1. Presenter: Yang-Min Huang Adviser: Dr. Ji-Jer Huang Chairman: Hung-Chi Yang 2013/4/10 1 Electrical Impedance Tomography ：電阻抗斷層造影

2. Outline Introduction Paper review Motivations & Purposes Methods & Materials Result Future Works References 2

3. Introduction Electrical impedance tomography (EIT) 3 EIT ：電阻抗斷層造影 Injection current sourcesMeasurement voltagesImage reconstruction

4. Introduction Comparison of Imaging Techniques 4 Imaging Technique ImagingCost($)Resolution(%)AdvantagesDisadvantages MRI Structural Functional Highest <0.1 Soft-tissue, High resolution Expensive, Magnetic field limit X-ray CTStructural High <1 High resolution, Fast Radiation, Difficult to distinguish the soft-tissue PETFunctional Middle >3 Ration show the organs physiological function Low resolution, Radiation Ultrasound Structural Functional Low 1 Non-invasive, Fast Low resolution, High noise, Bone reflect EITFunctional 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

5. 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. 5 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

6. Paper review(1) Electrode configuration 6

7. Paper review(1) 7

8. Motivations & Purposes To get the real image for using FEM and Neural Network. To complete the algorithm for using Matlab. 8

9. Methods & Materials Poisson equation Algorithm The forward problem The inverse problem 9

10. Methods & Materials Poisson equation σ ：導電係數 Ĵ : 電流密度 n ：物體表面的法向量 10

11. Methods & Materials FEM 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 ： voltage V ： basis vector space σ ： conductivity 11

12. Methods & Materials Radial Basis Function(RBF) neural network 12 RBF neural network ：輻狀基底函數類神經網路 σ ：變異數 SN ：樣本總數

13. Methods & Materials Block diagram 13

14. Result Verification 14

15. Result Measured voltage for using different current, 15 train data 15

16. Future Works Paper review To simulate more samples of image pattern To improve the RBF neural network To complete the user interface 16

17. 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 17