water and lipid content as a statistical spatial prior for the diffuse optical image reconstructions is also discussed. Simulations are performed to demonstrate the significant improvement in image quality afforded by combining spatial and spectral priors.