scholarly journals Ambiguity Suppression Based on Joint Optimization for Multichannel Hybrid and ±π/4 Quad-Pol SAR Systems

2021 ◽  
Vol 13 (10) ◽  
pp. 1907
Author(s):  
Pengfei Zhao ◽  
Yunkai Deng ◽  
Wei Wang ◽  
Yongwei Zhang ◽  
Robert Wang
Keyword(s):  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Joint Optimization ◽  
Reconstruction Method ◽  
Signal Ratio ◽  
Range Ambiguity ◽  
Multichannel Signal Processing ◽  
Imaging Simulation ◽  
Measurement Channels ◽  
The Cross

Hybrid and ±π/4 quadrature-polarimetric (quad-pol) synthetic aperture radar (SAR) systems operating from space can obtain all polarimetric components simultaneously but suffer from severe azimuth ambiguities in the cross-polarized (cross-pol) measurement channels. In this paper, the hybrid and ±π/4 quad-pol SAR systems with multiple receive channels in azimuth are widely investigated to suppress the azimuth ambiguities of the cross-pol components. We first provide a more thorough analysis of the multichannel hybrid and ±π/4 quad-pol SAR systems. Then, the multichannel signal processing is briefly discussed for the reconstruction of the quad-pol SAR signal from the aliased signals, in which the conventional reconstruction algorithm causes extremely severe azimuth ambiguities. To this end, an improved reconstruction method is proposed based on a joint optimization, which allows for the minimization of ambiguities from the desired polarization and the simultaneous power of undesired polarized signal. This method can largely suppress azimuth ambiguities compared with the conventional reconstruction algorithm. Finally, to verify the advantages and effectiveness of the proposed approach, the azimuth ambiguity-to-signal ratio (AASR), the range ambiguity-to-signal ratio (RASR) and signal-to-noise ratio (SNR) of all polarizations, as well as a set of imaging simulation results, are given to describe the effects of reconstruction on the multichannel hybrid and ±π/4 quad-pol SAR systems.

scholarly journals An Azimuth Signal-Reconstruction Method Based on Two-Step Projection Technology for Spaceborne Azimuth Multi-Channel High-Resolution and Wide-Swath SAR

2021 ◽  
Vol 13 (24) ◽  
pp. 4988
Author(s):  
Ning Li ◽  
Hanqing Zhang ◽  
Jianhui Zhao ◽  
Lin Wu ◽  
Zhengwei Guo
Keyword(s):  
High Resolution ◽  
Signal To Noise Ratio ◽  
Signal Reconstruction ◽  
Simulated Data ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Signal Ratio ◽  
Translation Invariant ◽  
Sampled Signal ◽  
Wide Swath

Azimuth non-uniform signal-reconstruction is a critical step for azimuth multi-channel high-resolution wide-swath (HRWS) synthetic aperture radar (SAR) data processing. However, the received non-uniform signal has noise in the actual azimuth multi-channel SAR (MCSAR) operation, which leads to the serious reduction in the signal-to-noise ratio (SNR) of the results processed by a traditional reconstruction algorithm. Aiming to address the problem of reducing the SNR of the traditional reconstruction algorithm in the reconstruction of non-uniform signal with noise, a novel signal-reconstruction algorithm based on two-step projection technology (TSPT) for the MCSAR system is proposed in this paper. The key part of the TSPT algorithm consists of a two-step projection. The first projection is to project the given signal into the selected intermediate subspace, spanned by the integer conversion of the compact support kernel function. This process generates a set of sparse equations, which can be solved efficiently by using the sparse equation solver. The second key projection is to project the first projection result into the subspace of the known sampled signal. The secondary projection can be achieved with a digital linear translation invariant (LSI) filter and generate a uniformly spaced signal. As a result, compared with the traditional azimuth MCSAR signal-reconstruction algorithm, the proposed algorithm can improve SNR and reduce the azimuth ambiguity-signal-ratio (AASR). The processing results of simulated data and real raw data verify the effectiveness of the proposed algorithm.

Nondestructive Inspection of Thin, Low-Z Samples using Multiplexed Compton Scatter Tomography

1997 ◽  
Vol 503 ◽  
Author(s):  
B. L. Evans ◽  
J. B. Martin ◽  
L. W. Burggraf
Keyword(s):  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Filtered Backprojection ◽  
Line Broadening ◽  
Compton Scatter ◽  
X Ray ◽  
Tomography System ◽  
Transmission Computed Tomography ◽  
High Purity Germanium ◽  
Scattered Energy

ABSTRACTThe viability of a Compton scattering tomography system for nondestructively inspecting thin, low Z samples for corrosion is examined. This technique differs from conventional x-ray backscatter NDI because it does not rely on narrow collimation of source and detectors to examine small volumes in the sample. Instead, photons of a single energy are backscattered from the sample and their scattered energy spectra are measured at multiple detector locations, and these spectra are then used to reconstruct an image of the object. This multiplexed Compton scatter tomography technique interrogates multiple volume elements simultaneously. Thin samples less than 1 cm thick and made of low Z materials are best imaged with gamma rays at or below 100 keV energy. At this energy, Compton line broadening becomes an important resolution limitation. An analytical model has been developed to simulate the signals collected in a demonstration system consisting of an array of planar high-purity germanium detectors. A technique for deconvolving the effects of Compton broadening and detector energy resolution from signals with additive noise is also presented. A filtered backprojection image reconstruction algorithm with similarities to that used in conventional transmission computed tomography is developed. A simulation of a 360–degree inspection gives distortion-free results. In a simulation of a single-sided inspection, a 5 mm × 5 mm corrosion flaw with 50% density is readily identified in 1-cm thick aluminum phantom when the signal to noise ratio in the data exceeds 28.

Image Reconstruction with the Fourier Coefficients for Magnetic Induction Tomography

2020 ◽  
Vol 16 (2) ◽  
pp. 156-163
Author(s):  
Jingwen Wang ◽  
Xu Wang ◽  
Dan Yang ◽  
Kaiyang Wang
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Magnetic Induction ◽  
Fourier Coefficients ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Measurement Equation ◽  
Image Reconstruction Method ◽  
Magnetic Induction Tomography

Background: Image reconstruction of magnetic induction tomography (MIT) is a typical ill-posed inverse problem, which means that the measurements are always far from enough. Thus, MIT image reconstruction results using conventional algorithms such as linear back projection and Landweber often suffer from limitations such as low resolution and blurred edges. Methods: In this paper, based on the recent finite rate of innovation (FRI) framework, a novel image reconstruction method with MIT system is presented. Results: This is achieved through modeling and sampling the MIT signals in FRI framework, resulting in a few new measurements, namely, fourier coefficients. Because each new measurement contains all the pixel position and conductivity information of the dense phase medium, the illposed inverse problem can be improved, by rebuilding the MIT measurement equation with the measurement voltage and the new measurements. Finally, a sparsity-based signal reconstruction algorithm is presented to reconstruct the original MIT image signal, by solving this new measurement equation. Conclusion: Experiments show that the proposed method has better indicators such as image error and correlation coefficient. Therefore, it is a kind of MIT image reconstruction method with high accuracy.

Model-based reconstruction algorithm in the detection of acute trauma-related lesions in brain CT examinations

2021 ◽  
pp. 197140092110087
Author(s):  
Andrea De Vito ◽  
Cesare Maino ◽  
Sophie Lombardi ◽  
Maria Ragusi ◽  
Cammillo Talei Franzesi ◽  
...  
Keyword(s):  
Image Quality ◽  
Iterative Reconstruction ◽  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Signal To Noise ◽  
Subjective Image Quality ◽  
Model Based ◽  
Hybrid Iterative Reconstruction ◽  
Noise Ratio ◽  
Enhanced Computed Tomography

Background and purpose To evaluate the added value of a model-based reconstruction algorithm in the assessment of acute traumatic brain lesions in emergency non-enhanced computed tomography, in comparison with a standard hybrid iterative reconstruction approach. Materials and methods We retrospectively evaluated a total of 350 patients who underwent a 256-row non-enhanced computed tomography scan at the emergency department for brain trauma. Images were reconstructed both with hybrid and model-based iterative algorithm. Two radiologists, blinded to clinical data, recorded the presence, nature, number, and location of acute findings. Subjective image quality was performed using a 4-point scale. Objective image quality was determined by computing the signal-to-noise ratio and contrast-to-noise ratio. The agreement between the two readers was evaluated using k-statistics. Results A subjective image quality analysis using model-based iterative reconstruction gave a higher detection rate of acute trauma-related lesions in comparison to hybrid iterative reconstruction (extradural haematomas 116 vs. 68, subdural haemorrhages 162 vs. 98, subarachnoid haemorrhages 118 vs. 78, parenchymal haemorrhages 94 vs. 64, contusive lesions 36 vs. 28, diffuse axonal injuries 75 vs. 31; all P<0.001). Inter-observer agreement was moderate to excellent in evaluating all injuries (extradural haematomas k=0.79, subdural haemorrhages k=0.82, subarachnoid haemorrhages k=0.91, parenchymal haemorrhages k=0.98, contusive lesions k=0.88, diffuse axonal injuries k=0.70). Quantitatively, the mean standard deviation of the thalamus on model-based iterative reconstruction images was lower in comparison to hybrid iterative one (2.12 ± 0.92 vsa 3.52 ± 1.10; P=0.030) while the contrast-to-noise ratio and signal-to-noise ratio were significantly higher (contrast-to-noise ratio 3.06 ± 0.55 vs. 1.55 ± 0.68, signal-to-noise ratio 14.51 ± 1.78 vs. 8.62 ± 1.88; P<0.0001). Median subjective image quality values for model-based iterative reconstruction were significantly higher ( P=0.003). Conclusion Model-based iterative reconstruction, offering a higher image quality at a thinner slice, allowed the identification of a higher number of acute traumatic lesions than hybrid iterative reconstruction, with a significant reduction of noise.

scholarly journals Optimization of SPECT/CT imaging protocols for quantitative and qualitative 99mTc SPECT

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Dennis Kupitz ◽  
Heiko Wissel ◽  
Jan Wuestemann ◽  
Stephanie Bluemel ◽  
Maciej Pech ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Quantitative Imaging ◽  
Scatter Correction ◽  
Reconstruction Algorithm ◽  
Weighting Factor ◽  
Quantitative Spect ◽  
Pet Ct ◽  
Spect Acquisition ◽  
The Mean ◽  
A Minor

Abstract Background The introduction of hybrid SPECT/CT devices enables quantitative imaging in SPECT, providing a methodological setup for quantitation using SPECT tracers comparable to PET/CT. We evaluated a specific quantitative reconstruction algorithm for SPECT data using a 99mTc-filled NEMA phantom. Quantitative and qualitative image parameters were evaluated for different parametrizations of the acquisition and reconstruction protocol to identify an optimized quantitative protocol. Results The reconstructed activity concentration (ACrec) and the signal-to-noise ratio (SNR) of all examined protocols (n = 16) were significantly affected by the parametrization of the weighting factor k used in scatter correction, the total number of iterations and the sphere volume (all, p < 0.0001). The two examined SPECT acquisition protocols (with 60 or 120 projections) had a minor impact on the ACrec and no significant impact on the SNR. In comparison to the known AC, the use of default scatter correction (k = 0.47) or object-specific scatter correction (k = 0.18) resulted in an underestimation of ACrec in the largest sphere volume (26.5 ml) by − 13.9 kBq/ml (− 16.3%) and − 7.1 kBq/ml (− 8.4%), respectively. An increase in total iterations leads to an increase in estimated AC and a decrease in SNR. The mean difference between ACrec and known AC decreased with an increasing number of total iterations (e.g., for 20 iterations (2 iterations/10 subsets) = − 14.6 kBq/ml (− 17.1%), 240 iterations (24i/10s) = − 8.0 kBq/ml (− 9.4%), p < 0.0001). In parallel, the mean SNR decreased significantly from 2i/10s to 24i/10s by 76% (p < 0.0001). Conclusion Quantitative SPECT imaging is feasible with the used reconstruction algorithm and hybrid SPECT/CT, and its consistent implementation in diagnostics may provide perspectives for quantification in routine clinical practice (e.g., assessment of bone metabolism). When combining quantitative analysis and diagnostic imaging, we recommend using two different reconstruction protocols with task-specific optimized setups (quantitative vs. qualitative reconstruction). Furthermore, individual scatter correction significantly improves both quantitative and qualitative results.

scholarly journals 2D In-Plane Sensitive Hall-Effect Sensor

Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 711
Author(s):  
Siya Lozanova ◽  
Ivan Kolev ◽  
Avgust Ivanov ◽  
Chavdar Roumenin
Keyword(s):  
Hall Effect ◽  
Signal To Noise Ratio ◽  
Hall Effect Sensor ◽  
Orthogonal Components ◽  
The Mean ◽  
The Cross ◽  
The Magnetic Field ◽  
Outer Contact ◽  
Hall Elements ◽  
Ic Technology

A new 2D (two-dimensional) in-plane sensitive Hall-effect sensor comprising two identical n-Si Greek-crosses is presented. Each of the crosses contains one central square contact and, symmetrically to each of their four sides, an outer contact is available. Outer electrode from one configuration is connected with the respective opposite contact from the other configuration, thus forming four parallel three-contact (3C) Hall elements. These original connections provide pairs of opposite supply currents in each of the cross-Hall structure. Also the obligatory load resistors in the outer contacts of 3С Hall elements are replaced by internal resistances of crosses themselves. The samples have been implemented by IC technology, using four masks. The magnetic field is parallel to the structures’ plane. The couples of opposite contacts of each Greek-cross are the outputs for the two orthogonal components of the magnetic vector at sensitivities S ≈ 115 V/AT whereas the cross-talk is very promising, reaching no more than 2.4%. The mean lowest detected magnetic induction B at a supply current Is = 3 mA over the frequency range f ≤ 500 Hz at a signal to noise ratio equal to unity, is Bmin ≈ 14 μT.

scholarly journals Source reconstruction for bioluminescence tomography via L1∕2 regularization

2018 ◽  
Vol 11 (02) ◽  
pp. 1750014 ◽  
Author(s):  
Jingjing Yu ◽  
Qiyue Li ◽  
Haiyu Wang
Keyword(s):  
Image Quality ◽  
Matching Pursuit ◽  
Imaging Modality ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Reconstruction Algorithms ◽  
Preclinical Research ◽  
Bioluminescence Tomography ◽  
Comparison Results ◽  
Stable Reconstruction

Bioluminescence tomography (BLT) is an important noninvasive optical molecular imaging modality in preclinical research. To improve the image quality, reconstruction algorithms have to deal with the inherent ill-posedness of BLT inverse problem. The sparse characteristic of bioluminescent sources in spatial distribution has been widely explored in BLT and many L1-regularized methods have been investigated due to the sparsity-inducing properties of L1 norm. In this paper, we present a reconstruction method based on L[Formula: see text] regularization to enhance sparsity of BLT solution and solve the nonconvex L[Formula: see text] norm problem by converting it to a series of weighted L1 homotopy minimization problems with iteratively updated weights. To assess the performance of the proposed reconstruction algorithm, simulations on a heterogeneous mouse model are designed to compare it with three representative sparse reconstruction algorithms, including the weighted interior-point, L1 homotopy, and the Stagewise Orthogonal Matching Pursuit algorithm. Simulation results show that the proposed method yield stable reconstruction results under different noise levels. Quantitative comparison results demonstrate that the proposed algorithm outperforms the competitor algorithms in location accuracy, multiple-source resolving and image quality.

An iterative reconstruction method for sparse-projection data for low-dose CT

2021 ◽  
pp. 1-16
Author(s):  
Ying Huang ◽  
Qian Wan ◽  
Zixiang Chen ◽  
Zhanli Hu ◽  
Guanxun Cheng ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Reconstructed Image ◽  
Projection Data ◽  
Reconstruction Method ◽  
X Ray ◽  
Image Structure ◽  
Reconstruction Performance ◽  
Risk Of Cancer ◽  
Suppress Noise

Reducing X-ray radiation is beneficial for reducing the risk of cancer in patients. There are two main approaches for achieving this goal namely, one is to reduce the X-ray current, and another is to apply sparse-view protocols to do image scanning and projections. However, these techniques usually lead to degradation of the reconstructed image quality, resulting in excessive noise and severe edge artifacts, which seriously affect the diagnosis result. In order to overcome such limitation, this study proposes and tests an algorithm based on guided kernel filtering. The algorithm combines the characteristics of anisotropic edges between adjacent image voxels, expresses the relevant weights with an exponential function, and adjusts the weights adaptively through local gray gradients to better preserve the image structure while suppressing noise information. Experiments show that the proposed method can effectively suppress noise and preserve the image structure. Comparing with similar algorithms, the proposed algorithm greatly improves the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) of the reconstructed image. The proposed algorithm has the best effect in quantitative analysis, which verifies the effectiveness of the proposed method and good image reconstruction performance. Overall, this study demonstrates that the proposed method can reduce the number of projections required for repeated CT scans and has potential for medical applications in reducing radiation doses.

scholarly journals Testing general relativity with the Doppler magnification effect

2019 ◽  
Vol 488 (3) ◽  
pp. 3759-3771 ◽  
Author(s):  
Sambatra Andrianomena ◽  
Camille Bonvin ◽  
David Bacon ◽  
Philip Bull ◽  
Chris Clarkson ◽  
...  
Keyword(s):  
General Relativity ◽  
Cross Correlation ◽  
Signal To Noise Ratio ◽  
Density Fluctuations ◽  
Signal To Noise ◽  
Tests Of General Relativity ◽  
Velocity Statistics ◽  
The Cross ◽  
Noise Ratio ◽  
Magnification Effect

ABSTRACT The apparent sizes and brightnesses of galaxies are correlated in a dipolar pattern around matter overdensities in redshift space, appearing larger on their near side and smaller on their far side. The opposite effect occurs for galaxies around an underdense region. These patterns of apparent magnification induce dipole and higher multipole terms in the cross-correlation of galaxy number density fluctuations with galaxy size/brightness (which is sensitive to the convergence field). This provides a means of directly measuring peculiar velocity statistics at low and intermediate redshift, with several advantages for performing cosmological tests of general relativity (GR). In particular, it does not depend on empirically calibrated scaling relations like the Tully–Fisher and Fundamental Plane methods. We show that the next generation of spectroscopic galaxy redshift surveys will be able to measure the Doppler magnification effect with sufficient signal-to-noise ratio to test GR on large scales. We illustrate this with forecasts for the constraints that can be achieved on parametrized deviations from GR for forthcoming low-redshift galaxy surveys with DESI and SKA2. Although the cross-correlation statistic considered has a lower signal-to-noise ratio than RSD, it will be a useful probe of GR since it is sensitive to different systematics.

scholarly journals Stochastic 3D Carbon Cloth GDL Reconstruction and Transport Prediction

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 572
Author(s):  
Yuan Gao ◽  
Teng Jin ◽  
Xiaoyan Wu
Keyword(s):  
Reconstruction Algorithm ◽  
Gas Diffusion ◽  
Reconstruction Method ◽  
Carbon Cloth ◽  
Statistical Characterization ◽  
Stochastic Reconstruction ◽  
Classical Simulation ◽  
The Many ◽  
Transport Prediction ◽  
Structure Properties

This paper presents the 3D carbon cloth gas diffusion layer (GDL) to predict transport behaviors of anisotropic structure properties. A statistical characterization and stochastic reconstruction method is established to construct the 3D micro-structure using the data from the true materials. Statistics of the many microstructure characteristics, such as porosity, pore size distribution, and shape of the void, are all quantified by image-based characterization. Furthermore, the stochastic reconstruction algorithm is proposed to generate random and anisotropic 3D microstructure models. The proposed method is demonstrated by some classical simulation prediction and to give the evaluation of the transport properties. Various reconstructed GDLs are also generated to demonstrate the capability of the proposed method. In the end, the adapted structure properties are offered to optimize the carbon cloth GDLs.

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