Model Of Diffuse Optical Tomography Research Articles

A homogenized cerebrospinal fluid model for diffuse optical tomography in the neonatal head

Diffuse optical tomography is a non-invasive and non-irradiating medical imaging technique that is particularly suitable for cerebral monitoring of newborns since it can be used at the bedside of the patient. Here, a new model for optical tomography in the neonatal brain is presented that takes into account the presence of arachnoid trabeculae in the cerebrospinal fluid (CSF). It is known that the classical diffusion approximation (DA) for light propagation is at the limit of validity in the CSF layer due to the low values of the absorption and scattering coefficients. The new model is obtained by the DA of the homogenized radiative transfer equation and is rigorously justified. Numerical results in two and three dimensions attest for the improved sensitivity of the new model to the presence of perturbations in the brain layer.

Frequency shifting model for diffuse optical tomography

The Diffuse Optical Tomography (DOT) imaging modality is an interesting research field for researchers since it has uncertainties in the solution space. DOT imaging modality is an unsolved scientific problem. Inverse problem solution and image reconstruction have never been of their best quality. Reconstructed images have low spatial resolution. The scattering nature of low-energized diffusive light is the obscuring effect for DOT modality. DOT has 3 functional sub-branches which are Continuous Wave, Time-Resolved, and Frequency-Domain. In this work, one new approach to Frequency Domain Diffuse Optical Tomography (FDDOT) biomedical optic imaging modality is presented to the readers. Frequency shifting data were added to the forward model problem which has source-detector couplings and several imaging voxels. 100 MHz center core light modulation frequency was selected as modulation frequency. 169 source-detector matches were used on back-reflected imaging geometry. Absorption coefficient µa was selected 0.1 cm−1. Scattering coefficient µs was selected 100 cm−1. 1-µm x, y, z cartesian grid coordinates were used in each direction for imaging tissue-like simulation media. A total of 100 frequency shifts were added to the forward model problem which has 5 Hz frequency step. 2 inclusion objects were embedded inside the imaging simulation phantom to be reconstructed. Their optical absorption coefficient µa = 0.7 cm−1. Two inclusion images were successfully reconstructed with the low contrast to noise ratio (CNR) and position error. The frequency shifting technique is first applied for FDDOT here in this work. This technique increased the total number of equations in the forward model problem; hence it is helping to solve the inverse problem more accurately. In this work, the positive effect of using frequency shifting methodology was observed. Differentiation of 2 embedded inclusions was completed and illustrated in this work. Two different scenarios were compared with each other. In the first scenario, only 100 MHz center core frequency was used for one source-detector match. In the second scenario, 100 additional shifted frequencies over 100 MHz center core frequency were added. It was seen that the second scenario which has more frequency shifts is superior to the general concept FDDOT methodology. This work shows that the frequency shift technique might be used for future FDDOT devices.

New nonlocal forward model for diffuse optical tomography.

The forward model in diffuse optical tomography (DOT) describes how light propagates through a turbid medium. It is often approximated by a diffusion equation (DE) that is numerically discretized by the classical finite element method (FEM). We propose a nonlocal diffusion equation (NDE) as a new forward model for DOT, the discretization of which is carried out with an efficient graph-based numerical method (GNM). To quantitatively evaluate the new forward model, we first conduct experiments on a homogeneous slab, where the numerical accuracy of both NDE and DE is compared against the existing analytical solution. We further evaluate NDE by comparing its image reconstruction performance (inverse problem) to that of DE. Our experiments show that NDE is quantitatively comparable to DE and is up to 64% faster due to the efficient graph-based representation that can be implemented identically for geometries in different dimensions. The proposed discretization method can be easily applied to other imaging techniques like diffuse correlation spectroscopy which are normally modeled by the diffusion equation.

A Hybrid Inversion Scheme Combining Markov Chain Monte Carlo and Iterative Methods for Determining Optical Properties of Random Media

Near-infrared spectroscopy (NIRS) including diffuse optical tomography is an imaging modality which makes use of diffuse light propagation in random media. When optical properties of a random medium are investigated from boundary measurements of reflected or transmitted light, iterative inversion schemes such as the Levenberg–Marquardt algorithm are known to fail when initial guesses are not close enough to the true value of the coefficient to be reconstructed. In this paper, we investigate how this weakness of iterative schemes is overcome using Markov chain Monte Carlo. Using time-resolved measurements performed against a polyurethane-based phantom, we present a case that the Levenberg–Marquardt algorithm fails to work but the proposed hybrid method works well. Then, with a toy model of diffuse optical tomography we illustrate that the Levenberg–Marquardt method fails when it is trapped by a local minimum but the hybrid method can escape from local minima by using the Metropolis–Hastings Markov chain Monte Carlo algorithm until it reaches the valley of the global minimum. The proposed hybrid scheme can be applied to different inverse problems in NIRS which are solved iteratively. We find that for both numerical and phantom experiments, optical properties such as the absorption and reduced scattering coefficients can be retrieved without being trapped by a local minimum when Monte Carlo simulation is run only about 100 steps before switching to an iterative method. The hybrid method is compared with simulated annealing. Although the Metropolis–Hastings MCMC arrives at the steady state at about 10,000 Monte Carlo steps, in the hybrid method the Monte Carlo simulation can be stopped way before the burn-in time.

Edge-promoting reconstruction of absorption and diffusivity in optical tomography

In optical tomography a physical body is illuminated with near-infrared light and the resulting outward photon flux is measured at the object boundary. The goal is to reconstruct internal optical properties of the body, such as absorption and diffusivity. In this work, it is assumed that the imaged object is composed of an approximately homogeneous background with clearly distinguishable embedded inhomogeneities. An algorithm for finding the maximum a posteriori estimate for the absorption and diffusion coefficients is introduced assuming an edge-preferring prior and an additive Gaussian measurement noise model. The method is based on iteratively combining a lagged diffusivity step and a linearization of the measurement model of diffuse optical tomography with priorconditioned LSQR. The performance of the reconstruction technique is tested via three-dimensional numerical experiments with simulated data.

A comparison of numerical methods for the forward model of diffuse optical tomography

A comparison of numerical methods for the forward model of diffuse optical tomography