4,457 research outputs found
Discrete Imaging Models for Three-Dimensional Optoacoustic Tomography using Radially Symmetric Expansion Functions
Optoacoustic tomography (OAT), also known as photoacoustic tomography, is an
emerging computed biomedical imaging modality that exploits optical contrast
and ultrasonic detection principles. Iterative image reconstruction algorithms
that are based on discrete imaging models are actively being developed for OAT
due to their ability to improve image quality by incorporating accurate models
of the imaging physics, instrument response, and measurement noise. In this
work, we investigate the use of discrete imaging models based on Kaiser-Bessel
window functions for iterative image reconstruction in OAT. A closed-form
expression for the pressure produced by a Kaiser-Bessel function is calculated,
which facilitates accurate computation of the system matrix.
Computer-simulation and experimental studies are employed to demonstrate the
potential advantages of Kaiser-Bessel function-based iterative image
reconstruction in OAT
Solving ill-posed inverse problems using iterative deep neural networks
We propose a partially learned approach for the solution of ill posed inverse
problems with not necessarily linear forward operators. The method builds on
ideas from classical regularization theory and recent advances in deep learning
to perform learning while making use of prior information about the inverse
problem encoded in the forward operator, noise model and a regularizing
functional. The method results in a gradient-like iterative scheme, where the
"gradient" component is learned using a convolutional network that includes the
gradients of the data discrepancy and regularizer as input in each iteration.
We present results of such a partially learned gradient scheme on a non-linear
tomographic inversion problem with simulated data from both the Sheep-Logan
phantom as well as a head CT. The outcome is compared against FBP and TV
reconstruction and the proposed method provides a 5.4 dB PSNR improvement over
the TV reconstruction while being significantly faster, giving reconstructions
of 512 x 512 volumes in about 0.4 seconds using a single GPU
A Dictionary Learning Approach with Overlap for the Low Dose Computed Tomography Reconstruction and Its Vectorial Application to Differential Phase Tomography
X-ray based Phase-Contrast Imaging (PCI) techniques have been demonstrated to enhance the visualization of soft tissues in comparison to conventional imaging methods. Nevertheless the delivered dose as reported in the literature of biomedical PCI applications often equals or exceeds the limits prescribed in clinical diagnostics. The optimization of new computed tomography strategies which include the development and implementation of advanced image reconstruction procedures is thus a key aspect. In this scenario, we implemented a dictionary learning method with a new form of convex functional. This functional contains in addition to the usual sparsity inducing and fidelity terms, a new term which forces similarity between overlapping patches in the superimposed regions. The functional depends on two free regularization parameters: a coefficient multiplying the sparsity-inducing L-1 norm of the patch basis functions coefficients, and a coefficient multiplying the L-2 norm of the differences between patches in the overlapping regions. The solution is found by applying the iterative proximal gradient descent method with FISTA acceleration. The gradient is computed by calculating projection of the solution and its error backprojection at each iterative step. We study the quality of the solution, as a function of the regularization parameters and noise, on synthetic data for which the solution is a-priori known. We apply the method on experimental data in the case of Differential Phase Tomography. For this case we use an original approach which consists in using vectorial patches, each patch having two components: one per each gradient component. The resulting algorithm, implemented in the European Synchrotron Radiation Facility tomography reconstruction code PyHST, has proven to be efficient and well-adapted to strongly reduce the required dose and the number of projections in medical tomography
A Dictionary Learning Approach with Overlap for the Low Dose Computed Tomography Reconstruction and Its Vectorial Application to Differential Phase Tomography
X-ray based Phase-Contrast Imaging (PCI) techniques have been demonstrated to enhance the visualization of soft tissues in comparison to conventional imaging methods. Nevertheless the delivered dose as reported in the literature of biomedical PCI applications often equals or exceeds the limits prescribed in clinical diagnostics. The optimization of new computed tomography strategies which include the development and implementation of advanced image reconstruction procedures is thus a key aspect. In this scenario, we implemented a dictionary learning method with a new form of convex functional. This functional contains in addition to the usual sparsity inducing and fidelity terms, a new term which forces similarity between overlapping patches in the superimposed regions. The functional depends on two free regularization parameters: a coefficient multiplying the sparsity-inducing L-1 norm of the patch basis functions coefficients, and a coefficient multiplying the L-2 norm of the differences between patches in the overlapping regions. The solution is found by applying the iterative proximal gradient descent method with FISTA acceleration. The gradient is computed by calculating projection of the solution and its error backprojection at each iterative step. We study the quality of the solution, as a function of the regularization parameters and noise, on synthetic data for which the solution is a-priori known. We apply the method on experimental data in the case of Differential Phase Tomography. For this case we use an original approach which consists in using vectorial patches, each patch having two components: one per each gradient component. The resulting algorithm, implemented in the European Synchrotron Radiation Facility tomography reconstruction code PyHST, has proven to be efficient and well-adapted to strongly reduce the required dose and the number of projections in medical tomography
Inversion of multiconfiguration complex EMI data with minimum gradient support regularization: A case study
Frequency-domain electromagnetic instruments allow the collection of data in
different configurations, that is, varying the intercoil spacing, the
frequency, and the height above the ground. Their handy size makes these tools
very practical for near-surface characterization in many fields of
applications, for example, precision agriculture, pollution assessments, and
shallow geological investigations. To this end, the inversion of either the
real (in-phase) or the imaginary (quadrature) component of the signal has
already been studied. Furthermore, in many situations, a regularization scheme
retrieving smooth solutions is blindly applied, without taking into account the
prior available knowledge. The present work discusses an algorithm for the
inversion of the complex signal in its entirety, as well as a regularization
method that promotes the sparsity of the reconstructed electrical conductivity
distribution. This regularization strategy incorporates a minimum gradient
support stabilizer into a truncated generalized singular value decomposition
scheme. The results of the implementation of this sparsity-enhancing
regularization at each step of a damped Gauss-Newton inversion algorithm (based
on a nonlinear forward model) are compared with the solutions obtained via a
standard smooth stabilizer. An approach for estimating the depth of
investigation, that is, the maximum depth that can be investigated by a chosen
instrument configuration in a particular experimental setting is also
discussed. The effectiveness and limitations of the whole inversion algorithm
are demonstrated on synthetic and real data sets
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