6,456 research outputs found
Graph- and finite element-based total variation models for the inverse problem in diffuse optical tomography
Total variation (TV) is a powerful regularization method that has been widely
applied in different imaging applications, but is difficult to apply to diffuse
optical tomography (DOT) image reconstruction (inverse problem) due to complex
and unstructured geometries, non-linearity of the data fitting and
regularization terms, and non-differentiability of the regularization term. We
develop several approaches to overcome these difficulties by: i) defining
discrete differential operators for unstructured geometries using both finite
element and graph representations; ii) developing an optimization algorithm
based on the alternating direction method of multipliers (ADMM) for the
non-differentiable and non-linear minimization problem; iii) investigating
isotropic and anisotropic variants of TV regularization, and comparing their
finite element- and graph-based implementations. These approaches are evaluated
on experiments on simulated data and real data acquired from a tissue phantom.
Our results show that both FEM and graph-based TV regularization is able to
accurately reconstruct both sparse and non-sparse distributions without the
over-smoothing effect of Tikhonov regularization and the over-sparsifying
effect of L regularization. The graph representation was found to
out-perform the FEM method for low-resolution meshes, and the FEM method was
found to be more accurate for high-resolution meshes.Comment: 24 pages, 11 figures. Reviced version includes revised figures and
improved clarit
Real-time diffuse optical tomography using reduced-order light propagation models based on a priori anatomical and functional information
This paper proposes a new fast 3D image reconstruction
algorithm for Diffuse Optical Tomography using reduced
order polynomial mappings from the space of optical
tissue parameters into the space of flux measurements at
the detector locations. The polynomial mappings are
constructed through an iterative estimation process
involving structure detection, parameter estimation and
cross-validation using data generated by simulating a
diffusion approximation of the radiative transfer equation
incorporating a priori anatomical and functional
information provided by MR scans and prior psychological
evidence. Numerical simulation studies demonstrate that
reconstructed images are remarkably similar in quality as
those obtained using the standard approach, but obtained at
a fraction of the time
3D shape based reconstruction of experimental data in Diffuse Optical Tomography
Diffuse optical tomography (DOT) aims at recovering three-dimensional images of absorption and scattering parameters inside diffusive body based on small number of transmission measurements at the boundary of the body. This image reconstruction problem is known to be an ill-posed inverse problem, which requires use of prior information for successful reconstruction. We present a shape based method for DOT, where we assume a priori that the unknown body consist of disjoint subdomains with different optical properties. We utilize spherical harmonics expansion to parameterize the reconstruction problem with respect to the subdomain boundaries, and introduce a finite element (FEM) based algorithm that uses a novel 3D mesh subdivision technique to describe the mapping from spherical harmonics coefficients to the 3D absorption and scattering distributions inside a unstructured volumetric FEM mesh. We evaluate the shape based method by reconstructing experimental DOT data, from a cylindrical phantom with one inclusion with high absorption and one with high scattering. The reconstruction was monitored, and we found a 87% reduction in the Hausdorff measure between targets and reconstructed inclusions, 96% success in recovering the location of the centers of the inclusions and 87% success in average in the recovery for the volumes
Gradient-based quantitative image reconstruction in ultrasound-modulated optical tomography: first harmonic measurement type in a linearised diffusion formulation
Ultrasound-modulated optical tomography is an emerging biomedical imaging
modality which uses the spatially localised acoustically-driven modulation of
coherent light as a probe of the structure and optical properties of biological
tissues. In this work we begin by providing an overview of forward modelling
methods, before deriving a linearised diffusion-style model which calculates
the first-harmonic modulated flux measured on the boundary of a given domain.
We derive and examine the correlation measurement density functions of the
model which describe the sensitivity of the modality to perturbations in the
optical parameters of interest. Finally, we employ said functions in the
development of an adjoint-assisted gradient based image reconstruction method,
which ameliorates the computational burden and memory requirements of a
traditional Newton-based optimisation approach. We validate our work by
performing reconstructions of optical absorption and scattering in two- and
three-dimensions using simulated measurements with 1% proportional Gaussian
noise, and demonstrate the successful recovery of the parameters to within
+/-5% of their true values when the resolution of the ultrasound raster probing
the domain is sufficient to delineate perturbing inclusions.Comment: 12 pages, 6 figure
Parametric Level Set Methods for Inverse Problems
In this paper, a parametric level set method for reconstruction of obstacles
in general inverse problems is considered. General evolution equations for the
reconstruction of unknown obstacles are derived in terms of the underlying
level set parameters. We show that using the appropriate form of parameterizing
the level set function results a significantly lower dimensional problem, which
bypasses many difficulties with traditional level set methods, such as
regularization, re-initialization and use of signed distance function.
Moreover, we show that from a computational point of view, low order
representation of the problem paves the path for easier use of Newton and
quasi-Newton methods. Specifically for the purposes of this paper, we
parameterize the level set function in terms of adaptive compactly supported
radial basis functions, which used in the proposed manner provides flexibility
in presenting a larger class of shapes with fewer terms. Also they provide a
"narrow-banding" advantage which can further reduce the number of active
unknowns at each step of the evolution. The performance of the proposed
approach is examined in three examples of inverse problems, i.e., electrical
resistance tomography, X-ray computed tomography and diffuse optical
tomography
Diffuse Optical Tomography: Image Reconstruction and Verification
Introduction: In this study, we intend to use diffuse optical Tomography (DOT) as a noninvasive, safe and low cost technique that can be considered as a functional imaging method and mention the importance of image reconstruction in accuracy and procession of image. One of the most important and fastest methods in image reconstruction is the boundary element method (BEM). This method is introduced and employed in our works.Method: Generally, to image a biological tissue we must obtain its optical properties. In order to reach this goal we benefit from diffusion equation because tissue is highly scattering medium. Diffusion equation is solved by boundary element equation (BEM) in our research. First, we assume a double layer phantom with different scattering and absorption coefficients to simulate and verify precession and accuracy of image reconstruction by BEM. Light absorption can be affected by volume fraction of blood in skin. For a specific skin species the volume fraction is calculated and then the results are compared with the reconstructed values obtained by BEM. Since the depth of tissue is important in light absorption a two layer phantom with known values is made and the depths of layers are reconstructed by BEM then they are compared with the expected values. A homogenous phantom with known scattering and absorption coefficients was made and then these coefficients were reconstructed by BEM. Finally, an inhomogeneous phantom (phantom with defect) whose defect was in a known position was made and the absorption and scattering coefficients were reconstructed and compared with real values.Results: Comparison between real or simulated values and reconstructed values of scattering and absorption coefficients, volume fraction of blood and thickness of phantom layers by BEM shows maximum errors of 24%, 7% and 35%, respectively.Conclusion: Comparison between BEM data and real or simulated values shows an acceptable agreement. Consequently, we can rely on BEM as a beneficial method in diffuse optical tomography image reconstruction
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