Analysis of algebraic reconstruction technique for accurate imaging of gas temperature and concentration based on tunable diode laser absorption spectroscopy

An improved algebraic reconstruction technique（ART） combined with tunable diode laser absorption spectroscopy（TDLAS） is presented in this paper for determining two-dimensional（2D） distribution of H2O concentration and temperature in a simulated combustion flame.This work aims to simulate the reconstruction of spectroscopic measurements by a multi-view parallel-beam scanning geometry and analyze the effects of projection rays on reconstruction accuracy.It finally proves that reconstruction quality dramatically increases with the number of projection rays increasing until more than 180 for 20 × 20 grid,and after that point,the number of projection rays has little influence on reconstruction accuracy.It is clear that the temperature reconstruction results are more accurate than the water vapor concentration obtained by the traditional concentration calculation method.In the present study an innovative way to reduce the error of concentration reconstruction and improve the reconstruction quality greatly is also proposed,and the capability of this new method is evaluated by using appropriate assessment parameters.By using this new approach,not only the concentration reconstruction accuracy is greatly improved,but also a suitable parallel-beam arrangement is put forward for high reconstruction accuracy and simplicity of experimental validation.Finally,a bimodal structure of the combustion region is assumed to demonstrate the robustness and universality of the proposed method.Numerical investigation indicates that the proposed TDLAS tomographic algorithm is capable of detecting accurate temperature and concentration profiles.This feasible formula for reconstruction research is expected to resolve several key issues in practical combustion devices.

ACCURATE ATTENUATION CORRECTION FOR ALGEBRAIC RECONSTRUCTION TECHNIQUE IN SPECT

We present a new iterative reconstruction algorithm to improve the algebraic reconstruction technique （ART） for the Single-Photon Emission Computed Tomography. Our method is a generalization of the Kaczmarz iterative algorithm for solving linear systems of equations and introduces exact and implicit attenuation correction derived from the attenuated Radon transform operator at each step of the algorithm. The performances of the presented algorithm have been tested upon various numerical experiments in presence of both strongly non-uniform attenuation and incomplete measurements data. We also tested the ability of our algorithm to handle moderate noisy data. Simulation studies demonstrate that the proposed method has a significant improvement in the quality of reconstructed images over ART. Moreover, convergence speed was improved and stability was established, facing noisy data, once we incorporate filtration procedure in our algorithm.

A new inversion method for reconstruction of plasmaspheric He^(+)density from EUV images

The Computer Tomography(CT)method is used for remote sensing the Earth’s plasmasphere.One challenge for image reconstruction is insufficient projection data,mainly caused by limited projection angles.In this study,we apply the Algebraic Reconstruction Technique(ART)and the minimization of the image Total Variation(TV)method,with a combination of priori knowledge of north–south symmetry,to reconstruct plasmaspheric He+density from simulated EUV images.The results demonstrate that incorporating priori assumption can be particularly useful when the projection data is insufficient.This method has good performance even with a projection angle of less than 150 degrees.The method of our study is expected to have applications in the Soft X-ray Imager(SXI)reconstruction for the Solar wind–Magnetosphere–Ionosphere Link Explorer(SMILE)mission.

Tropospheric Wet Refractivity Tomography Based on the BeiDou Satellite System

This paper presents a novel approach for assessing the precision of the wet refractivity field using BDS （BeiDou navigation satellite system） simulations only,GPS,and BDS＋GPS for the Shenzhen and Hongkong GNSS network.The simulations are carried out by adding artificial noise to a real observation dataset.Instead of using the δ and σ parameters computed from slant wet delay,as in previous studies,we employ the Bias and RMS parameters,computed from the tomography results of total voxels,in order to obtain a more direct and comprehensive evaluation of the precision of the refractivity field determination.The results show that：（1） the precision of tropospheric wet refractivity estimated using BDS alone （only 9 satellites used） is basically comparable to that of GPS; （2） BDS＋GPS （as of current operation） may not be able to significantly improve the data＇s spatial density for the application of refractivity tomography; and （3） any slight increase in the precision of refractivity tomography,particularly in the lower atmosphere,bears great significance for any applications dependent on the Chinese operational meteorological service.

Radar Imaging Based on Iterative Algorithms

It has long been realized that the problem of radar imaging is a special case of image reconstruction in which the data are incomplete and noisy. In other fields, iterative reconstruction algorithms have been used successfully to improve the image quality. This paper studies the application of iterative algorithms in radar imaging. A discrete model is first derived, and the iterative algorithms are then adapted to radar imaging. Although such algorithms are usually time consuming, this paper shows that, if the algorithms are appropriately simplified, it is possible to realize them even in real time. The efficiency of iterative algorithms is shown through computer simulations.

Dual-basis reconstruction techniques for tomographic PIV

As an inverse problem, particle reconstruction in tomographic particle image velocimetry attempts to solve a large-scale underdetermined linear system using an optimization technique. The most popular approach, the multiplicative algebraic reconstruction technique(MART), uses entropy as an objective function in the optimization. All available MART-based methods are focused on improving the efficiency and accuracy of particle reconstruction. However, those methods do not perform very well on dealing with ghost particles in highly seeded measurements. In this report, a new technique called dual-basis pursuit(DBP), which is based on the basis pursuit technique, is proposed for tomographic particle reconstruction. A template basis is introduced as a priori knowledge of a particle intensity distribution combined with a correcting basis to enable a full span of the solution space of the underdetermined linear system. A numerical assessment test with 2D synthetic images indicated that the DBP technique is superior to MART method, can completely recover a particle field when the number of particles per pixel(ppp) is less than 0.15, and can maintain a quality factor Q of above 0.8 for ppp up to 0.30. Unfortunately, the DBP method is difficult to utilize in 3D applications due to the cost of its excessive memory usage. Therefore, a dual-basis MART was designed that performed better than the traditional MART and can potentially be utilized for 3D applications.

An ART iterative reconstruction algorithm for computed tomography of diffraction enhanced imaging

X-ray diffraction enhanced imaging （DEI） has extremely high sensitivity for weakly absorbing low- Z samples in medical and biological fields. In this paper, we propose an Algebra Reconstruction Technique （ART） iterative reconstruction algorithm for computed tomography of diffraction enhanced imaging （DEI-CT）. An Ordered Subsets （OS） technique is used to accelerate the ART reconstruction. Few-view reconstruction is also studied, and a partial differential equation （PDE） type filter which has the ability of edge-preserving and denoising is used to improve the image quality and eliminate the artifacts. The proposed algorithm is validated with both the numerical simulations and the experiment at the Beijing synchrotron radiation facility （BSRF）.

Convergence Results of Landweber Iterations for Linear Systems

The Landweber scheme is a method for algebraic image reconstructions. The convergence behavior of the Landweber scheme is of both theoretical and practical importance. Using the diagonalization of matrix, we derive a neat iterative representation formula for the Landweber schemes and consequently establish the convergence conditions of Landweber iteration. This work refines our previous convergence results on the Landweber scheme.