Magnetic-resonance-imaging-coupled broadband near-infrared tomography system for small animal brain studies

Ultrafast and Ultrahigh-Resolution Diffuse Optical Tomography for Brain Imaging with Sensitivity Equation based Noniterative Sparse Optical Reconstruction (SENSOR)

We introduce a novel image reconstruction method for time-resolved diffuse optical tomography (DOT) that yields submillimeter resolution in less than a second. This opens the door to high-resolution real-time DOT in imaging of the brain activity. We call this approach the sensitivity equation based noniterative sparse optical reconstruction (SENSOR) method. The high spatial resolution is achieved by implementing an asymptotic l ₀-norm operator that guarantees to obtain sparsest representation of reconstructed targets. The high computational speed is achieved by employing the nontruncated sensitivity equation based noniterative inverse formulation combined with reduced sensing matrix and parallel computing. We tested the new method with numerical and experimental data. The results demonstrate that the SENSOR algorithm can achieve 1 mm³ spatial-resolution optical tomographic imaging at depth of ∼60 mean free paths (MFPs) in 20∼30 milliseconds on an Intel Core i9 processor.

L1-norm based nonlinear reconstruction improves quantitative accuracy of spectral diffuse optical tomography

Spectrally constrained diffuse optical tomography (SCDOT) is known to improve reconstruction in diffuse optical imaging; constraining the reconstruction by coupling the optical properties across multiple wavelengths suppresses artefacts in the resulting reconstructed images. In other work, L1-norm regularization has been shown to improve certain types of image reconstruction problems as its sparsity-promoting properties render it robust against noise and enable the preservation of edges in images, but because the L1-norm is non-differentiable, it is not always simple to implement. In this work, we show how to incorporate L1 regularization into SCDOT. Three popular algorithms for L1 regularization are assessed for application in SCDOT: iteratively reweighted least square algorithm (IRLS), alternating directional method of multipliers (ADMM), and fast iterative shrinkage-thresholding algorithm (FISTA). We introduce an objective procedure for determining the regularization parameter in these algorithms and compare their performance in simulated experiments, and in real data acquired from a tissue phantom. Our results show that L1 regularization consistently outperforms Tikhonov regularization in this application, particularly in the presence of noise.

Differentiation of tumor vasculature heterogeneity levels in small animals based on total hemoglobin concentration using magnetic resonance-guided diffuse optical tomography in vivo

Insight into the vasculature of the tumor in small animals has the potential to impact many areas of cancer research. The heterogeneity of the vasculature of a tumor is directly related to tumor stage and disease progression. In this small scale animal study, we investigated the feasibility of differentiating tumors with different levels of vasculature heterogeneity in vivo using a previously developed hybrid magnetic resonance imaging (MRI) and diffuse optical tomography (DOT) system for small animal imaging. Cross-sectional total hemoglobin concentration maps of 10 Fisher rats bearing R3230 breast tumors are reconstructed using multi-wavelength DOT measurements both with and without magnetic resonance (MR) structural a priori information. Simultaneously acquired MR structural images are used to guide and constrain the DOT reconstruction, while dynamic contrast-enhanced MR functional images are used as the gold standard to classify the vasculature of the tumor into two types: high versus low heterogeneity. These preliminary results show that the stand-alone DOT is unable to differentiate tumors with low and high vascular heterogeneity without structural a priori information provided by a high resolution imaging modality. The mean total hemoglobin concentrations comparing the vasculature of the tumors with low and high heterogeneity are significant (p-value 0.02) only when MR structural a priori information is utilized.

Combined Diffuse Optical Tomography (DOT) and MRI System for Cancer Imaging in Small Animals

Recently, there has been a great amount of interest in developing multi-modality imaging techniques for oncologic research and clinical studies with the aim of obtaining complementary information and, thus, improving the detection and characterization of tumors. In this present work, the details of a combined MR-diffuse optical imaging system for dual-modality imaging of small animals are given. As a part of this effort, a multi-spectral frequency domain diffuse optical tomography system is integrated with an MRI system. Here, a network analyzer provides the rf modulation signal for the laser diodes and measures the amplitude and the phase of the detected signals. Photomultiplier tubes are utilized to measure low-level signals. The integration of this optical imaging system with the 4T MRI system is realized by incorporating a fiber adaptive interface inside the MR magnet. Coregistration is achieved by a special probe design utilizing fiducial markers. A finite element algorithm is used to solve the diffusion equation and an inverse solver based on this forward solver is implemented to calculate the absorption and scattering maps from the acquired data. The MR a priori information is used to guide the optical reconstruction algorithm. Phantom studies show that the absorption coefficient of a 7 mm inclusion in an irregular object located in 64 mm phantom is recovered with 11% error when MR a priori information is used. ENU induced tumor model is used to test the performance of the system in vivo.

Coregistration of Dynamic Contrast Enhanced MRI and Broadband Diffuse Optical Spectroscopy for Characterizing Breast Cancer

A handheld scanning probe based on broadband Diffuse Optical Spectroscopy (DOS) was used in combination with dynamic contrast enhanced MRI (DCE-MRI) to quantitatively characterize locally-advanced breast cancers in six patients. Measurements were performed sequentially using external fiducial markers for co-registration. Tumor patterns were categorized according to MRI morphological data, and 3D DCE-MRI slices were converted into a volumetric matrix with isotropic voxels to generate views that coincided with the DOS scanning plane. Tumor volume and depth at each DOS measurement site were determined, and a tissue optical index (TOI) that reflects both angiogenic and stromal characteristics was derived from broadband DOS data.

In all six cases, optical scans showed significant TOI contrast corresponding to MRI morphological information. Sharp TOI peaks were recovered for well-circumscribed masses. A reduction in TOI was found inside a tumor with a necrotic center. A broadened peak was observed for a diffuse tumor pattern, and an inflammatory septal case provided two TOI peaks that correlated qualitatively with MRI enhancement. These results provide qualitative confirmation of the common signal origin and complementary information content that can be achieved by combining optical and MR imaging for breast cancer detection and clinical management.

Development and investigation of a magnetic resonance imaging-compatible microlens-based optical detector

A noncontact optical detector for in vivo imaging has been developed that is compatible with magnetic resonance imaging (MRI). The optical detector employs microlens arrays and might be classified as a plenoptic camera. As a resulting of its design, the detector possesses a slim thickness and is self-shielding against radio frequency (RF) pulses. For experimental investigation, a total of six optical detectors were arranged in a cylindrical fashion, with the imaged object positioned in the center of this assembly. A purposely designed RF volume resonator coil has been developed and is incorporated within the optical imaging system. The whole assembly was placed into the bore of a 1.5 T patient-sized MRI scanner. Simple-geometry phantom studies were performed to assess compatibility and performance characteristics regarding both optical and MR imaging systems. A bimodal ex vivo nude mouse measurement was conducted. From the MRI data, the subject surface was extracted. Optical images were projected on this surface by means of an inverse mapping algorithm. Simultaneous measurements did not reveal influences from the magnetic field and RF pulses onto optical detector performance (spatial resolution, sensitivity). No significant influence of the optical imaging system onto MRI performance was detectable.

FMT-PCCT: hybrid fluorescence molecular tomography-x-ray phase-contrast CT imaging of mouse models

The implementation of hybrid fluorescence molecular tomography (FMT) and X-ray computed tomography (CT) has been shown to be a necessary development, not only for combining anatomical with functional and molecular contrast, but also for generating optical images of high accuracy. FMT affords highly sensitive 3-D imaging of fluorescence bio-distribution, but in stand-alone form it offers images of low resolution. It was shown that FMT accuracy significantly improves by considering anatomical priors from CT. Conversely, CT generally suffers from low soft tissue contrast. Therefore utilization of CT data as prior information in FMT inversion is challenging when different internal organs are not clearly differentiated. Instead, we combined herein FMT with emerging X-ray phase-contrast CT (PCCT). PCCT relies on phase shift differences in tissue to achieve soft tissue contrast superior to conventional CT. We demonstrate for the first time FMT-PCCT imaging of different animal models, where FMT and PCCT scans were performed in vivo and ex vivo, respectively. The results show that FMT-PCCT expands the potential of FMT in imaging lesions with otherwise low or no CT contrast, while retaining the cost benefits of CT and simplicity of hybrid device realizations. The results point to the most accurate FMT performance to date.

Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography

Multimodal approaches that combine near-infrared (NIR) and conventional imaging modalities have been shown to improve optical parameter estimation dramatically and thus represent a prevailing trend in NIR imaging. These approaches typically involve applying anatomical templates from magnetic resonance imaging/computed tomography/ultrasound images to guide the recovery of optical parameters. However, merging these data sets using current technology requires multiple software packages, substantial expertise, significant time-commitment, and often results in unacceptably poor mesh quality for optical image reconstruction, a reality that represents a significant roadblock for translational research of multimodal NIR imaging. This work addresses these challenges directly by introducing automated digital imaging and communications in medicine image stack segmentation and a new one-click three-dimensional mesh generator optimized for multimodal NIR imaging, and combining these capabilities into a single software package (available for free download) with a streamlined workflow. Image processing time and mesh quality benchmarks were examined for four common multimodal NIR use-cases (breast, brain, pancreas, and small animal) and were compared to a commercial image processing package. Applying these tools resulted in a fivefold decrease in image processing time and 62% improvement in minimum mesh quality, in the absence of extra mesh postprocessing. These capabilities represent a significant step toward enabling translational multimodal NIR research for both expert and nonexpert users in an open-source platform.

Singular value decomposition based regularization prior to spectral mixing improves crosstalk in dynamic imaging using spectral diffuse optical tomography

The spectrally constrained diffuse optical tomography (DOT) method relies on incorporating spectral prior information directly into the image reconstruction algorithm, thereby correlating the underlying optical properties across multiple wavelengths. Although this method has been shown to provide a solution that is stable, the use of conventional Tikhonov-type regularization techniques can lead to additional crosstalk between parameters, particularly in linear, single-step dynamic imaging applications. This is due mainly to the suboptimal regularization of the spectral Jacobian matrix, which smoothes not only the image-data space, but also the spectral mapping space. In this work a novel regularization technique based on the singular value decomposition (SVD) is presented that preserves the spectral prior information while regularizing the Jacobian matrix, leading to dramatically reduced crosstalk between the recovered parameters. Using simulated data, images of changes in oxygenated and deoxygenated hemoglobin concentrations are reconstructed via the SVD-based approach and compared with images reconstructed by using non-spectral and conventional spectral methods. In a 2D, two wavelength example, it is shown that the proposed approach provides a 98% reduction in crosstalk between recovered parameters as compared with conventional spectral reconstruction algorithms, and 60% as compared with non-spectrally constrained algorithms. Using a subject specific multilayered model of the human head, a noiseless dynamic simulation of cortical activation is performed to further demonstrate such improvement in crosstalk. However, with the addition of realistic noise in the data, both non-spectral and proposed algorithms perform similarly, indicating that the use of spectrally constrained reconstruction algorithms in dynamic DOT may be limited by the contrast of the signal as well as the noise characteristics of the system.

The utility of direct-current as compared to frequency domain measurements in spectrally-constrained diffuse optical tomography toward cancer imaging

This work investigates, by means of analytical and simulation studies, the performance of spectrally-constrained image reconstruction in Continuous-Wave or Direct-Current (DC) and Frequency Domain (FD) near-infrared optical tomography. A recent analytic approach for estimating the accuracy of target recovery and the level of background artifact for optical tomography at single wavelength, based on the analysis of parametric reconstruction uncertainty level (PRUL), is extended to spectrally-constrained optical tomography. The analytical model is implemented to rank three sets of wavelengths that had been used as spectral prior in an independent experimental study. Subsequent simulation appraises the recovery of oxygenated hemoglobin (HbO), deoxygenated hemoglobin (Hb), water (H2O), scattering amplitude (A), and scattering power (b) using DC-only, DC-excluded FD, and DC-included FD, based on the three sets of wavelengths as the spectral prior. The simulation results support the analytic ranking of the performance of the three sets of spectral priors, and generally agree with the performance outcome of DC-only versus that of DC-excluded FD and DC-included FD. Specifically, this study indicate that: 1) the rank of overall quality of chromophore recovery is Hb, H2O, and HbO from the highest to lowest; and in the scattering part the A is always better recovered than b. This outcome does suggest that the DC-only information gives rise to unique solution to the image reconstruction routine under the given spectral prior. 2) DC-information is not-redundant in FD-reconstruction, as the artifact levels of DC-included FD reconstruction are always lower than those of DC-excluded FD. 3) The artifact level as represented by the noise-to-contrast-ratio is almost always the lowest in DC-only, leading to generally better resolution of multiple targets of identical contrasts over the background than in FD. However, the FD could outperform DC in the recovery of scattering properties including both A and b when the spectral prior is less optimal, implying the benefit of phase-information in scattering recovery in the context of spectrally-constrained optical tomography.

Hybrid small animal imaging system combining magnetic resonance imaging with fluorescence tomography using single photon avalanche diode detectors

The high sensitivity of fluorescence imaging enables the detection of molecular processes in living organisms. However, diffuse light propagation in tissue prevents accurate recovery of tomographic information on fluorophore distribution for structures embedded deeper than 0.5 mm. Combining optical with magnetic resonance imaging (MRI) provides an accurate anatomical reference for fluorescence imaging data and thereby enables the correlation of molecular with high quality structural/functional information. We describe an integrated system for small animal imaging incorporating a noncontact fluorescence molecular tomography (FMT) system into an MRI detector. By adopting a free laser beam design geometrical constraints imposed by the use of optical fibers could be avoided allowing for flexible fluorescence excitation schemes. Photon detection based on a single-photon avalanche diode array enabled simultaneous FMT/MRI measurements without interference between modalities. In vitro characterization revealed good spatial accuracy of FMT data and accurate quantification of dye concentrations. Feasibility of FMT/MRI was demonstrated in vivo by simultaneous assessment of protease activity and tumor morphology in murine colon cancer xenografts.

Fluorescence molecular tomography: principles and potential for pharmaceutical research

Fluorescence microscopic imaging is widely used in biomedical research to study molecular and cellular processes in cell culture or tissue samples. This is motivated by the high inherent sensitivity of fluorescence techniques, the spatial resolution that compares favorably with cellular dimensions, the stability of the fluorescent labels used and the sophisticated labeling strategies that have been developed for selectively labeling target molecules. More recently, two and three-dimensional optical imaging methods have also been applied to monitor biological processes in intact biological organisms such as animals or even humans. These whole body optical imaging approaches have to cope with the fact that biological tissue is a highly scattering and absorbing medium. As a consequence, light propagation in tissue is well described by a diffusion approximation and accurate reconstruction of spatial information is demanding. While in vivo optical imaging is a highly sensitive method, the signal is strongly surface weighted, i.e., the signal detected from the same light source will become weaker the deeper it is embedded in tissue, and strongly depends on the optical properties of the surrounding tissue. Derivation of quantitative information, therefore, requires tomographic techniques such as fluorescence molecular tomography (FMT), which maps the three-dimensional distribution of a fluorescent probe or protein concentration. The combination of FMT with a structural imaging method such as X-ray computed tomography (CT) or Magnetic Resonance Imaging (MRI) will allow mapping molecular information on a high definition anatomical reference and enable the use of prior information on tissue's optical properties to enhance both resolution and sensitivity. Today many of the fluorescent assays originally developed for studies in cellular systems have been successfully translated for experimental studies in animals. The opportunity of monitoring molecular processes non-invasively in the intact organism is highly attractive from a diagnostic point of view but even more so for the drug developer, who can use the techniques for proof-of-mechanism and proof-of-efficacy studies. This review shall elucidate the current status and potential of fluorescence tomography including recent advances in multimodality imaging approaches for preclinical and clinical drug development.

Quantitative fluorescence tomography using a combined tri-modality FT/DOT/XCT system

In this work, a first-of-its-kind fully integrated tri-modality system that combines fluorescence, diffuse optical and x-ray tomography (FT/DOT/XCT) into the same setting is presented. The purpose of this system is to perform quantitative fluorescence tomography using multi-modality imaging approach. XCT anatomical information is used as structural priori while optical background heterogeneity information obtained by DOT measurements is used as functional priori. The performance of the hybrid system is evaluated using multi-modality phantoms. In particular, we show that a 2.4 mm diameter fluorescence inclusion located in a heterogeneous medium can be localized accurately with the functional a priori information, although the fluorophore concentration is recovered with 70% error. On the other hand, the fluorophore concentration can be accurately recovered within 8% error only when both DOT optical background functional and XCT structural a priori information are utilized to guide and constrain the FT reconstruction algorithm.

Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction

Diffuse optical tomography, also known as near infrared tomography, has been under investigation, for non-invasive functional imaging of tissue, specifically for the detection and characterization of breast cancer or other soft tissue lesions. Much work has been carried out for accurate modeling and image reconstruction from clinical data. NIRFAST, a modeling and image reconstruction package has been developed, which is capable of single wavelength and multi-wavelength optical or functional imaging from measured data. The theory behind the modeling techniques as well as the image reconstruction algorithms is presented here, and 2D and 3D examples are presented to demonstrate its capabilities. The results show that 3D modeling can be combined with measured data from multiple wavelengths to reconstruct chromophore concentrations within the tissue. Additionally it is possible to recover scattering spectra, resulting from the dominant Mie-type scatter present in tissue. Overall, this paper gives a comprehensive over view of the modeling techniques used in diffuse optical tomographic imaging, in the context of NIRFAST software package.

A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging

A prototype small animal imaging system was created for coupling fluorescence tomography (FT) with x-ray microcomputed tomography (microCT). The FT system has the potential to provide synergistic information content resultant from using microCT images as prior spatial information and then allows overlay of the FT image onto the original microCT image. The FT system was designed to use single photon counting to provide maximal sensitivity measurements in a noncontact geometry. Five parallel detector locations are used, each allowing simultaneous sampling of the fluorescence and transmitted excitation signals through the tissue. The calibration and linearity range performance of the system are outlined in a series of basic performance tests and phantom studies. The ability to image protoporphyrin IX in mouse phantoms was assessed and the system is ready for in vivo use to study biological production of this endogenous marker of tumors. This multimodality imaging system will have a wide range of applications in preclinical cancer research ranging from studies of the tumor microenvironment and treatment efficacy for emerging cancer therapeutics.

Broadband frequency-domain near-infrared spectral tomography using a mode-locked Ti:sapphire laser

Frequency-domain near-infrared (NIR) diffuse spectral tomography with a mode-locked Ti:sapphire laser is presented, providing tunable multiwavelength quantitative spectroscopy with maximal power for thick tissue imaging. The system was developed to show that intrinsically high stability can be achieved with many wavelengths in the NIR range, using a mode-locked signal of 80 MHz with heterodyned lock-in detection. The effect of cumulative noise from multiple wavelengths of data on the reconstruction process was studied, and it was shown that inclusion of more wavelengths can reduce skew in the noise distribution. This normalization of the data variance then minimizes errors in estimation of chromophore concentrations. Simulations and tissue phantom experiments were used to quantify this improvement in image accuracy for recovery of tissue hemoglobin and oxygen saturation.

Wavelength dependence of sensitivity in spectral diffuse optical imaging: effect of normalization on image reconstruction

Near Infrared Diffuse Optical Tomography has the potential to be used as a non-invasive imaging tool for biological tissue specifically for the diagnosis and characterization of breast cancer. Most model based reconstruction algorithms rely on calculating and inverting a large Jacobian matrix. Although this method is flexible for a wide range of complex problems, it usually results in large image artifacts from hypersensitivity around the detectors. In this work a Jacobian normalization technique is presented which takes into account the varying magnitude of different optical parameters creating a more uniform update within a spectral image reconstruction model. Using simulated data the Jacobian normalization method is used to reconstructed images of absolute chromophore and scattering parameters which are qualitatively and quantitatively as compared to conventional methods. The hypersensitivity resulting in boundary artifacts are shown to be minimized with only a small additional computational cost.

Wavelength band optimization in spectral near-infrared optical tomography improves accuracy while reducing data acquisition and computational burden

Multispectral near-infrared (NIR) tomographic imaging has the potential to provide information about molecules absorbing light in tissue, as well as subcellular structures scattering light, based on transmission measurements. However, the choice of possible wavelengths used is crucial for the accurate separation of these parameters, as well as for diminishing crosstalk between the contributing chromophores. While multispectral systems are often restricted by the wavelengths of laser diodes available, continuous-wave broadband systems exist that have the advantage of providing broadband NIR spectroscopy data, albeit without the benefit of the temporal data. In this work, the use of large spectral NIR datasets is analyzed, and an objective function to find optimal spectral ranges (windows) is examined. The optimally identified wavelength bands derived from this method are tested using both simulations and experimental data. It is found that the proposed method achieves images as qualitatively accurate as using the full spectrum, but improves crosstalk between parameters. Additionally, the judicious use of these spectral windows reduces the amount of data needed for full spectral tomographic imaging by 50%, therefore increasing computation time dramatically.

Developments in quantitative oxygen-saturation imaging of breast tissue in vivo using multispectral near-infrared tomography

Imaging of oxygen saturation provides a spatial map of the tissue metabolic activity and has potential in diagnosis and treatment monitoring of breast cancer. Oxygen-saturation imaging is possible through near-infrared (NIR) tomography, but has low signal-to-noise ratio (SNR). This can be augmented by using NIR tomography as an add-on to MRI. Presented are results from a free-standing NIR system and a hybrid MR-guided system for breast imaging. In results from imaging 60 healthy volunteers in the initial NIR system, oxygen saturation was a significant discriminator between the BIRADS classifications of adipose tissue, heterogeneously dense, and extremely dense tissue. By using the MR-guided NIR system, more accurate tissue-specific data were obtained on adipose and fibroglandular volumes, with 11 healthy volunteers. In these data, oxygen saturation in the adipose tissue correlated with percentage of adipose tissue. In two case studies of infiltrating ductal carcinomas, oxygen saturation was reduced at the site of the tumor, as compared with the surrounding healthy tissue, agreeing with conventional thought that hypoxia exists in larger solid tumors. The MRI-guided NIR images of oxygen saturation provide higher resolution and superior SNR and will likely be used in the future to study and characterize specific tissue volumes.

Radiative transport in fluorescence-enhanced frequency domain photon migration

Small animal optical tomography has significant, but potential application for streamlining drug discovery and pre-clinical investigation of drug candidates. However, accurate modeling of photon propagation in small animal volumes is critical to quantitatively obtain accurate tomographic images. Herein we present solutions from a robust fluorescence-enhanced, frequency domain radiative transport equation (RTE) solver with unique attributes that facilitate its deployment within tomographic algorithms. Specifically, the coupled equations describing time-dependent excitation and emission light transport are solved using discrete ordinates (SN) angular differencing along with linear discontinuous finite-element spatial differencing on unstructured tetrahedral grids. Source iteration in conjunction with diffusion synthetic acceleration is used to iteratively solve the resulting system of equations. This RTE solver can accurately and efficiently predict ballistic as well as diffusion limited transport regimes which could simultaneously exist in small animals. Furthermore, the solver provides accurate solutions on unstructured, tetrahedral grids with relatively large element sizes as compared to commonly employed solvers that use step differencing. The predictions of the solver are validated by a series of frequency-domain, phantom measurements with optical properties ranging from diffusion limited to transport limited propagation. Our results demonstrate that the RTE solution consistently matches measurements made under both diffusion and transport-limited conditions. This work demonstrates the use of an appropriate RTE solver for deployment in small animal optical tomography.

An integrated measurement system for simultaneous functional magnetic resonance imaging and diffuse optical tomography in human brain mapping

An integrated measurement system is described for performing simultaneous functional magnetic resonance imaging (fMRI) and diffuse optical tomography (DOT) for human brain mapping experiments. The components of this system consist of an MRI-compatible multi-overlapping-channel optical probe, methods for co-registration of optical and fMRI measurements, and DOT reconstruction algorithms with structural and physiological constraints derived from the MRI data. The optical probe is fully MRI-compatible in the sense that it produces negligible MR image distortion and does not require any modification to the MRI scanner or data acquisition protocol. The probe can be attached to any part of the head without posing any limitation on optical data acquisition. Co-registration of images from fMRI and optical measurements was achieved by localizing the positions of the optical fibers using MRI markers. Human studies show successful implementation of the entire system.

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.

Dynamic physiological modeling for functional diffuse optical tomography

Diffuse optical tomography (DOT) is a noninvasive imaging technology that is sensitive to local concentration changes in oxy- and deoxyhemoglobin. When applied to functional neuroimaging, DOT measures hemodynamics in the scalp and brain that reflect competing metabolic demands and cardiovascular dynamics. The diffuse nature of near-infrared photon migration in tissue and the multitude of physiological systems that affect hemodynamics motivate the use of anatomical and physiological models to improve estimates of the functional hemodynamic response. In this paper, we present a linear state-space model for DOT analysis that models the physiological fluctuations present in the data with either static or dynamic estimation. We demonstrate the approach by using auxiliary measurements of blood pressure variability and heart rate variability as inputs to model the background physiology in DOT data. We evaluate the improvements accorded by modeling this physiology on ten human subjects with simulated functional hemodynamic responses added to the baseline physiology. Adding physiological modeling with a static estimator significantly improved estimates of the simulated functional response, and further significant improvements were achieved with a dynamic Kalman filter estimator (paired t tests, n=10, P<0.05). These results suggest that physiological modeling can improve DOT analysis. The further improvement with the Kalman filter encourages continued research into dynamic linear modeling of the physiology present in DOT. Cardiovascular dynamics also affect the blood-oxygen-dependent (BOLD) signal in functional magnetic resonance imaging (fMRI). This state-space approach to DOT analysis could be extended to BOLD fMRI analysis, multimodal studies and real-time analysis.