11 research outputs found
Addressing flux suppression, radio frequency interference, and selection of optimal solution intervals during radio interferometric calibration
The forthcoming Square Kilometre Array is expected to provide answers to some of the most intriguing questions about our Universe. However, as it is already noticeable from MeerKAT and other precursors, the amounts of data produced by these new instruments are significantly challenging to calibrate and image. Calibration of radio interferometric data is usually biased by incomplete sky models and radio frequency interference (RFI) resulting in calibration artefacts that limit the dynamic range and image fidelity of the resulting images. One of the most noticeable of these artefacts is the formation of spurious sources which causes suppression of real emissions. Fortunately, it has been shown that calibration algorithms employing heavy-tailed likelihood functions are less susceptible to this due to their robustness against outliers. Leveraging on recent developments in the field of complex optimisation, we implement a robust calibration algorithm using a Student’s t likelihood function and Wirtinger derivatives. The new algorithm, dubbed the robust solver, is incorporated as a subroutine into the newly released calibration software package CubiCal. We perform statistical analysis on the distribution of visibilities and provide an insight into the functioning of the robust solver and describe different scenarios where it will improve calibration. We use simulations to show that the robust solver effectively reduces the amount of flux suppressed from unmodelled sources both in direction independent and direction dependent calibration. Furthermore, the robust solver is shown to successfully mitigate the effects of low-level RFI when applied to a simulated and a real VLA dataset. Finally, we demonstrate that there are close links between the amount of flux suppressed from sources, the effects of the RFI and the employed solution interval during radio interferometric calibration. Hence, we investigate the effects of solution intervals and the different factors to consider in order to select adequate solution intervals. Furthermore, we propose a practical brute force method for selecting optimal solution intervals. The proposed method is successfully applied to a VLA dataset
Fully 3D Implementation of the End-to-end Deep Image Prior-based PET Image Reconstruction Using Block Iterative Algorithm
Deep image prior (DIP) has recently attracted attention owing to its
unsupervised positron emission tomography (PET) image reconstruction, which
does not require any prior training dataset. In this paper, we present the
first attempt to implement an end-to-end DIP-based fully 3D PET image
reconstruction method that incorporates a forward-projection model into a loss
function. To implement a practical fully 3D PET image reconstruction, which
could not be performed due to a graphics processing unit memory limitation, we
modify the DIP optimization to block-iteration and sequentially learn an
ordered sequence of block sinograms. Furthermore, the relative difference
penalty (RDP) term was added to the loss function to enhance the quantitative
PET image accuracy. We evaluated our proposed method using Monte Carlo
simulation with [F]FDG PET data of a human brain and a preclinical study
on monkey brain [F]FDG PET data. The proposed method was compared with
the maximum-likelihood expectation maximization (EM), maximum-a-posterior EM
with RDP, and hybrid DIP-based PET reconstruction methods. The simulation
results showed that the proposed method improved the PET image quality by
reducing statistical noise and preserved a contrast of brain structures and
inserted tumor compared with other algorithms. In the preclinical experiment,
finer structures and better contrast recovery were obtained by the proposed
method. This indicated that the proposed method can produce high-quality images
without a prior training dataset. Thus, the proposed method is a key enabling
technology for the straightforward and practical implementation of end-to-end
DIP-based fully 3D PET image reconstruction.Comment: 9 pages, 10 figure
Generalized averaged Gaussian quadrature and applications
A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal