Image reconstruction by solving the inverse problem in ultrasonic transmission tomography system

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tomasz Rymarczyk ◽  
Konrad Kania ◽  
Michał Gołąbek ◽  
Jan Sikora ◽  
Michał Maj ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Measuring System ◽  
Practical Implementation ◽  
Projection Data ◽  
Reconstruction Algorithms ◽  
Transmission Tomography ◽  
Content Type ◽  
Fermat Principle ◽  
Finite Set ◽  
Transformation Methods

Purpose The purpose of this study is to develop a reconstruction and measurement system for data analysis using ultrasonic transmission tomography. The problem of reconstruction from the projection is encountered in practical implementation, which consists in reconstructing an image that is an estimation of an unknown object from a finite set of projection data. Reconstructive algorithms used in transmission tomography are based on linear mathematical models, which makes it necessary to process non-linear data into estimates for a finite number of projections. The application of transformation methods requires building a mathematical model in which the projection data forming the known and unknown quantities are functions with arguments from a continuous set of real numbers, determining the function describing the unknown quantities sought in the form of inverse relation and adapting it to operate on discrete and noisy data. This was done by designing a tomographic device and proprietary algorithms capable of reconstructing two-dimensional images regardless of the size, shape, location or number of inclusions hidden in the examined object. Design/methodology/approach The application consists of a device and measuring sensors, as well as proprietary algorithms for image reconstruction. Ultrasonic transmission tomography makes it possible to analyse processes occurring in an object without interfering with the examined object. The proposed solution uses algorithms based on ray integration, the Fermat principle and deterministic methods. Two applications were developed, one based on C and implemented on the embedded device, while the other application was made in Matlab. Findings Research shows that ultrasonic transmission tomography provides an effective analysis of tested objects in closed tanks. Research limitations/implications In the presented technique, the use of ultrasonic absorption wave has been limited. Nevertheless, the effectiveness of such a solution has been confirmed. Practical implications The presented solution can be used for research and monitoring of technological processes. Originality/value Author’s tomographic system consisting of a measuring system and image reconstruction algorithms.

Ultra-limited-angle CT image reconstruction algorithm based on reweighting and edge-preserving

2022 ◽  
pp. 1-13
Author(s):  
Lei Shi ◽  
Gangrong Qu ◽  
Yunsong Zhao
Keyword(s):  
Image Reconstruction ◽  
Condition Number ◽  
Reconstruction Algorithm ◽  
Projection Data ◽  
Ct Image ◽  
Reconstruction Algorithms ◽  
Edge Preserving ◽  
Image Reconstruction Algorithm ◽  
Ct Image Reconstruction ◽  
Limited Angle

BACKGROUND: Ultra-limited-angle image reconstruction problem with a limited-angle scanning range less than or equal to π 2 is severely ill-posed. Due to the considerably large condition number of a linear system for image reconstruction, it is extremely challenging to generate a valid reconstructed image by traditional iterative reconstruction algorithms. OBJECTIVE: To develop and test a valid ultra-limited-angle CT image reconstruction algorithm. METHODS: We propose a new optimized reconstruction model and Reweighted Alternating Edge-preserving Diffusion and Smoothing algorithm in which a reweighted method of improving the condition number is incorporated into the idea of AEDS image reconstruction algorithm. The AEDS algorithm utilizes the property of image sparsity to improve partially the results. In experiments, the different algorithms (the Pre-Landweber, AEDS algorithms and our algorithm) are used to reconstruct the Shepp-Logan phantom from the simulated projection data with noises and the flat object with a large ratio between length and width from the real projection data. PSNR and SSIM are used as the quantitative indices to evaluate quality of reconstructed images. RESULTS: Experiment results showed that for simulated projection data, our algorithm improves PSNR and SSIM from 22.46db to 39.38db and from 0.71 to 0.96, respectively. For real projection data, our algorithm yields the highest PSNR and SSIM of 30.89db and 0.88, which obtains a valid reconstructed result. CONCLUSIONS: Our algorithm successfully combines the merits of several image processing and reconstruction algorithms. Thus, our new algorithm outperforms significantly other two algorithms and is valid for ultra-limited-angle CT image reconstruction.

A review on image reconstruction algorithms for electrical capacitance/resistance tomography

Sensor Review ◽  
2016 ◽  
Vol 36 (4) ◽  
pp. 429-445 ◽  
Author(s):  
Ziqiang Cui ◽  
Qi Wang ◽  
Qian Xue ◽  
Wenru Fan ◽  
Lingling Zhang ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Flow Measurement ◽  
Electrical Capacitance Tomography ◽  
Electrical Capacitance ◽  
Reconstruction Algorithms ◽  
Content Type ◽  
Multi Phase Flow ◽  
Multi Phase ◽  
Image Reconstruction Algorithms ◽  
Capacitance Tomography

Purpose Electrical capacitance tomography (ECT) and electrical resistance tomography (ERT) are promising techniques for multiphase flow measurement due to their high speed, low cost, non-invasive and visualization features. There are two major difficulties in image reconstruction for ECT and ERT: the “soft-field”effect, and the ill-posedness of the inverse problem, which includes two problems: under-determined problem and the solution is not stable, i.e. is very sensitive to measurement errors and noise. This paper aims to summarize and evaluate various reconstruction algorithms which have been studied and developed in the word for many years and to provide reference for further research and application. Design/methodology/approach In the past 10 years, various image reconstruction algorithms have been developed to deal with these problems, including in the field of industrial multi-phase flow measurement and biological medical diagnosis. Findings This paper reviews existing image reconstruction algorithms and the new algorithms proposed by the authors for electrical capacitance tomography and electrical resistance tomography in multi-phase flow measurement and biological medical diagnosis. Originality/value The authors systematically summarize and evaluate various reconstruction algorithms which have been studied and developed in the word for many years and to provide valuable reference for practical applications.

scholarly journals Research electrical impedance tomography system suitable for making in out of factory conditions.

2021 ◽  
Vol 2021 (9) ◽  
Author(s):  
A.V. Korjenevsky ◽  
Keyword(s):  
Image Reconstruction ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Direct Interaction ◽  
Measuring System ◽  
Dynamic Visualization ◽  
Reconstruction Algorithms ◽  
Impedance Tomography ◽  
Tomography System ◽  
Wireless Interfaces

The objective of this work is to present the first results of the development of an electrical impedance tomography system assembled from ready-made blocks with free software. Electrical impedance tomography is considered as a possible alternative and adjunct to computer tomography in lung diagnostics. One of the problems along this path is the mutual inaccessibility of equipment of an acceptable level for research physicians and of contacts with such physicians for the developers. An easily reproducible EIT hardware platform from ready-made modules and open source software have been developed and tested as an option for enthusiastic researchers who do not have sufficient experience to independently develop the hardware and software and the means to purchase expensive commercial devices. An evaluation board for an integrated bioimpedance meter, produced by the microcircuit manufacturer, is used to implement the main elements of the measuring system. The 16-channel multiplexers are also available as ready-made modules. A high-performance controller module based on a 32-bit system-on-chip ESP32 with built-in wireless interfaces has a compiler and SDK ported to the Arduino environment. This makes customization and testing of the embedded software possible for non-core professionals. Image reconstruction algorithms are available online on the Institute's server. Both dynamic (visualization of changes only) and static imaging are possible. The results of tomography system testing on the phantom and on the human body demonstrated the high quality of the data collected. Improving the speed of the measuring system and adding software functions, including the use of wireless interfaces for data transmission and direct interaction with the image reconstruction server are topical tasks.

Image reconstruction for transmission tomography when projection data are incomplete

2006 ◽  
Vol 51 (21) ◽  
pp. 5603-5619 ◽  
Author(s):  
Donald L Snyder ◽  
Joseph A O'Sullivan ◽  
Ryan J Murphy ◽  
David G Politte ◽  
Bruce R Whiting ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Projection Data ◽  
Transmission Tomography

scholarly journals Ordered Subset Expectation Maximum Algorithms Based on Symmetric Structure for Image Reconstruction

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 449
Author(s):  
Chang Liu ◽  
Jun Qiu
Keyword(s):  
Image Reconstruction ◽  
Coefficient Matrix ◽  
Experimental Results ◽  
Projection Data ◽  
Reconstruction Algorithms ◽  
New Approaches ◽  
Symmetric Structure ◽  
Geometric Symmetry ◽  
Symmetry Structure ◽  
Imaging Speed

In this paper, we propose the symmetric structure of the reconstructed points discretization model to partition and order the subsets of Ordered Subset Expectation Maximum (OSEM) algorithms for image reconstruction and then simplify the calculation of the projection coefficient matrix while satisfying the balancing properties of subsets. The reconstructed points discretization model was utilized to describe the forward and inverse relationships between the reconstructed points and the projection data according to the distance from the point to the ray rather than the intersection length between the square pixel and the ray. This discretization model provides new approaches for improving and constructing the reconstruction algorithms on the basis of the geometry of the model. The experimental results show that the OSEM algorithms based on the reconstructed points discretization model and its geometric symmetry structure can effectively improve the imaging speed and the imaging precision.

Optical sensor of components concentration in solution

Sensor Review ◽  
2019 ◽  
Vol 39 (5) ◽  
pp. 704-707
Author(s):  
Igor S. Nadezhdin ◽  
Aleksey G. Goryunov ◽  
Yuliya Yu Nadezhdina
Keyword(s):  
Sensitive Element ◽  
Light Signal ◽  
Measuring System ◽  
Practical Implementation ◽  
Remote Measurement ◽  
Content Type ◽  
Optical Cable ◽  
Require Power ◽  
Fiber Optical ◽  
Measurement Principle

Purpose This paper aims to focus on the development of an optical concentration sensor designed for measuring the concentration of components in solutions. Design/methodology/approach The operating principle of the developed sensor is based on the Bouguer–Lambert–Beer law. An optical measuring system using fiber optical cables was used for the practical implementation of the concentration sensor. Findings As a result of fiber optical cable use in the concentration sensor, the remote measurement principle was implemented, ensuring the instrument’s reliability and the reduction of operating costs. Originality/value The advantage of the proposed measuring system is that the sensitive element is maintenance-free, does not require power supply and can operate under severe industrial conditions. Using a fiber optic cable to transmit a light signal allows placing the sensitive element at a distance of several tens of meters from the electronics unit (the smart part).

scholarly journals Noise-Robust Image Reconstruction Based on Minimizing Extended Class of Power-Divergence Measures

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1005
Author(s):  
Ryosuke Kasai ◽  
Yusaku Yamaguchi ◽  
Takeshi Kojima ◽  
Omar M. Abou Al-Ola ◽  
Tetsuya Yoshinaga
Keyword(s):  
Image Reconstruction ◽  
Nonlinear Differential Equations ◽  
Natural Extension ◽  
Projection Data ◽  
Large Set ◽  
Reconstruction Algorithms ◽  
Divergence Measures ◽  
Leibler Divergence ◽  
Extended Class ◽  
Power Divergence

The problem of tomographic image reconstruction can be reduced to an optimization problem of finding unknown pixel values subject to minimizing the difference between the measured and forward projections. Iterative image reconstruction algorithms provide significant improvements over transform methods in computed tomography. In this paper, we present an extended class of power-divergence measures (PDMs), which includes a large set of distance and relative entropy measures, and propose an iterative reconstruction algorithm based on the extended PDM (EPDM) as an objective function for the optimization strategy. For this purpose, we introduce a system of nonlinear differential equations whose Lyapunov function is equivalent to the EPDM. Then, we derive an iterative formula by multiplicative discretization of the continuous-time system. Since the parameterized EPDM family includes the Kullback–Leibler divergence, the resulting iterative algorithm is a natural extension of the maximum-likelihood expectation-maximization (MLEM) method. We conducted image reconstruction experiments using noisy projection data and found that the proposed algorithm outperformed MLEM and could reconstruct high-quality images that were robust to measured noise by properly selecting parameters.

Incorporating known information into image reconstruction algorithms for transmission tomography

2002 ◽  
Author(s):  
Ryan Murphy ◽  
Joseph A. O'Sullivan ◽  
Jasenka Benac ◽  
Donald L. Snyder ◽  
Bruce R. Whiting ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Reconstruction Algorithms ◽  
Transmission Tomography ◽  
Image Reconstruction Algorithms

Three dimensional deblurring of transmitted-light brightfield micrographs

1993 ◽  
Vol 51 ◽  
pp. 564-565
Author(s):  
Santosh Bhattacharyya
Keyword(s):  
Image Reconstruction ◽  
Three Dimensional ◽  
Transmitted Light ◽  
Reconstruction Algorithms ◽  
3D Image Reconstruction ◽  
Structural Details ◽  
Spread Function ◽  
Early Testing ◽  
Linearized Model ◽  
Image Reconstruction Algorithms

Three dimensional microscopic structures play an important role in the understanding of various biological and physiological phenomena. Structural details of neurons, such as the density, caliber and volumes of dendrites, are important in understanding physiological and pathological functioning of nervous systems. Even so, many of the widely used stains in biology and neurophysiology are absorbing stains, such as horseradish peroxidase (HRP), and yet most of the iterative, constrained 3D optical image reconstruction research has concentrated on fluorescence microscopy. It is clear that iterative, constrained 3D image reconstruction methodologies are needed for transmitted light brightfield (TLB) imaging as well. One of the difficulties in doing so, in the past, has been in determining the point spread function of the system.We have been developing several variations of iterative, constrained image reconstruction algorithms for TLB imaging. Some of our early testing with one of them was reported previously. These algorithms are based on a linearized model of TLB imaging.

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