scholarly journals X-rays image reconstruction using proximal algorithm and adapted TV regularization

2021 ◽  
Author(s):  
Aicha Allag ◽  
Redouane Drai ◽  
Tarek Boutkedjirt ◽  
Abdessalam Benammar ◽  
Wahiba Djerir
Keyword(s):  
Image Reconstruction ◽  
X Rays ◽  
Proximal Algorithm ◽  
Tv Regularization

scholarly journals X-rays Image reconstruction using Proximal Algorithm and adapted TV Regularization

2021 ◽  
Vol 348 ◽  
pp. 01011
Author(s):  
Aicha Allag ◽  
Redouane Drai ◽  
Tarek Boutkedjirt ◽  
Abdessalam Benammar ◽  
Wahiba Djerir
Keyword(s):  
Structural Information ◽  
X Rays ◽  
Reconstruction Algorithms ◽  
Reconstruction Problem ◽  
Tv Regularization ◽  
Object Based ◽  
Suggested Technique ◽  
Reconstruction Image ◽  
Ill Posed

Computed tomography (CT) aims to reconstruct an internal distribution of an object based on projection measurements. In the case of a limited number of projections, the reconstruction problem becomes significantly ill-posed. Practically, reconstruction algorithms play a crucial role in overcoming this problem. In the case of missing or incomplete data, and in order to improve the quality of the reconstruction image, the choice of a sparse regularisation by adding l1 norm is needed. The reconstruction problem is then based on using proximal operators. We are interested in the Douglas-Rachford method and employ total variation (TV) regularization. An efficient technique based on these concepts is proposed in this study. The primary goal is to achieve high-quality reconstructed images in terms of PSNR parameter and relative error. The numerical simulation results demonstrate that the suggested technique minimizes noise and artifacts while preserving structural information. The results are encouraging and indicate the effectiveness of the proposed strategy.

scholarly journals Learning-Based Image Reconstruction via Parallel Proximal Algorithm

2018 ◽  
Vol 25 (7) ◽  
pp. 989-993 ◽  
Author(s):  
Emrah Bostan ◽  
Ulugbek S. Kamilov ◽  
Laura Waller
Keyword(s):  
Image Reconstruction ◽  
Proximal Algorithm

scholarly journals Timing Analysis with INTEGRAL: Comparing Different Reconstruction Algorithms

10.14311/1312 ◽  
2011 ◽  
Vol 51 (1) ◽  
Author(s):  
V. Grinberg ◽  
I. Kreykenbohm ◽  
F. Fürst ◽  
J. Wilms ◽  
K. Pottschmidt ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Time Resolution ◽  
Timing Analysis ◽  
Light Curves ◽  
X Rays ◽  
Reconstruction Algorithms ◽  
X Ray ◽  
Coded Mask

INTEGRAL is one of the few instruments capable of detecting X-rays above 20 keV. It is therefore in principle well suited for studying X-ray variability in this regime. Because INTEGRAL uses coded mask instruments for imaging, the reconstruction of light curves of X-ray sources is highly non-trivial. We present results from a comparison of two commonly employed algorithms, which primarily measure flux from mask deconvolution (ii_lc_extract) and from calculating the pixel illuminated fraction (ii_light). Both methods agree well for timescales above about 10 s, the highest time resolution for which image reconstruction is possible. For higher time resolution, ii light produces meaningful results, although the overall variance of the lightcurves is not preserved.

scholarly journals Joint image reconstruction and segmentation: Comparison of two algorithms for few-view tomography

2019 ◽  
Vol 43 (6) ◽  
pp. 1008-1020 ◽  
Author(s):  
V.V. Vlasov ◽  
A.B. Konovalov ◽  
S.V. Kolchugin
Keyword(s):  
Image Reconstruction ◽  
High Frequency ◽  
Reconstruction Technique ◽  
Adaptive Segmentation ◽  
Reconstruction Accuracy ◽  
Piecewise Constant ◽  
X Ray ◽  
Exact Reconstruction ◽  
Tv Regularization ◽  
Good Potential

Two algorithms of few-view tomography are compared, specifically, the iterative Potts minimization algorithm (IPMA) and the algebraic reconstruction technique with TV-regularization and adaptive segmentation (ART-TVS). Both aim to reconstruct piecewise-constant structures, use the compressed sensing theory, and combine image reconstruction and segmentation procedures. Using a numerical experiment, it is shown that either algorithm can exactly reconstruct the Shepp-Logan phantom from as small as 7 views with noise characteristic of the medical applications of X-ray tomography. However, if an object has a complicated high-frequency structure (QR-code), the minimal number of views required for its exact reconstruction increases to 17–21 for ART-TVS and to 32–34 for IPMA. The ART-TVS algorithm developed by the authors is shown to outperform IPMA in reconstruction accuracy and speed and in resistance to abnormally high noise as well. ART-TVS holds good potential for further improvement.

Early results for the STIX image reconstruction problem: imaging from visibility amplitudes

2021 ◽  
Author(s):  
michele piana ◽  
paolo massa ◽  
emma perracchione ◽  
andrea francesco battaglia ◽  
federico benvenuto ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Image Reconstruction ◽  
Imaging System ◽  
X Rays ◽  
Pixel Detector ◽  
Reconstruction Problem ◽  
Solar Dynamics Observatory ◽  
X Ray ◽  
Early Results ◽  
The Fourier Transform

<p>The Spectrometer/Telescope for Imaging X-rays (STIX) is the instrument of the Solar Orbiter mission conceived for the observation of the hard X-ray flaring emission, with the objective of providing insights on the diagnosis of thermal and non-thermal accelerated electrons at the Sun. The STIX imaging system is composed of 30 pairs of tungsten grids, each one placed in front of a four-pixel detector, and produces as many Fourier components of the angular distribution of the flaring source, via Moiré pattern modulation. Therefore, the data recorded by STIX, named visibilities, can be interpreted as a sparse sampling of the Fourier transform of the X-ray signal and the corresponding image reconstruction problem requires the inversion of the Fourier transform from limited data, usually addressed with regularization techniques. Since the current calibration status of STIX measurements still prevents the use of visibility phases for imaging purposes, here we propose a parameter identification process based on forward fitting  just the amplitude of the experimental visibilities. Specifically, we have parameterized the flaring source by means of pre-assigned source shapes (e.g., circular and elliptical bi-variate Gaussian functions), and we relied on several approaches to non-linear optimization in order to estimating the shape parameters. In particular, we have implemented a forward-fit method based on deterministic chi-squared minimization, a stochastic optimization algorithm and a deep neural approach based on ensemble learning, also equipping them with an ad hoc statistical technique for uncertainty quantification. The performances of the three approaches are compared in the case of both microflares and M class events recorded by STIX during its commissioning phase and the validation of results is realized also exploiting the EUV information provided by the Atmospheric Imaging Assembly within the Solar Dynamics Observatory.</p>

Efficient implementation of X-ray ghost imaging based on a modified compressive sensing algorithm

2022 ◽  
Author(s):  
Haipeng Zhang ◽  
Ke Li ◽  
Changzhe Zhao ◽  
Jie Tang ◽  
Tiqiao Xiao
Keyword(s):  
Image Reconstruction ◽  
Compressive Sensing ◽  
Signal To Noise Ratio ◽  
Efficient Implementation ◽  
X Rays ◽  
Ghost Imaging ◽  
Signal To Noise ◽  
X Ray ◽  
Efficient Data ◽  
Reconstruction Time

Abstract Towards efficient implementation of X-ray ghost imaging (XGI), efficient data acquisition and fast image reconstruction together with high image quality are preferred. In view of radiation dose resulted from the incident X-rays, fewer measurements with sufficient signal-to-noise ratio (SNR) are always anticipated. Available methods based on linear and compressive sensing algorithms cannot meet all the requirements simultaneously. In this paper, a method based a modified compressive sensing algorithm called CGDGI, is developed to solve the problem encountered in available XGI methods. Simulation and experiments demonstrated the practicability of CGDGI-based method for the efficient implementation of XGI. The image reconstruction time of sub-second implicates that the proposed method has the potential for real time XGI.

scholarly journals X-Rays Tomographic Reconstruction Images using Proximal Methods based on L 1 Norm and TV Regularization

2018 ◽  
Vol 127 ◽  
pp. 236-245
Author(s):  
Aicha Allag ◽  
Redouane Drai ◽  
Abdessalem Benammar ◽  
Tarek Boutkedjirt
Keyword(s):  
Tomographic Reconstruction ◽  
Proximal Methods ◽  
X Rays ◽  
Tv Regularization

scholarly journals Count-based imaging model for the Spectrometer/Telescope for Imaging X-rays (STIX) in Solar Orbiter

2019 ◽  
Vol 624 ◽  
pp. A130 ◽  
Author(s):  
Paolo Massa ◽  
Michele Piana ◽  
Anna Maria Massone ◽  
Federico Benvenuto
Keyword(s):  
Image Reconstruction ◽  
Solar Flares ◽  
High Energy ◽  
Photon Flux ◽  
X Rays ◽  
X Ray ◽  
Imaging Model ◽  
Reconstruction Methods ◽  
Incoming Photon ◽  
Imaging Instrument

The Spectrometer/Telescope for Imaging X-rays (STIX) will study solar flares across the hard X-ray window provided by the Solar Orbiter cluster. Similarly to the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI), STIX is a visibility-based imaging instrument that will require Fourier-based image reconstruction methods. However, in this paper we show that as for RHESSI, count-based imaging is also possible for STIX. Specifically, we introduce and illustrate a mathematical model that mimics the STIX data formation process as a projection from the incoming photon flux into a vector consisting of 120 count components. Then we test the reliability of expectation maximization for image reconstruction in the case of several simulated configurations that are typical of flare morphology.

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