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The Algorithm of Image Reconstruction in EIT

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

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The Algorithm of Image Reconstruction in EIT

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  1. 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

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

  3. Introduction • Electrical impedance tomography (EIT) EIT：電阻抗斷層造影

  4. Introduction • Comparison of Imaging Techniques • 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˘gaG¨ursoy*, Member, IEEE, YasinMamatjan, 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

  6. Paper review(1) • Electrode configuration

  7. Paper review(1)

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

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

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

  11. Methods & Materials • FEM for EIT forward problem Galerkin method Φ：voltage V：basis vector spaceσ：conductivity FEM：有限元素法 EIT：電阻抗斷層造影Galerkin method：伽遼金方法

  12. Methods & Materials • Radial 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

  13. Methods & Materials • Block diagram

  14. Result • Verification

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

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

  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˘gaG¨ursoy*, Member, IEEE, YasinMamatjan, 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

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