scholarly journals An Optimization-Based Approach to Radar Image Reconstruction in Breast Microwave Sensing

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8172
Author(s):  
Tyson Reimer ◽  
Stephen Pistorius
Keyword(s):  
Image Reconstruction ◽  
Sensitivity And Specificity ◽  
Hot Spot ◽  
Tumour Response ◽  
Reconstruction Algorithm ◽  
Relative Increase ◽  
Radar Image ◽  
Reconstruction Process ◽  
Lower Specificity ◽  
Spot Image

Breast microwave sensing (BMS) has been studied as a potential technique for cancer detection due to the observed microwave properties of malignant and healthy breast tissues. This work presents a novel radar-based image reconstruction algorithm for use in BMS that reframes the radar image reconstruction process as an optimization problem. A gradient descent optimizer was used to create an optimization-based radar reconstruction (ORR) algorithm. Two hundred scans of MRI-derived breast phantoms were performed with a preclinical BMS system. These scans were reconstructed using the ORR, delay-and-sum (DAS), and delay-multiply-and-sum (DMAS) beamformers. The ORR was observed to improve both sensitivity and specificity compared to DAS and DMAS. The estimated sensitivity and specificity of the DAS beamformer were 19% and 44%, respectively, while for ORR, they were 27% and 56%, representing a relative increase of 42% and 27%. The DAS reconstructions also exhibited a hot-spot image artifact, where a localized region of high intensity that did not correspond to any physical phantom feature would be present in an image. This artifact appeared like a tumour response within the image and contributed to the lower specificity of the DAS beamformer. This artifact was not observed in the ORR reconstructions. This work demonstrates the potential of an optimization-based conceptualization of the radar image reconstruction problem in BMS. The ORR algorithm implemented in this work showed improved diagnostic performance and fewer image artifacts compared to the widely employed DAS algorithm.

The Design of Compressed Sensing Image Reconstruction Algorithm Based on Structure Priori Model

2014 ◽  
Vol 602-605 ◽  
pp. 3311-3315
Author(s):  
Lin Zhang ◽  
Xia Ling Zeng
Keyword(s):  
Image Processing ◽  
Image Reconstruction ◽  
Compressed Sensing ◽  
Sampling Rate ◽  
Reconstruction Algorithm ◽  
Optimization Process ◽  
Image Block ◽  
Reconstruction Process ◽  
Prior Model ◽  
Dimensional Optimization

Compressed sensing is referred to as the CS technology; it can realize image compression and reconstruction process in low sample rate. It has great potential to reduce the sampling rate and improve the quality of image processing. In this paper, we introduce the structure prior model into the compressed sensing and image processing, and make the image reconstruction of high dimensional optimization process simplified into a series of low dimensional optimization process, which improves the processing speed and image quality. In order to verify the effectiveness and reliability of the proposed algorithm, this paper uses combined control form of C language and MATLAB software to design the programming of structure prior model, and use the Simulink environment to debug the program. Through the calculation we get the image block and the reconstruction result. It provides the technical reference for the research on image compressed sensing technology.

scholarly journals A New Approach to Image Reconstruction from Projections Using a Recurrent Neural Network

2008 ◽  
Vol 18 (2) ◽  
pp. 147-157 ◽  
Author(s):  
Robert Cierniak
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Recurrent Neural Network ◽  
Reconstruction Algorithm ◽  
Neural Network Approach ◽  
Reconstruction Process ◽  
New Approach ◽  
The Neural Network ◽  
Image Reconstruction From Projections ◽  
Parallel Geometry

A New Approach to Image Reconstruction from Projections Using a Recurrent Neural NetworkA new neural network approach to image reconstruction from projections considering the parallel geometry of the scanner is presented. To solve this key problem in computed tomography, a special recurrent neural network is proposed. The reconstruction process is performed during the minimization of the energy function in this network. The performed computer simulations show that the neural network reconstruction algorithm designed to work in this way outperforms conventional methods in the obtained image quality.

MICROWAVE IMAGING OF EMBEDDED OBJECT USING AN ENHANCED IMAGE RECONSTRUCTION ALGORITHM

2015 ◽  
Vol 74 (20) ◽  
pp. 1793-1801
Author(s):  
Sidi Mohammed Chouiti ◽  
Lotfi Merad ◽  
Sidi Mohammed Meriah ◽  
Xavier Raimundo
Keyword(s):  
Image Reconstruction ◽  
Reconstruction Algorithm ◽  
Microwave Imaging ◽  
Image Reconstruction Algorithm

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.

Research on the application of 3D reconstruction technology in traditional handicraft design

2021 ◽  
pp. 002072092110052
Author(s):  
GuoLong Zhang
Keyword(s):  
Poisson Equation ◽  
Optimization Algorithm ◽  
Three Dimensional ◽  
Reconstruction Algorithm ◽  
Social Production ◽  
Design Constraint ◽  
Reconstruction Process ◽  
Key Steps ◽  
Shielding Design ◽  
Reconstruction Model

The use of computer technology for three-dimensional (3 D) reconstruction is one of the important development directions of social production. The purpose is to find a new method that can be used in traditional handicraft design, and to explore the application of 3 D reconstruction technology in it. Based on the description and analysis of 3 D reconstruction technology, the 3 D reconstruction algorithm based on Poisson equation is analyzed, and the key steps and problems of the method are clarified. Then, by introducing the shielding design constraint, a 3 D reconstruction algorithm based on shielded Poisson equation is proposed. Finally, the performance of two algorithms is compared by reconstructing the 3 D image of rabbit. The results show that: when the depth value of the algorithm is 11, the surface of the rabbit image obtained by the proposed optimization algorithm is smoother, and the details are more delicate and fluent; under different depth values, with the increase of the depth value, the number of vertices and faces of the two algorithms increase, and the optimal depth values of 3 D reconstruction are more than 8. However, the proposed optimization algorithm has more vertices, and performs better in the reconstruction process; the larger the depth value is, the more time and memory are consumed in 3 D reconstruction, so it is necessary to select the appropriate depth value; the shielding parameters of the algorithm have a great impact on the fineness of the reconstruction model. The larger the parameter is, the higher the fineness is. In a word, the proposed 3 D reconstruction algorithm based on shielded Poisson equation has better practicability and superiority.

scholarly journals Constrained Backtracking Matching Pursuit Algorithm for Image Reconstruction in Compressed Sensing

2021 ◽  
Vol 11 (4) ◽  
pp. 1435
Author(s):  
Xue Bi ◽  
Lu Leng ◽  
Cheonshik Kim ◽  
Xinwen Liu ◽  
Yajun Du ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Matching Pursuit ◽  
Reconstruction Algorithm ◽  
Energy Criterion ◽  
Step Size ◽  
Relationship Analysis ◽  
Support Set ◽  
Reconstruction Performance ◽  
Sparsity Level

Image reconstruction based on sparse constraints is an important research topic in compressed sensing. Sparsity adaptive matching pursuit (SAMP) is a greedy pursuit reconstruction algorithm, which reconstructs signals without prior information of the sparsity level and potentially presents better reconstruction performance than other greedy pursuit algorithms. However, SAMP still suffers from being sensitive to the step size selection at high sub-sampling ratios. To solve this problem, this paper proposes a constrained backtracking matching pursuit (CBMP) algorithm for image reconstruction. The composite strategy, including two kinds of constraints, effectively controls the increment of the estimated sparsity level at different stages and accurately estimates the true support set of images. Based on the relationship analysis between the signal and measurement, an energy criterion is also proposed as a constraint. At the same time, the four-to-one rule is improved as an extra constraint. Comprehensive experimental results demonstrate that the proposed CBMP yields better performance and further stability than other greedy pursuit algorithms for image reconstruction.

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