Diffuse Optical Data Research Articles

Hybrid time-domain and continuous-wave diffuse optical tomography instrument with concurrent, clinical magnetic resonance imaging for breast cancer imaging.

.Diffuse optical tomography has demonstrated significant potential for clinical utility in the diagnosis and prognosis of breast cancer, and its use in combination with other structural imaging modalities improves lesion localization and the quantification of functional tissue properties. Here, we introduce a hybrid diffuse optical imaging system that operates concurrently with magnetic resonance imaging (MRI) in the imaging suite, utilizing commercially available MR surface coils. The instrument acquires both continuous-wave and time-domain diffuse optical data in the parallel-plate geometry, permitting both absolute assignment of tissue optical properties and three-dimensional tomography; moreover, the instrument is designed to incorporate diffuse correlation spectroscopic measurements for probing tissue blood flow. The instrument is described in detail here. Image reconstructions of a tissue phantom are presented as an initial indicator of the system’s ability to accurately reconstruct optical properties and the concrete benefits of the spatial constraints provided by concurrent MRI. Last, we briefly discuss how various data combinations that the instrument could facilitate, including tissue perfusion, can enable more comprehensive assessment of lesion physiology.

Fast Algorithms for Hyperspectral Diffuse Optical Tomography

The image reconstruction of chromophore concentrations using diffuse optical tomography (DOT) data can be described mathematically as an ill-posed inverse problem. Recent work has shown that the use of hyperspectral DOT data, as opposed to data sets comprising a single or, at most, a dozen wavelengths, has the potential for improving the quality of the reconstructions. The use of hyperspectral diffuse optical data in the formulation and solution of the inverse problem poses a significant computational burden. The forward operator is, in actuality, nonlinear. However, under certain assumptions, a linear approximation, called the Born approximation, provides a suitable surrogate for the forward operator, and we assume this to be true in the present work. Computation of the Born matrix requires the solution of thousands of large scale discrete PDEs and the reconstruction problem requires matrix-vector products with the (dense) Born matrix. In this paper, we address both of these difficulties, thus making the...

Multimodality characterization of microstructure by the combination of diffusion NMR and time-domain diffuse optical data

Combining datasets with a model of the underlying physics prior to mapping of tissue provides a novel approach improving the estimation of parameters. We demonstrate this approach by merging near infrared diffuse optical signal data with diffusion NMR data to inform a model describing the microstructure of a sample. The study is conducted on a homogeneous emulsion of oil in a dispersion medium of water and proteins. The use of a protein based background, rich in collagen, introduces a similarity to real tissues compared to other models such as intralipids. The sample is investigated with the two modalities separately. Then, the two datasets are used to inform a combined model, and to estimate the size of the microstructural elements and the volume fraction. The combined model fits the microstructural properties by minimizing the difference between experimental and modelled data. The experimental results are validated with confocal laser scanning microscopy. The final results demonstrate that the combined model provides improved estimates of microstructural parameters compared to either individual model alone.

A random-effects model for group-level analysis of diffuse optical brain imaging

Diffuse optical imaging is a non-invasive technique for measuring changes in blood oxygenation in the brain. This technique is based on the temporally and spatially resolved recording of optical absorption in tissue within the near-infrared range of light. Optical imaging can be used to study functional brain activity similar to functional MRI. However, group level comparisons of brain activity from diffuse optical data are difficult due to registration of optical sensors between subjects. In addition, optical signals are sensitive to inter-subject differences in cranial anatomy and the specific arrangement of optical sensors relative to the underlying functional region. These factors can give rise to partial volume errors and loss of sensitivity and therefore must be accounted for in combining data from multiple subjects. In this work, we describe an image reconstruction approach using a parametric Bayesian model that simultaneously reconstructs group-level images of brain activity in the context of a random-effects analysis. Using this model, we demonstrate that localization accuracy and the statistical effects size of group-level reconstructions can be improved when compared to individualized reconstructions. In this model, we use the Restricted Maximum Likelihood (ReML) method to optimize a Bayesian random-effects model.

TU-E-201C-07: Computer Aided Detection for Diffuse Optical Mammography

Purpose: Diffuse Optical Tomography (DOT) provides multi‐parameter data on metabolic function, however interpretation of these data can be challenging. Computer Aided Detection (CAD) data analysis procedures for DOT are introduced and applied to derive composite signatures of malignancy in human breast tissue. In contrast to previous optical mammographyanalysis schemes, the new statistical approach utilizes distributions of several optical properties across multiple subjects and across the many voxels from each subject. The methodology is tested in a population of biopsy‐confirmed malignant (35) and benign (8) lesions. Methods: DOT CAD employs multi‐parameter, multi‐voxel, multi‐subject measurements to derive a simple function which transforms DOT images of tissue chromophores and scattering into a ‘probability of malignancy’ tomogram. The formalism incorporates both intra‐subject spatial heterogeneity and inter‐subject distributions of physiological properties derived from a population of cancer‐containing breasts. A weighted combination of physiological parameters define a ‘Malignancy Parameter’ (M). Logistic regression is currently utilized for weighting factor optimization. The utility of M is examined, employing 3D DOT images from an additional subject in a leave‐one‐out cross validation procedure. Results: Initial results confirm the automated technique can, without any human intervention, produce tomograms that distinguish healthy from malignant tissue. When compared with a gold standard tissue segmentation, this protocol produced an average true positive rate (sensitivity) of 89% and true negative rate (specificity) of 94% using an empirically chosen probability threshold. Conclusions: This study suggests the automated multi‐subject, multi‐voxel, multi‐parameter statistical analysis of diffuse optical data are potentially quite useful, producing tomograms which distinguish healthy from malignant tissue using the relatively simple logistic regression classifier. This type of data analysis may also prove useful for suppression of image artifacts.

Computer aided automatic detection of malignant lesions in diffuse optical mammography

Computer aided detection (CAD) data analysis procedures are introduced and applied to derive composite diffuse optical tomography (DOT) signatures of malignancy in human breast tissue. In contrast to previous optical mammography analysis schemes, the new statistical approach utilizes optical property distributions across multiple subjects and across the many voxels of each subject. The methodology is tested in a population of 35 biopsy-confirmed malignant lesions. DOT CAD employs multiparameter, multivoxel, multisubject measurements to derive a simple function that transforms DOT images of tissue chromophores and scattering into a probability of malignancy tomogram. The formalism incorporates both intrasubject spatial heterogeneity and intersubject distributions of physiological properties derived from a population of cancercontaining breasts (the training set). A weighted combination of physiological parameters from the training set define a malignancy parameter (M), with the weighting factors optimized by logistic regression to separate training-set cancer voxels from training-set healthy voxels. The utility of M is examined, employing 3D DOT images from an additional subjects (the test set). Initial results confirm that the automated technique can produce tomograms that distinguish healthy from malignant tissue. When compared to a gold standard tissue segmentation, this protocol produced an average true positive rate (sensitivity) of 89% and a true negative rate (specificity) of 94% using an empirically chosen probability threshold. This study suggests that the automated multisubject, multivoxel, multiparameter statistical analysis of diffuse optical data is potentially quite useful, producing tomograms that distinguish healthy from malignant tissue. This type of data analysis may also prove useful for suppression of image artifacts.

Three-dimensional shape-based imaging of absorption perturbation for diffuse optical tomography.

We present a shape-based approach to three-dimensional image reconstruction from diffuse optical data. Our approach differs from others in the literature in that we jointly reconstruct object and background characterization and localization simultaneously, rather than sequentially process for optical properties and postprocess for edges. The key to the efficiency and robustness of our algorithm is in the model we propose for the optical properties of the background and anomaly: We use a low-order parameterization of the background and another for the interior of the anomaly, and we use an ellipsoid to describe the boundary of the anomaly. This model has the effect of regularizing the inversion problem and provides a natural means of including additional physical properties if they are known a priori. A Gauss-Newton-type algorithm with line search is implemented to solve the underlying nonlinear least-squares problem and thereby determine the coefficients of the parameterizations and the descriptors of the ellipsoid. Numerical results show the effectiveness of this method.

A MATLAB package for the EIDORS project to reconstruct two-dimensionalEIT images

The EIDORS (electrical impedance and diffuse optical reconstruction software)project aims to produce a software system for reconstructing images fromelectrical or diffuse optical data. MATLAB is a software that is used in theEIDORS project for rapid prototyping, graphical user interface constructionand image display. We have written a MATLAB package(http://venda.uku.fi/ mvauhkon/) which can be used for two-dimensional meshgeneration, solving the forward problem and reconstructing and displaying thereconstructed images (resistivity or admittivity). In this paper we brieflydescribe the mathematical theory on which the codes are based on and also givesome examples of the capabilities of the package.