scholarly journals Convolutional Neural Networks Approach for Solar Reconstruction in SCAO Configurations

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2233 ◽  
Author(s):  
Sergio Luis Suárez Gómez ◽  
Carlos González-Gutiérrez ◽  
Francisco García Riesgo ◽  
Maria Luisa Sánchez Rodríguez ◽  
Francisco Javier Iglesias Rodríguez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Adaptive Optics ◽  
Reconstruction Algorithm ◽  
Reconstruction Technique ◽  
Solar Observation ◽  
The Sun ◽  
Reconstruction Algorithms ◽  
Turbulence Effects ◽  
Atmospheric Turbulence Effects

Correcting atmospheric turbulence effects in light with Adaptive Optics is necessary, since it produces aberrations in the wavefront of astronomical objects observed with telescopes from Earth. These corrections are performed classically with reconstruction algorithms; between them, neural networks showed good results. In the context of solar observation, the usage of Adaptive Optics on solar differs from nocturnal operations, bringing up a challenge to correct the image aberrations. In this work, a convolutional approach is given to address this issue, considering SCAO configurations. A reconstruction algorithm is presented, “Shack-Hartmann reconstruction with deep learning on solar–prototype” (proto-HELIOS), to correct on fixed solar images, achieving an average 85.39% of precision in the reconstruction. Additionally, results encourage to continue working with these techniques to achieve a reconstruction technique for all the regions of the sun.

scholarly journals Deep learning for the Sun

2020 ◽  
Vol 61 (3) ◽  
pp. 3.34-3.39
Author(s):  
John A Armstrong ◽  
Christopher M J Osborne ◽  
Lyndsay Fletcher
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Solar Activity ◽  
The Sun

Abstract John A Armstrong, Christopher M J Osborne and Lyndsay Fletcher examine how neural networks can be used to explore the nature and location of solar activity.

scholarly journals Use of Deep Learning to Improve the Computational Complexity of Reconstruction Algorithms in High Energy Physics

2021 ◽  
Vol 11 (23) ◽  
pp. 11467
Author(s):  
Núria Valls Canudas ◽  
Míriam Calvo Gómez ◽  
Elisabet Golobardes Ribé ◽  
Xavier Vilasis-Cardona
Keyword(s):  
Deep Learning ◽  
Particle Physics ◽  
High Energy Physics ◽  
Reconstruction Algorithm ◽  
High Energy ◽  
Electromagnetic Calorimeter ◽  
Training Dataset ◽  
Reconstruction Algorithms ◽  
Reconstruction Process ◽  
Small Parts

The optimization of reconstruction algorithms has become a key aspect in the field of experimental particle physics. Since technology has allowed gradually increasing the complexity of the measurements, the amount of data taken that needs to be interpreted has grown as well. This is the case with the LHCb experiment at CERN, where a major upgrade currently undergoing will considerably increase the data processing rate. This has presented the need to search for specific reconstruction techniques that aim to accelerate one of the most time consuming reconstruction algorithms in LHCb, the electromagnetic calorimeter clustering. Together with the use of deep learning techniques and the understanding of the current reconstruction algorithm, we propose a method that decomposes the reconstruction process into small parts that can be formulated as a cellular automaton. This approach is shown to benefit the generalized learning of small convolutional neural network architectures and also simplify the training dataset. Final results applied to a complete LHCb simulation reconstruction are compatible in terms of efficiency, and execute in nearly constant time with independence on the complexity of the data.

scholarly journals Variable Weighted Ordered Subset Image Reconstruction Algorithm

2006 ◽  
Vol 2006 ◽  
pp. 1-7 ◽  
Author(s):  
Jinxiao Pan ◽  
Tie Zhou ◽  
Yan Han ◽  
Ming Jiang
Keyword(s):  
Numerical Simulation ◽  
Iterative Reconstruction ◽  
Reconstruction Algorithm ◽  
Reconstruction Technique ◽  
Algebraic Reconstruction Technique ◽  
Reconstruction Algorithms ◽  
Ct Data ◽  
Simultaneous Algebraic Reconstruction Technique ◽  
Algebraic Reconstruction

We propose two variable weighted iterative reconstruction algorithms (VW-ART and VW-OS-SART) to improve the algebraic reconstruction technique (ART) and simultaneous algebraic reconstruction technique (SART) and establish their convergence. In the two algorithms, the weighting varies with the geometrical direction of the ray. Experimental results with both numerical simulation and real CT data demonstrate that the VW-ART has a significant improvement in the quality of reconstructed images over ART and OS-SART. Moreover, both VW-ART and VW-OS-SART are more promising in convergence speed than the ART and SART, respectively.

scholarly journals Fully Convolutional Approaches for Numerical Approximation of Turbulent Phases in Solar Adaptive Optics

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1630
Author(s):  
Francisco García Riesgo ◽  
Sergio Luis Suárez Gómez ◽  
Enrique Díez Alonso ◽  
Carlos González-Gutiérrez ◽  
Jesús Daniel Santos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
High Altitude ◽  
Adaptive Optics ◽  
Numerical Approximation ◽  
Reconstruction Algorithms ◽  
Wavefront Sensors ◽  
Night Time ◽  
Phase Recovery ◽  
Fully Convolutional Neural Networks

Information on the correlations from solar Shack–Hartmann wavefront sensors is usually used for reconstruction algorithms. However, modern applications of artificial neural networks as adaptive optics reconstruction algorithms allow the use of the full image as an input to the system intended for estimating a correction, avoiding approximations and a loss of information, and obtaining numerical values of those correlations. Although studied for night-time adaptive optics, the solar scenario implies more complexity due to the resolution of the solar images potentially taken. Fully convolutional neural networks were the technique chosen in this research to address this problem. In this work, wavefront phase recovery for adaptive optics correction is addressed, comparing networks that use images from the sensor or images from the correlations as inputs. As a result, this research shows improvements in performance for phase recovery with the image-to-phase approach. For recovering the turbulence of high-altitude layers, up to 93% similarity is reached.

scholarly journals Deep Learning approach to LHCb Calorimeter reconstruction using a Cellular Automaton

2021 ◽  
Vol 251 ◽  
pp. 04008
Author(s):  
Núria Valls Canudas ◽  
Xavier Vilasis Cardona ◽  
Míriam Calvo Gómez ◽  
Elisabet Golobardes Ribé
Keyword(s):  
Deep Learning ◽  
Reconstruction Algorithm ◽  
Electromagnetic Calorimeter ◽  
Training Dataset ◽  
Network Architectures ◽  
Reconstruction Algorithms ◽  
Reconstruction Process ◽  
Learning Techniques ◽  
Current Algorithm ◽  
Small Parts

The optimization of reconstruction algorithms has become a key aspect in LHCb as it is currently undergoing a major upgrade that will considerably increase the data processing rate. Aiming to accelerate the second most time consuming reconstruction process of the trigger, we propose an alternative reconstruction algorithm for the Electromagnetic Calorimeter of LHCb. Together with the use of deep learning techniques and the understanding of the current algorithm, our proposal decomposes the reconstruction process into small parts that benefit the generalized learning of small neural network architectures and simplifies the training dataset. This approach takes as input the full simulation data of the calorimeter and outputs a list of reconstructed clusters in a nearly constant time without any dependency in the event complexity.

scholarly journals Deep learning versus iterative image reconstruction algorithm for head CT in trauma

2022 ◽  
Author(s):  
Zlatan Alagic ◽  
Jacqueline Diaz Cardenas ◽  
Kolbeinn Halldorsson ◽  
Vitali Grozman ◽  
Stig Wallgren ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Reconstruction Algorithm ◽  
Weighted Kappa ◽  
Trauma Patients ◽  
Reconstruction Algorithms ◽  
Head Ct ◽  
Image Reconstruction Algorithm ◽  
Experienced Reader

Abstract Purpose To compare the image quality between a deep learning–based image reconstruction algorithm (DLIR) and an adaptive statistical iterative reconstruction algorithm (ASiR-V) in noncontrast trauma head CT. Methods Head CT scans from 94 consecutive trauma patients were included. Images were reconstructed with ASiR-V 50% and the DLIR strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H). The image quality was assessed quantitatively and qualitatively and compared between the different reconstruction algorithms. Inter-reader agreement was assessed by weighted kappa. Results DLIR-M and DLIR-H demonstrated lower image noise (p < 0.001 for all pairwise comparisons), higher SNR of up to 82.9% (p < 0.001), and higher CNR of up to 53.3% (p < 0.001) compared to ASiR-V. DLIR-H outperformed other DLIR strengths (p ranging from < 0.001 to 0.016). DLIR-M outperformed DLIR-L (p < 0.001) and ASiR-V (p < 0.001). The distribution of reader scores for DLIR-M and DLIR-H shifted towards higher scores compared to DLIR-L and ASiR-V. There was a tendency towards higher scores with increasing DLIR strengths. There were fewer non-diagnostic CT series for DLIR-M and DLIR-H compared to ASiR-V and DLIR-L. No images were graded as non-diagnostic for DLIR-H regarding intracranial hemorrhage. The inter-reader agreement was fair-good between the second most and the less experienced reader, poor-moderate between the most and the less experienced reader, and poor-fair between the most and the second most experienced reader. Conclusion The image quality of trauma head CT series reconstructed with DLIR outperformed those reconstructed with ASiR-V. In particular, DLIR-M and DLIR-H demonstrated significantly improved image quality and fewer non-diagnostic images. The improvement in qualitative image quality was greater for the second most and the less experienced readers compared to the most experienced reader.

scholarly journals A Computationally Efficient Reconstruction Algorithm for Circular Cone-Beam Computed Tomography Using Shallow Neural Networks

2020 ◽  
Vol 6 (12) ◽  
pp. 135
Author(s):  
Marinus J. Lagerwerf ◽  
Daniël M. Pelt ◽  
Willem Jan Palenstijn ◽  
Kees Joost Batenburg
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Reconstruction Algorithm ◽  
Circular Cone ◽  
Alternative Methods ◽  
Cone Beam ◽  
Reconstruction Algorithms ◽  
Reconstruction Accuracy ◽  
Fdk Algorithm

Circular cone-beam (CCB) Computed Tomography (CT) has become an integral part of industrial quality control, materials science and medical imaging. The need to acquire and process each scan in a short time naturally leads to trade-offs between speed and reconstruction quality, creating a need for fast reconstruction algorithms capable of creating accurate reconstructions from limited data. In this paper, we introduce the Neural Network Feldkamp–Davis–Kress (NN-FDK) algorithm. This algorithm adds a machine learning component to the FDK algorithm to improve its reconstruction accuracy while maintaining its computational efficiency. Moreover, the NN-FDK algorithm is designed such that it has low training data requirements and is fast to train. This ensures that the proposed algorithm can be used to improve image quality in high-throughput CT scanning settings, where FDK is currently used to keep pace with the acquisition speed using readily available computational resources. We compare the NN-FDK algorithm to two standard CT reconstruction algorithms and to two popular deep neural networks trained to remove reconstruction artifacts from the 2D slices of an FDK reconstruction. We show that the NN-FDK reconstruction algorithm is substantially faster in computing a reconstruction than all the tested alternative methods except for the standard FDK algorithm and we show it can compute accurate CCB CT reconstructions in cases of high noise, a low number of projection angles or large cone angles. Moreover, we show that the training time of an NN-FDK network is orders of magnitude lower than the considered deep neural networks, with only a slight reduction in reconstruction accuracy.

scholarly journals Pulmonary Nodule Detection in Chest Ct Using a Deep Learning-Based Reconstruction Algorithm

2021 ◽  
Author(s):  
C Franck ◽  
A Snoeckx ◽  
M Spinhoven ◽  
H El Addouli ◽  
S Nicolay ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pulmonary Nodule ◽  
Reconstruction Algorithm ◽  
Chest Ct ◽  
Reconstruction Algorithms ◽  
Point Scale ◽  
Chest Computed Tomography ◽  
Significant Difference ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

Abstract This study’s aim was to assess whether deep learning image reconstruction (DLIR) techniques are non-inferior to ASIR-V for the clinical task of pulmonary nodule detection in chest computed tomography. Up to 6 (range 3–6, mean 4.2) artificial lung nodules (diameter: 3, 5, 8 mm; density: −800, −630, +100 HU) were inserted at different locations in the Kyoto Kagaku Lungman phantom. In total, 16 configurations (10 abnormal, 6 normal) were scanned at 7.6, 3, 1.6 and 0.38 mGy CTDIvol (respectively 0, 60, 80 and 95% dose reduction). Images were reconstructed using 50% ASIR-V and a deep learning-based algorithm with low (DL-L), medium (DL-M) and high (DL-H) strength. Four chest radiologists evaluated 256 series by locating and scoring nodules on a five-point scale. No statistically significant difference was found among the reconstruction algorithms (p = 0.987, average across readers AUC: 0.555, 0.561, 0.557, 0.558 for ASIR-V, DL-L, DL-M, DL-H).

Split Bregman iteration based reconstruction algorithm for electrical capacitance tomography

2018 ◽  
Vol 41 (9) ◽  
pp. 2389-2399 ◽  
Author(s):  
Lian Lu ◽  
Guowei Tong ◽  
Ge Guo ◽  
Shi Liu
Keyword(s):  
Cost Function ◽  
Tikhonov Regularization ◽  
Measurement Data ◽  
Reconstruction Algorithm ◽  
Electrical Capacitance Tomography ◽  
Reconstruction Technique ◽  
Electrical Capacitance ◽  
Reconstruction Algorithms ◽  
Split Bregman ◽  
Capacitance Tomography

The electrical capacitance tomography (ECT) technique uses the measured capacitance data to reconstruct the permittivity distribution in a specific measurement area, in which the performances of reconstruction algorithms play a crucial role in the reliability of measurement results. According to the Tikhonov regularization technique, a new cost function with the total least squares technique and the ℓ1-norm based regularizer is presented, in which measurement noises, model deviations and the influence of the outliers in the measurement data are simultaneously considered. The split Bregman technique and the fast-iterative shrinkage-thresholding method are combined into a new iterative scheme to solve the proposed cost function efficiently. Numerical experiment results show that the proposed algorithm achieves the boost in the precision of reconstruction, and under the noise-free condition the image errors for the imaging targets simulated in this paper, that is, 8.4%, 12.4%, 13.5% and 6.4%, are smaller than the linear backprojection (LBP) algorithm, the Tikhonov regularization (TR) algorithm, the truncated singular value decomposition (TSVD) algorithm, the Landweber algorithm and the algebraic reconstruction technique (ART).

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