1 / 17

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

ania
Download Presentation

The Algorithm of Image Reconstruction in EIT

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related