5,620 research outputs found
An integral geometry lemma and its applications: the nonlocality of the Pavlov equation and a tomographic problem with opaque parabolic objects
As in the case of soliton PDEs in 2+1 dimensions, the evolutionary form of
integrable dispersionless multidimensional PDEs is non-local, and the proper
choice of integration constants should be the one dictated by the associated
Inverse Scattering Transform (IST). Using the recently made rigorous IST for
vector fields associated with the so-called Pavlov equation
, we have recently esatablished that, in
the nonlocal part of its evolutionary form , the formal
integral corresponding to the solutions of the Cauchy
problem constructed by such an IST is the asymmetric integral
. In this paper we show that this results could be guessed
in a simple way using a, to the best of our knowledge, novel integral geometry
lemma. Such a lemma establishes that it is possible to express the integral of
a fairly general and smooth function over a parabola of the
plane in terms of the integrals of over all straight lines non
intersecting the parabola. A similar result, in which the parabola is replaced
by the circle, is already known in the literature and finds applications in
tomography. Indeed, in a two-dimensional linear tomographic problem with a
convex opaque obstacle, only the integrals along the straight lines
non-intersecting the obstacle are known, and in the class of potentials
with polynomial decay we do not have unique solvability of the inverse
problem anymore. Therefore, for the problem with an obstacle, it is natural not
to try to reconstruct the complete potential, but only some integral
characteristics like the integral over the boundary of the obstacle. Due to the
above two lemmas, this can be done, at the moment, for opaque bodies having as
boundary a parabola and a circle (an ellipse).Comment: LaTeX, 13 pages, 3 figures. arXiv admin note: substantial text
overlap with arXiv:1507.0820
Histogram Tomography
In many tomographic imaging problems the data consist of integrals along
lines or curves. Increasingly we encounter "rich tomography" problems where the
quantity imaged is higher dimensional than a scalar per voxel, including
vectors tensors and functions. The data can also be higher dimensional and in
many cases consists of a one or two dimensional spectrum for each ray. In many
such cases the data contain not just integrals along rays but the distribution
of values along the ray. If this is discretized into bins we can think of this
as a histogram. In this paper we introduce the concept of "histogram
tomography". For scalar problems with histogram data this holds the possibility
of reconstruction with fewer rays. In vector and tensor problems it holds the
promise of reconstruction of images that are in the null space of related
integral transforms. For scalar histogram tomography problems we show how bins
in the histogram correspond to reconstructing level sets of function, while
moments of the distribution are the x-ray transform of powers of the unknown
function. In the vector case we give a reconstruction procedure for potential
components of the field. We demonstrate how the histogram longitudinal ray
transform data can be extracted from Bragg edge neutron spectral data and
hence, using moments, a non-linear system of partial differential equations
derived for the strain tensor. In x-ray diffraction tomography of strain the
transverse ray transform can be deduced from the diffraction pattern the full
histogram transverse ray transform cannot. We give an explicit example of
distributions of strain along a line that produce the same diffraction pattern,
and characterize the null space of the relevant transform.Comment: Small corrections from last versio
3D Coronal Density Reconstruction and Retrieving the Magnetic Field Structure during Solar Minimum
Measurement of the coronal magnetic field is a crucial ingredient in
understanding the nature of solar coronal phenomena at all scales. We employed
STEREO/COR1 data obtained during a deep minimum of solar activity in February
2008 (Carrington rotation CR 2066) to retrieve and analyze the
three-dimensional (3D) coronal electron density in the range of heights from
1.5 to 4 Rsun using a tomography method. With this, we qualitatively deduced
structures of the coronal magnetic field. The 3D electron density analysis is
complemented by the 3D STEREO/EUVI emissivity in the 195 A band obtained by
tomography for the same CR. A global 3D MHD model of the solar corona was used
to relate the reconstructed 3D density and emissivity to open/closed magnetic
field structures. We show that the density maximum locations can serve as an
indicator of current sheet position, while the locations of the density
gradient maximum can be a reliable indicator of coronal hole boundaries. We
find that the magnetic field configuration during CR 2066 has a tendency to
become radially open at heliocentric distances greater than 2.5 Rsun. We also
find that the potential field model with a fixed source surface (PFSS) is
inconsistent with the boundaries between the regions with open and closed
magnetic field structures. This indicates that the assumption of the potential
nature of the coronal global magnetic field is not satisfied even during the
deep solar minimum. Results of our 3D density reconstruction will help to
constrain solar coronal field models and test the accuracy of the magnetic
field approximations for coronal modeling.Comment: Published in "Solar Physics
Gaussian process tomography for soft x-ray spectroscopy at WEST without equilibrium information
International audienceGaussian process tomography (GPT) is a recently developed tomography method based on the Bayesian probability theory [J. Svensson, JET Internal Report EFDA-JET-PR(11)24, 2011 and Li et al., Rev. Sci. Instrum. 84, 083506 (2013)]. By modeling the soft X-ray (SXR) emissivity field in a poloidal cross section as a Gaussian process, the Bayesian SXR tomography can be carried out in a robust and extremely fast way. Owing to the short execution time of the algorithm, GPT is an important candidate for providing real-time reconstructions with a view to impurity transport and fast magnetohydrodynamic control. In addition, the Bayesian formalism allows quantifying uncertainty on the inferred parameters. In this paper, the GPT technique is validated using a synthetic data set expected from the WEST tokamak, and the results are shown of its application to the reconstruction of SXR emissivity profiles measured on Tore Supra. The method is compared with the standard algorithm based on minimization of the Fisher information
Tomographic reconstruction of quantum states in N spatial dimensions
Most quantum tomographic methods can only be used for one-dimensional
problems. We show how to infer the quantum state of a non-relativistic
N-dimensional harmonic oscillator system by simple inverse Radon transforms.
The procedure is equally applicable to finding the joint quantum state of
several distinguishable particles in different harmonic oscillator potentials.
A requirement of the procedure is that the angular frequencies of the N
harmonic potentials are incommensurable. We discuss what kind of information
can be found if the requirement of incommensurability is not fulfilled and also
under what conditions the state can be reconstructed from finite time
measurements. As a further example of quantum state reconstruction in N
dimensions we consider the two related cases of an N-dimensional free particle
with periodic boundary conditions and a particle in an N-dimensional box, where
we find a similar condition of incommensurability and finite recurrence time
for the one-dimensional system.Comment: 8 pages, 1 figur
Direct and Inverse Computational Methods for Electromagnetic Scattering in Biological Diagnostics
Scattering theory has had a major roll in twentieth century mathematical
physics. Mathematical modeling and algorithms of direct,- and inverse
electromagnetic scattering formulation due to biological tissues are
investigated. The algorithms are used for a model based illustration technique
within the microwave range. A number of methods is given to solve the inverse
electromagnetic scattering problem in which the nonlinear and ill-posed nature
of the problem are acknowledged.Comment: 61 pages, 5 figure
Visual Quality Enhancement in Optoacoustic Tomography using Active Contour Segmentation Priors
Segmentation of biomedical images is essential for studying and
characterizing anatomical structures, detection and evaluation of pathological
tissues. Segmentation has been further shown to enhance the reconstruction
performance in many tomographic imaging modalities by accounting for
heterogeneities of the excitation field and tissue properties in the imaged
region. This is particularly relevant in optoacoustic tomography, where
discontinuities in the optical and acoustic tissue properties, if not properly
accounted for, may result in deterioration of the imaging performance.
Efficient segmentation of optoacoustic images is often hampered by the
relatively low intrinsic contrast of large anatomical structures, which is
further impaired by the limited angular coverage of some commonly employed
tomographic imaging configurations. Herein, we analyze the performance of
active contour models for boundary segmentation in cross-sectional optoacoustic
tomography. The segmented mask is employed to construct a two compartment model
for the acoustic and optical parameters of the imaged tissues, which is
subsequently used to improve accuracy of the image reconstruction routines. The
performance of the suggested segmentation and modeling approach are showcased
in tissue-mimicking phantoms and small animal imaging experiments.Comment: Accepted for publication in IEEE Transactions on Medical Imagin
Singular value decomposition for the 2D fan-beam Radon transform of tensor fields
In this article we study the fan-beam Radon transform of
symmetrical solenoidal 2D tensor fields of arbitrary rank in a unit disc
as the operator, acting from the object space to the data space
The orthogonal polynomial basis of solenoidal tensor
fields on the disc was built with the help of Zernike polynomials
and then a singular value decomposition (SVD) for the operator
was obtained. The inversion formula for the fan-beam tensor transform follows from this decomposition. Thus obtained inversion formula can be
used as a tomographic filter for splitting a known tensor field into potential
and solenoidal parts. Numerical results are presented.Comment: LaTeX, 37 pages with 5 figure
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