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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