For much of the past decade, we have developed most of the essential hardware and software components needed for
practical implementation of dynamic NIRS imaging. Until recently, however, these efforts have been hampered by the
lack of calibrating phantoms whose dynamics substantially mimic those seen in tissue. Here we present findings that
document the performance of a dynamic phantom based on use of twisted nematic liquid crystal (LC) technology.
Programmable time courses of applied voltage cause the opacity of the LC devices, which are embedded in a background
matrix consisting of polysiloxane (silicone) admixed with scattering and absorbing materials, to vary in a manner that
mimics the spatiotemporal hemodynamic pattern of interest. Methods for producing phantoms with selected absorption
and scattering, internal heterogeneity, external geometry, hardness, and number and locations of embedded LCs are
described. Also described is a method for overcoming the apparent limitation that arises from LCs being mainly
independent of the illumination wavelength. The results presented demonstrate that: the opacity vs. voltage response of
LCs are highly stable and repeatable; the dynamic phantom can be driven at physiologically relevant speeds, and will
produce time-varying absorption that follows the programmed behavior with high fidelity; image time series recovered
from measurements on the phantom have high temporal and spatial location accuracy. Thus the dynamic phantom can
fill the need for test media that practitioners may use to confirm the accuracy of computed imaging results, assure the
correct operation of imaging hardware, and compare performance of different data analysis algorithms.
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, <=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.
In this report we present a brief outline of our technological approaches to developing a comprehensive imaging platform suitable for the investigation of the dynamics of the hemoglobin signal in large tissue structures using NIRS imaging techniques. Our approach includes a combined hardware and software development effort that provides for i) hardware integration, ii) system calibration, iii) data integrity checks, iv) image recovery, v) image enhancement and vi) signal processing. Presented are representative results obtained from human subjects that explore the sensitivity and other capabilities of the measuring system to detect focal hemodynamic responses in the head, breast and limb of volunteers. Results obtained support the contention that time-series NIRS imaging is a powerful and sensitive technique for exploring the hemodynamics of healthy and diseased tissues.
A reconstruction technique that allows for real-time recovery of a time series of images is presented. The reconstruction technique is intended for use in a dynamic DC imaging system, and uses a model-based normalized-preconditioned-transformed system equation that considers light diffusion and is suitable for structures having arbitrary geometry and composition. The algorithm was tested on synthetic 2D media containing dynamic optical contrast features, with the presence of added noise and other uncertainties that commonly are present in experimental data. Post-reconstruction analyses performed on the reconstructed image time series show that the algorithm is capable of correctly locating the inclusions (total of eight) with excellent spatiotemporal accuracy, even in the presence of 32% noise and other uncertainties. In addition, a semi-analytic graphical tool we have identified, that can guide design of suitable measurement geometries and selection of appropriate refinement of the imaging operator, is described.
The emerging sub-field of dynamic medical optical tomography shows great potential for conferring significantly enhanced early diagnosis and treatment monitoring capabilities upon researchers and clinicians. In previous reports we have showed that adoption of elementary time-series analysis techniques can bring about large large improvements in localization and contrast in optical tomographic images. Here we build upon the earlier work, and show that well-known techniques for extraction and localization of signals embedded in a noisy background, and for deconvolution of signal mixtures, also can be successfully applied to the problem of interpreting dynamic optical tomography data sets. A general linear model computation is used for the signal extraction/localization problem, while the deconvolution problem is addressed by means of a blind source separation technique extensively reported.
We have introduced working technology that provides for time-series imaging of the hemoglobin signal in large tissue structures. In this study we have explored our ability to detect aberrant time-frequency responses of breast vasculature for subjects with Stage II breast cancer at rest and in response to simple provocations. The hypothesis being explored is that time-series imaging will be sensitive to the known structural and functional malformations of the tumor vasculature. Mammographic studies were conducted using an adjustable hemisheric measuring head containing 21 source and 21 detector locations (441 source-detector pairs). Simultaneous dual-wavelength studies were performed at 760 and 830 nm at a framing rate of ~2.7 Hz. Optical measures were performed on women lying prone with the breast hanging in a pendant position. Two class of measures were performed: (1) 20- minute baseline measure wherein the subject was at rest; (2) provocation studies wherein the subject was asked to perform some simple breathing maneuvers. Collected data were analyzed to identify the time-frequency structure and central tendencies of the detector responses and those of the image time series. Imaging data were generated using the Normalized Difference Method (Pei et al., Appl. Opt. 40, 5755-5769, 2001). Results obtained clearly document three classes of anomalies when compared to the normal contralateral breast. 1) Breast tumors exhibit altered oxygen supply/demand imbalance in response to an oxidative challenge (breath hold). 2) The vasomotor response of the tumor vasculature is mainly depressed and exhibits an altered modulation. 3) The affected area of the breast wherein the altered vasomotor signature is seen extends well beyond the limits of the tumor itself.
Presented are the operating characteristics of an integrated CW-near infrared tomographic imaging system capable of fast data collection and producing 2D/3D images of optical contrast features that exhibit dynamic behavior in tissue and other highly scattering media in real time. Results of preliminary in vivo studies on healthy and cancerous breast tissue are shown.
The utility of optical tomography as a static imaging modality is limited by its intrinsically low spatial resolution and quantitative accuracy. When applied to dynamic measurements, however, optical imaging methods have the potential to assess tissue function as revealed by temporal variations in tissue optical properties. These variations are a consequence of vascular hemodynamic processes, which are known to exhibit considerably spatiotemporal heterogeneity. In this report we provide evidence, from simulation, that complex dynamic behavior in optical coefficients occurring in localized regions in highly scattering media can be accurately characterized by the method of dynamic optical tomography, even in the limiting case of spatiotemporally coincident behavior.
In this report, we present a method to reduce the cross-talk problem in optical tomography. The method described is an extension of a previously reported perturbation formulation related to relative detector values, and employs a weight matrix scaling technique together with a constrained CGD method for imaging reconstruction. Results from numerical and experimental studies using DC measurement data demonstrate that the approach can effectively isolate absorption and scattering heterogeneities, even for complex combinations of perturbations in optical properties. The derive method is remarkably stable to errors originating from an insufficiently accurate estimate of properties of the reference medium.
Vascular disease is a significant source of mortality and morbidity for many patient populations. While substantial strides in surgical therapeutics have been made in the past decade, our limited understanding of the microvascular processes, which are invisible to conventional imaging modalities and beyond the scope of our current physiologic paradigms, has slowed the advancement of medical therapeutic interventions. In this report we present data in support of an emerging body of work demonstrating that the method of dynamic optical tomography can yield critical insights into the underpinnings of microvascular pathophysiology in large tissue structures.
Dynamic processes in biology are often controlled by multiple parameters that interact in a complex nonlinear fashion. Increasingly, evidence has accumulated that such behavior exhibits the property of sensitivity to initial conditions, a feature exhibited by chaotic systems. One such system is the vasculature. In this report, we present what we believe is the first experimental demonstration ever of imaging chaotic behavior of the vasculature in a large tissue structure (i.e., the human forearm). Supporting these findings are results from numerical simulation demonstrating our ability to image and correctly characterize complex dynamic behavior in dense scattering media that experience spatiotemporally coincident variations in hemodynamic states.
Representative results from simulated, laboratory and physiological studies are presented, demonstrating the ability to extract important features of dynamic behavior from dense scattering media. These results were obtained by analyzing a time series of image data. Investigations on the human forearm clearly reveal the ability to identify and correctly locate principal features of the vasculature. Characterization of these features using linear and nonlinear time-series analysis methods can produce a wealth of information regarding the spatio-temporal features of the dynamics of vascular reactivity.
In this paper, we present a Born-Type iterative algorithm for reconstruction of absorption and diffusion coefficient distributions of a heterogeneous scattering medium. This method is derived based on the integral form of the diffusion equation for the photon flux. It takes into account the nonlinear nature of the problem by using an iterative perturbation approach. Within each iteration, the forward problem (update of the total field and Green's function) is solved by the finite element method (FEM), and the inverse problem (update of the medium properties) is obtained by a regularized least squares method. This method has been used to reconstruct 'pathologies' embedded in an inhomogeneous test medium simulating a normal female breast from frequency domain data. The test medium is constructed by assigning optical coefficients according to an MR derived anatomical map. Our simulation results show that the algorithm is computationally practical and can yield qualitatively and quantitatively correct absorption and scattering distributions of embedded objects from simulated data with up to 5% additive noise in the simulated measurement data.
In this paper, a reconstruction algorithm for fluorescence yield and lifetime imaging in dense scattering media is formulated and implemented. Two frequency domain radiation transport equations based on the diffusion approximation are used to model the migration of excitation and emitted photons. In the forward formulation, a finite element approach, which is specially effective for complex geometries and inhomogeneous distribution of medium properties, is adopted to obtain the required imaging operator and the simulated detector responses related to the photon fluxes on the boundary. Inverse formulation is derived based on the integral form of two diffusion equations. The technique is demonstrated by reconstructing spatial images of heterogenous fluorophore distribution and life time using simulated data obtained from homogeneous and complex (i.e., MRI breast map) media containing objects with fluorophore and with and without added noise.
In this paper, we present a Born Iterative Method for imaging optical properties of turbid media using frequency-domain data. In each iteration, the incident field and the associated weight matrix are first recalculated based on the previous reconstructed image. A new estimate is then obtained by a multigrid finite difference method. The inversion is carried out through a Tikhonov regularized optimization process using the conjugate gradient descent. Using this method, the distribution of the complex wavenumbers in a test medium is first reconstructed, from which the absorption and scattering distributions are then derived. Simulation results have shown that this method can yield quantitatively quite accurate reconstruction even when a strong perturbation exists between the actual medium and an assumed homogeneous background medium, in which case the Born approximation cannot work well. Both full-angle and limited angle measurement schemes have been simulated to understand the effect of the location of detectors and sources.
In this paper, a reconstruction algorithm for frequency-domain optical tomography in human tissue is presented. A fast and efficient multigrid finite difference (MGFD) method is adopted as a forward solver to obtain the simulated detector responses and the required imaging operator. The solutions obtained form MGFD method for 3D problems with weakly discontinuous cocoefficients are compared with analyzed solutions to determine the accuracy of the numerical method. Simultaneous reconstruction of both absorption and scattering coefficients for tissue-like media is accomplished by solving a perturbation equation using the Born approximation. This solution is obtained by a conjugate gradient descent method with Tikhonov regularization. Two examples are given to show the quality of the reconstruction results. Both involve the examination of anatomically accurate optical models of tissue derived from segmented 3D magnetic resonance images to which have been assigned optical coefficients to the designated tissue types. One is a map of a female breast containing two small 'added pathologies', such as tumors. The other is a map of the brain containing a 'local bleeding' area, representing a hemorrhage. The reconstruction results show that the algorithm is computationally practical and can yield qualitatively correct geometry of the objects embedded in the simulated human tissue. Acceptable results are obtaiend even when 10% noise is present in the data.
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