7,182 research outputs found
A Framework for Directional and Higher-Order Reconstruction in Photoacoustic Tomography
Photoacoustic tomography is a hybrid imaging technique that combines high
optical tissue contrast with high ultrasound resolution. Direct reconstruction
methods such as filtered backprojection, time reversal and least squares suffer
from curved line artefacts and blurring, especially in case of limited angles
or strong noise. In recent years, there has been great interest in regularised
iterative methods. These methods employ prior knowledge on the image to provide
higher quality reconstructions. However, easy comparisons between regularisers
and their properties are limited, since many tomography implementations heavily
rely on the specific regulariser chosen. To overcome this bottleneck, we
present a modular reconstruction framework for photoacoustic tomography. It
enables easy comparisons between regularisers with different properties, e.g.
nonlinear, higher-order or directional. We solve the underlying minimisation
problem with an efficient first-order primal-dual algorithm. Convergence rates
are optimised by choosing an operator dependent preconditioning strategy. Our
reconstruction methods are tested on challenging 2D synthetic and experimental
data sets. They outperform direct reconstruction approaches for strong noise
levels and limited angle measurements, offering immediate benefits in terms of
acquisition time and quality. This work provides a basic platform for the
investigation of future advanced regularisation methods in photoacoustic
tomography.Comment: submitted to "Physics in Medicine and Biology". Changes from v1 to
v2: regularisation with directional wavelet has been added; new experimental
tests have been include
Quantum hierarchic models for information processing
Both classical and quantum computations operate with the registers of bits.
At nanometer scale the quantum fluctuations at the position of a given bit,
say, a quantum dot, not only lead to the decoherence of quantum state of this
bit, but also affect the quantum states of the neighboring bits, and therefore
affect the state of the whole register. That is why the requirement of reliable
separate access to each bit poses the limit on miniaturization, i.e, constrains
the memory capacity and the speed of computation. In the present paper we
suggest an algorithmic way to tackle the problem of constructing reliable and
compact registers of quantum bits. We suggest to access the states of quantum
register hierarchically, descending from the state of the whole register to the
states of its parts. Our method is similar to quantum wavelet transform, and
can be applied to information compression, quantum memory, quantum
computations.Comment: 14 pages, LaTeX, 1 eps figur
TurbuStat: Turbulence Statistics in Python
We present TurbuStat (v1.0): a Python package for computing turbulence
statistics in spectral-line data cubes. TurbuStat includes implementations of
fourteen methods for recovering turbulent properties from observational data.
Additional features of the software include: distance metrics for comparing two
data sets; a segmented linear model for fitting lines with a break-point; a
two-dimensional elliptical power-law model; multi-core fast-fourier-transform
support; a suite for producing simulated observations of fractional Brownian
Motion fields, including two-dimensional images and optically-thin HI data
cubes; and functions for creating realistic world coordinate system information
for synthetic observations. This paper summarizes the TurbuStat package and
provides representative examples using several different methods. TurbuStat is
an open-source package and we welcome community feedback and contributions.Comment: Accepted in AJ. 21 pages, 8 figure
Finding faint HI structure in and around galaxies: scraping the barrel
Soon to be operational HI survey instruments such as APERTIF and ASKAP will
produce large datasets. These surveys will provide information about the HI in
and around hundreds of galaxies with a typical signal-to-noise ratio of
10 in the inner regions and 1 in the outer regions. In addition, such
surveys will make it possible to probe faint HI structures, typically located
in the vicinity of galaxies, such as extra-planar-gas, tails and filaments.
These structures are crucial for understanding galaxy evolution, particularly
when they are studied in relation to the local environment. Our aim is to find
optimized kernels for the discovery of faint and morphologically complex HI
structures. Therefore, using HI data from a variety of galaxies, we explore
state-of-the-art filtering algorithms. We show that the intensity-driven
gradient filter, due to its adaptive characteristics, is the optimal choice. In
fact, this filter requires only minimal tuning of the input parameters to
enhance the signal-to-noise ratio of faint components. In addition, it does not
degrade the resolution of the high signal-to-noise component of a source. The
filtering process must be fast and be embedded in an interactive visualization
tool in order to support fast inspection of a large number of sources. To
achieve such interactive exploration, we implemented a multi-core CPU (OpenMP)
and a GPU (OpenGL) version of this filter in a 3D visualization environment
().Comment: 17 pages, 9 figures, 4 tables. Astronomy and Computing, accepte
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