Ludguier D. Montejo

Frequency-Domain Optical Tomographic Imaging of Arthritic Finger Joints

We are presenting data from the largest clinical trial on optical tomographic imaging of finger joints to date. Overall we evaluated 99 fingers of patients affected by rheumatoid arthritis (RA) and 120 fingers from healthy volunteers. Using frequency-domain imaging techniques we show that sensitivities and specificities of 0.85 and higher can be achieved in detecting RA. This is accomplished by deriving multiple optical parameters from the optical tomographic images and combining them for the statistical analysis. Parameters derived from the scattering coefficient perform slightly better than absorption derived parameters. Furthermore we found that data obtained at 600 MHz leads to better classification results than data obtained at 0 or 300 MHz.

Implementation of the equation of radiative transfer on block-structured grids for modeling light propagation in tissue

We present the first algorithm for solving the equation of radiative transfer (ERT) in the frequency domain (FD) on three-dimensional block-structured Cartesian grids (BSG). This algorithm allows for accurate modeling of light propagation in media of arbitrary shape with air-tissue refractive index mismatch at the boundary at increased speed compared to currently available structured grid algorithms. To accurately model arbitrarily shaped geometries the algorithm generates BSGs that are finely discretized only near physical boundaries and therefore less dense than fine grids. We discretize the FD-ERT using a combination of the upwind-step method and the discrete ordinates (SN) approximation. The source iteration technique is used to obtain the solution. We implement a first order interpolation scheme when traversing between coarse and fine grid regions. Effects of geometry and optical parameters on algorithm performance are evaluated using numerical phantoms (circular, cylindrical, and arbitrary shape) and varying the absorption and scattering coefficients, modulation frequency, and refractive index. The solution on a 3-level BSG is obtained up to 4.2 times faster than the solution on a single fine grid, with minimal increase in numerical error (less than 5%).

Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 1: feature extraction

Abstract. This is the first part of a two-part paper on the application of computer-aided diagnosis to diffuse optical tomography (DOT). An approach for extracting heuristic features from DOT images and a method for using these features to diagnose rheumatoid arthritis (RA) are presented. Feature extraction is the focus of Part 1, while the utility of five classification algorithms is evaluated in Part 2. The framework is validated on a set of 219 DOT images of proximal interphalangeal (PIP) joints. Overall, 594 features are extracted from the absorption and scattering images of each joint. Three major findings are deduced. First, DOT images of subjects with RA are statistically different (p<0.05) from images of subjects without RA for over 90% of the features investigated. Second, DOT images of subjects with RA that do not have detectable effusion, erosion, or synovitis (as determined by MRI and ultrasound) are statistically indistinguishable from DOT images of subjects with RA that do exhibit effusion, erosion, or synovitis. Thus, this subset of subjects may be diagnosed with RA from DOT images while they would go undetected by reviews of MRI or ultrasound images. Third, scattering coefficient images yield better one-dimensional classifiers. A total of three features yield a Youden index greater than 0.8. These findings suggest that DOT may be capable of distinguishing between PIP joints that are healthy and those affected by RA with or without effusion, erosion, or synovitis.

Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 2: image classification

Abstract. This is the second part of a two-part paper on the application of computer-aided diagnosis to diffuse optical tomography (DOT) for diagnosing rheumatoid arthritis (RA). A comprehensive analysis of techniques for the classification of DOT images of proximal interphalangeal joints of subjects with and without RA is presented. A method for extracting heuristic features from DOT images was presented in Part 1. The ability of five classification algorithms to accurately label each DOT image as belonging to a subject with or without RA is analyzed here. The algorithms of interest are the k-nearest-neighbors, linear and quadratic discriminant analysis, self-organizing maps, and support vector machines (SVM). With a polynomial SVM classifier, we achieve 100.0% sensitivity and 97.8% specificity. Lower bounds for these results (at 95.0% confidence level) are 96.4% and 93.8%, respectively. Image features most predictive of RA are from the spatial variation of optical properties and the absolute range in feature values. The optimal classifiers are low-dimensional combinations (<7 features). These results underscore the high potential for DOT to become a clinically useful diagnostic tool and warrant larger prospective clinical trials to conclusively demonstrate the ultimate clinical utility of this approach.

A finite-volume algorithm for modeling light transport with the time-independent simplified spherical harmonics approximation to the equation of radiative transfer

In this work we introduce the finite volume (FV) approximation to the simplified spherical harmonics (SPN) equations for modeling light propagation in tissue. The SPN equations, with partly reflective boundary conditions, are discretized on unstructured grids. The resulting system of linear equations is solved with a Krylov subspace iterative method called the generalized minimal residual (GMRES) algorithm. The accuracy of the FV-SPN algorithm is validated through numerical simulations of light propagation in a numerical phantom with embedded inhomogeneities. We use a FV implementation of the equation of radiative transfer (ERT) as the benchmark algorithm. Solutions obtained using the FV-SPN (N > 1) algorithm are compared to solutions obtained with the ERT and the diffusion equation (SP1). Compared to the SP1, the SP3 solutions obtained using the FV-SPN algorithm can better approximate ERT solutions near boundary sources and in the vicinity of void-like regions. Solutions using the SP3 algorithm are obtained 9.95 times faster than solutions with the ERT-based algorithm.

Measurement Operator for Angular Dependent Photon Propagation in Contact-Free Optical Tomography

Based on light propagation theory, the measurements of a contact-free imaging system with generalized optical components can be obtained from a linear transformation of the light intensity distribution on the surface of the imaging object. In this work, we derived the linear measurement operator needed to perform this transformation. Numerical experiments were designed and conducted for validation.

Frequency-domain optical tomographic image reconstruction algorithm with the simplified spherical harmonics (SP3) light propagation model

We introduce here the finite volume formulation of the frequency-domain simplified spherical harmonics model with n-th order absorption coefficients (FD-SPN) that approximates the frequency-domain equation of radiative transfer (FD-ERT). We then present the FD-SPN based reconstruction algorithm that recovers absorption and scattering coefficients in biological tissue. The FD-SPN model with 3rd order absorption coefficient (i.e., FD-SP3) is used as a forward model to solve the inverse problem. The FD-SP3 is discretized with a node-centered finite volume scheme and solved with a restarted generalized minimum residual (GMRES) algorithm. The absorption and scattering coefficients are retrieved using a limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm. Finally, the forward and inverse algorithms are evaluated using numerical phantoms with optical properties and size that mimic small-volume tissue such as finger joints and small animals. The forward results show that the FD-SP3 model approximates the FD-ERT (S12) solution within relatively high accuracy; the average error in the phase (<3.7%) and the amplitude (<7.1%) of the partial current at the boundary are reported. From the inverse results we find that the absorption and scattering coefficient maps are more accurately reconstructed with the SP3 model than those with the SP1 model. Therefore, this work shows that the FD-SP3 is an efficient model for optical tomographic imaging of small-volume media with non-diffuse properties both in terms of computational time and accuracy as it requires significantly lower CPU time than the FD-ERT (S12) and also it is more accurate than the FD-SP1.

A Dynamic Image Reconstruction Method with Spatio-Temporal Constraints

We introduce here a temporally constrained image reconstruction algorithm for fast dynamic imaging of the spatial distribution of tissue parameters such as oxy-hemoglobin, HbO2, or deoxy-hemoglobin, Hb, and their derived parameters, e.g., HbT or StO2. An unknown spatial-temporal distribution of the tissue parameter is represented by a combination of basis functions where bases are predefined and their coefficients are unknown. The performance of the new algorithm is evaluated using experimental studies with dynamic imaging of vascular disease in foot. The results show that the temporally constrained algorithm leads to 26- fold acceleration in the image reconstruction as compared to more traditional methods that have to reconstruct all time frames data sequentially.

Comparison of Classification Methods for Detection of Rheumatoid Arthritis with Optical Tomography

Using optical tomographic data from fingers affected by RA we compare the performance of 3 different classification methods. Linear discriminant and quadratic discriminant analysis methods yield high sensitivities while support-vector machine-based methods yield high specificities.

Implementation of the radiative transfer equation on block-structured grids for modeling fluorescence light propagation in tissue with arbitrary shape

We developed a method for solving the fluorescence equation of radiative transfer in the frequency domain on blockstructured grids. In this way fluorescence light propagation in arbitrarily shaped tissue can be modeled with high accuracy without compromising on the convergence speed of these codes. The block-structure grid generator is developed as a multi-purpose tool that can be used with many numerical schemes. We present results from numerical studies that show that it is possible to resolve curved boundaries with grids that maintain much of the intrinsic structure of Cartesian grids. The natural ordering of this grid allows for simplified algorithms. In simulation studies we found that we can reduce the error in boundary fluence by a factor of five by using a two-level block structured grid. The increase in computational cost is only two-fold. We compare benchmark solutions to results with various levels of refinement, boundary conditions, and different geometries.