A novel approach for EIT regularization via spatial and spectral principal component analysis
| Autor: | Mark-John Bruwer, John F. MacGregor, Gerald R. Moran, Aravinthan Jegatheesan, Mehran Goharian |
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| Rok vydání: | 2007 |
| Předmět: |
Principal Component Analysis
Physiology Mathematical analysis Biomedical Engineering Biophysics Reproducibility of Results Inverse problem Regularization (mathematics) Models Biological Sensitivity and Specificity Finite element method Nonlinear system Physiology (medical) Principal component analysis Image Interpretation Computer-Assisted Electric Impedance Computer Simulation Plethysmography Impedance Algorithm Electrical impedance tomography Tomography Subspace topology Algorithms Voltage Mathematics |
| Zdroj: | Physiological measurement. 28(9) |
| ISSN: | 0967-3334 |
| Popis: | Electrical impedance tomography, EIT, is an imaging modality in which the internal conductivity distribution of an object is reconstructed based on voltage measurements on the boundary. This reconstruction problem is a nonlinear and ill-posed inverse problem, which requires regularization to ensure a stable solution. Most popular regularization approaches enforce smoothness in the inverse solution. In this paper, we propose a novel approach to build a subspace for regularization using a spectral and spatial multi-frequency analysis approach. The approach is based on the construction of a subspace for the expected conductivity distributions using principal component analysis. It is shown via simulations that the reconstructed images obtained with the proposed method are better than with the standard regularization approach. Using this approach, the percentage of misclassified finite elements was reduced up to twelve fold from the initial percentages after five iterations. The advantage of this technique is that prior information is extracted from the characteristic response of an object at different frequencies and spatially across the finite elements. |
| Databáze: | OpenAIRE |
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