the algorithm of image reconstruction in eit
DESCRIPTION
The Algorithm of Image Reconstruction in EIT. Presenter: Yang-Min Huang Adviser: Dr. Ji-Jer Huang Chairman: Hung-Chi Yang 2013/4/10. Electrical Impedance Tomography : 電阻抗斷層造影. Outline. Introduction Paper review Motivations & Purposes Methods & Materials Result Future Works - PowerPoint PPT PresentationTRANSCRIPT
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The Algorithm of Image Reconstruction in EIT
Presenter: Yang-Min Huang Adviser: Dr. Ji-Jer HuangChairman: Hung-Chi Yang
2013/4/10
Electrical Impedance Tomography :電阻抗斷層造影
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OutlineIntroduction
Paper review
Motivations & Purposes
Methods & Materials
Result
Future Works
References
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IntroductionElectrical impedance tomography (EIT)
EIT:電阻抗斷層造影
•Injection current sources
•Measurement voltages
•Image reconstruction
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IntroductionComparison of Imaging Techniques
ImagingTechnique
Imaging Cost($) Resolution(%)
Advantages Disadvantages
MRIStructuralFunctional Highest <0.1 Soft-tissue, High resolution Expensive, Magnetic field
limit
X-ray CT Structural High <1 High resolution, Fast
Radiation, Difficult to distinguish the soft-tissue
PET Functional Middle >3 Ration show the organs physiological function
Low resolution, Radiation
Ultrasound StructuralFunctional Low 1 Non-invasive, Fast Low resolution, High noise,
Bone reflect
EIT Functional Lowest 1 Non-invasive, No radiation,
Portable Low resolution
MRI:核磁共振造影 PET :正子放射造影 EIT :電阻抗斷層造影X-ray CT : X 光電腦斷層 Ultrasound :超音波
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Paper review(1) From:Do˘ga G¨ursoy*, Member, IEEE, Yasin Mamatjan, Andy Adler, and Hermann
Scharfetter” Enhancing Impedance Imaging Through Multimodal Tomography” IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 58, NO. 11, NOVEMBER 2011
Purpose To investigate how much additional performance improvements can be expected by combining datasets of different modalities.
EIT:電阻抗斷層造影 MIT :磁感應斷層造影 ICEIT :誘導電流電阻抗斷層造影
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Paper review(1)Electrode configuration
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Paper review(1)
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Motivations & PurposesTo get the real image for using FEM and
Neural Network.
To complete the algorithm for using Matlab.
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Methods & MaterialsPoisson equation
Algorithm The forward problem The inverse problem
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Methods & MaterialsPoisson equation
σ:導電係數 Ĵ : 電流密度n :物體表面的法向量
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Methods & MaterialsFEM for EIT forward problem Galerkin method
FEM:有限元素法EIT :電阻抗斷層造影Galerkin method :伽遼金方法
Φ: voltageV: basis vector spaceσ: conductivity
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Methods & MaterialsRadial Basis Function(RBF) neural network
RBF neural network :輻狀基底函數類神經網路σ :變異數SN :樣本總數
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Methods & MaterialsBlock diagram
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ResultVerification
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ResultMeasured voltage for using different current,
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Future WorksPaper review
To simulate more samples of image pattern
To improve the RBF neural network
To complete the user interface
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References P. Wang, H. Li, L. Xie, Y. Sun, “The Implementation of FEM and RBF Neural Network in
EIT”, Proceedings of the 2009 Second International Conference on Intelligent Networks and Intelligent Systems, pp. 66-69, IEEE Computer Society, 2009.
Do˘ga G¨ursoy*, Member, IEEE, Yasin Mamatjan, Andy Adler, and Hermann Scharfetter” Enhancing Impedance Imaging Through Multimodal Tomography” IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 58, NO. 11, NOVEMBER 2011
Ybarra, G. A., Q. H. Liu, G. Ye, K. H. Lim, R. George, and W. T. Joines, "Breast imaging using electrical impedance tomography (EIT)," Emerging Technologies in Breast Imaging and Mammography, Ed.: J. Suri, R. M. Rangayyan, and S. Laxminarayan, American Scientific Publishers, 2008.
黃俊惟,電阻抗斷層成像技術之研究,南台科技大學電機工程研究所碩士論文, 2010