期刊
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 69, 期 4, 页码 1512-1523出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2021.3122113
关键词
Clustering; distorted Born iterative method; electromagnetic brain imaging; microwave tomography
资金
- Department of Innovation and Tourism Industry Development of Queensland, Australia, under the Advance Queensland Industry Research Fellowships Program
In this study, a modified distorted Born iterative method (DBIM) is proposed for stroke detection and classification in brain imaging. By clustering the reconstructed electrical properties, the proposed method improves the accuracy and stability of the reconstruction, outperforming conventional methods.
A modified distorted Born iterative method (DBIM), which includes clustering of reconstructed electrical properties (EPs) after certain iterations, is presented for brain imaging aiming at stroke detection and classification. For this approach to work, a rough estimation of number of different materials (or bio-tissues) in the imaged domain and their corresponding rough dielectric properties (permittivity and conductivity) are needed as a prior information. The proposed adaptive clustering DBIM (AC-DBIM) is compared with three conventional methods (DBIM, multiplicative regularized contrast source inversion (MR-CSI), and CSI for shape and location reconstruction (SL-CSI)) in two-dimensional scenario on a head phantom and numerical head model with different strokes. Three-dimensional simulations are also conducted to indicate the suitability of AC-DBIM in real-life brain imaging. Lastly, the proposed algorithm is assessed using a clinical electromagnetic head scanner developed on phantoms. The simulation and experimental results show superiority of AC-DBIM compared to conventional methods. AC-DBIM achieves significant improvement in the size and shape reconstruction and reduction in errors and standard deviation of the reconstructed epsilon(r). and sigma at clinical scenarios compared with conventional DBIM.
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