Back-propagation neural network-based reconstruction algorithm for diffuse optical tomography

Feng, Jinchao; Sun, Qiuwan; Li, Zhe; Sun, Zhonghua; Jia, Kebin

doi:10.1117/1.jbo.24.5.051407

Cited by 60 publications

(55 citation statements)

References 50 publications

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“…(7). The threshold is set by the user in an empirical manner [36], and will produce a more accurate image the lower the threshold, named UDT. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{equation*} UDT < E^{q,n}\left ({l,m,n }\right)\tag{7}\end{equation*} \end{document} where < means inequity where UDT should keep lowest.…”

Section: Proposed Method: Elastographic Tomosynthesismentioning

confidence: 99%

Elastographic Tomosynthesis From X-Ray Strain Imaging of Breast Cancer

Sutphin

Olson

Motai

et al. 2019

IEEE J. Transl. Eng. Health Med.

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Noncancerous breast tissue and cancerous breast tissue have different elastic properties. In particular, cancerous breast tumors are stiff when compared to the noncancerous surrounding tissue. This difference in elasticity can be used as a means for detection through the method of elastographic tomosynthesis by means of physical modulation. This paper deals with a method to visualize elasticity of soft tissues, particularly breast tissues, via x-ray tomosynthesis. X-ray tomosynthesis is now used to visualize breast tissues with better resolution than the conventional single-shot mammography. The advantage of X-ray tomosynthesis over X-ray CT is that fewer projections are needed than CT to perform the reconstruction, thus radiation exposure and cost are both reduced. Two phantoms were used for the testing of this method, a physical phantom and an in silico phantom. The standard root mean square error in the tomosynthesis for the physical phantom was 2.093 and the error in the in silico phantom was negligible. The elastographs were created through the use of displacement and strain graphing. A Gaussian Mixture Model with an expectation–maximization clustering algorithm was applied in three dimensions with an error of 16.667%. The results of this paper have been substantial when using phantom data. There are no equivalent comparisons yet in 3D x-ray elastographic tomosynthesis. Tomosynthesis with and without physical modulation in the 3D elastograph can identify feature groupings used for biopsy. The studies have potential to be applied to human test data used as a guide for biopsy to improve accuracy of diagnosis results. Further research on this topic could prove to yield new techniques for human patient diagnosis purposes.

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Section: Proposed Method: Elastographic Tomosynthesismentioning

confidence: 99%

Elastographic Tomosynthesis From X-Ray Strain Imaging of Breast Cancer

Sutphin

Olson

Motai

et al. 2019

IEEE J. Transl. Eng. Health Med.

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“…The obstacle avoidance function of the fire robot mainly relies on the distance sensor to transmit the position information of the robot. At present, there are various distance sensors on the market, such as ultrasound-based, infrared-based, and laser-based distance sensors [14]. It is very difficult to find suitable sensors for obstacle avoidance of firefighting robots.…”

Section: Obstacle Avoidance Module Of the Fire Robotmentioning

confidence: 99%

Multi-sensor information fusion detection system for fire robot through back propagation neural network

Zhang

2020

PLoS ONE

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Objective To reduce the danger for firefighters and ensure the safety of firefighters as much as possible, based on the back propagation neural network (BPNN) the fire sensor multi-sensor information fusion detection system is investigated. Method According to previous studies, the information sources and information processing methods for the design of this study are first explained. Then, the basic structure and flowchart of the research object in this study are designed. Based on the structure diagram and flowchart, the BPNN is selected to fuse the feature layers in this study, and the fuzzy control is selected to fuse the decision layers in this study. The multi-sensor information fusion detection system collects information for the sensors first, processes the collected information, and sends it to the processor of the robot. The processor analyzes and processes the received signal, and transmits the obtained information to the control terminal through the wireless communication system. Results Through the tests in this study, it is found that when the number of hidden layer nodes of the BPNN is 7, the optimal training result is obtained. On this basis, the test of BPNN in this study is performed. The test results show that after 127 iterations, the error of the BPNN reaches the lowest target value, indicating that the BPNN achieves an excellent level of accuracy. The trained BPNN has a running time of 0.0276 s and a mean square error of 0.0013. The smaller the mean square error value is, the higher the accuracy of the BPNN is, which shows that the BPNN meets the high precision requirements of this study. Conclusion The research on the multi-sensor information fusion detection system of fire robots in this study can provide theoretical support for the research on forest fire detection in China. Since the proposed BPNN-based robot is applied to the inspection and processing of forest

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“…In 2018, Zheng et al [ 26 , 27 ] developed an auto-encoder method to achieve complicated reconstruction in electrical capacitance tomography. Feng et al [ 28 ] investigated the feasibility of a back-propagation neural network (BPNN) to reestablish the distribution of optical properties in a diffuse optical tomography (DOT) problem. Their method of evaluating resolution is also used for reference by other scholars.…”

Section: Introductionmentioning

confidence: 99%

Application of a Generative Adversarial Network in Image Reconstruction of Magnetic Induction Tomography

Yang

Liu

Wang

et al. 2021

Sensors

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Image reconstruction of Magnetic induction tomography (MIT) is an ill-posed problem. The non-linear characteristics lead many difficulties to its solution. In this paper, a method based on a Generative Adversarial Network (GAN) is presented to tackle these barriers. Firstly, the principle of MIT is analyzed. Then the process for finding the global optimum of conductivity distribution is described as a training process, and the GAN model is proposed. Finally, the image was reconstructed by a part of the model (the generator). All datasets are obtained from an eight-channel MIT model by COMSOL Multiphysics software. The voltage measurement samples are used as input to the trained network, and its output is an estimate for image reconstruction of the internal conductivity distribution. The results based on the proposed model and the traditional algorithms were compared, which have shown that average root mean squared error of reconstruction results obtained by the proposed method is 0.090, and the average correlation coefficient with original images is 0.940, better than corresponding indicators of BPNN and Tikhonov regularization algorithms. Accordingly, the GAN algorithm was able to fit the non-linear relationship between input and output, and visual images also show that it solved the usual problems of artifact in traditional algorithm and hot pixels in L2 regularization, which is of great significance for other ill-posed or non-linear problems.

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Back-propagation neural network-based reconstruction algorithm for diffuse optical tomography

Cited by 60 publications

References 50 publications

Elastographic Tomosynthesis From X-Ray Strain Imaging of Breast Cancer

Elastographic Tomosynthesis From X-Ray Strain Imaging of Breast Cancer

Multi-sensor information fusion detection system for fire robot through back propagation neural network

Application of a Generative Adversarial Network in Image Reconstruction of Magnetic Induction Tomography

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