Breast cancer recognition by electrical impedance tomography implemented with Gaussian relaxation-time distribution (EIT–GRTD)
The comparison between breast cancer recognition by electrical impedance tomography implemented with Gaussian relaxation time distribution (EIT-GRTD) and conventional EIT has been conducted to evaluate the optimal frequency for cancer detection fcancer. The EIT-GRTD has two steps, which are 1) the d...
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| Format: | Article |
| Language: | English |
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Sciendo
2024-08-01
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| Series: | Journal of Electrical Bioimpedance |
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| Online Access: | https://doi.org/10.2478/joeb-2024-0011 |
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| _version_ | 1832593609408380928 |
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| author | Setyawan Galih Sejati Prima Asmara Ibrahim Kiagus Aufa Takei Masahiro |
| author_facet | Setyawan Galih Sejati Prima Asmara Ibrahim Kiagus Aufa Takei Masahiro |
| author_sort | Setyawan Galih |
| collection | DOAJ |
| description | The comparison between breast cancer recognition by electrical impedance tomography implemented with Gaussian relaxation time distribution (EIT-GRTD) and conventional EIT has been conducted to evaluate the optimal frequency for cancer detection fcancer. The EIT-GRTD has two steps, which are 1) the determination of the fcancer and 2) the refinement of breast reconstruction through time-constant enhancement. This paper employs two-dimensional numerical simulations by a finite element method (FEM) software to replicate the process of breast cancer recognition. The simulation is constructed based on two distinct electrical properties, which are conductivity σ and permitivitty ε, inherent to two major breast tissues: adipose tissues, and breast cancer tissues. In this case, the σ and ε of breast cancer σcancer, εcancer are higher than adipose tissues σadipose, εadipose. The simulation results indicate that the most effective frequency for breast cancer detection based on EIT-GRTD is fcancer = 56,234 Hz. Meanwhile, conventional EIT requires more processing to determine the fcancer based on image results or spatial conductivity analysis. Quantitatively, both EIT-GRTD and conventional EIT can clearly show the position of the cancer in layers 1 and 2 for EIT-GRTD and only layer 1 for conventional EIT. |
| format | Article |
| id | doaj-art-4d9689e1ac3e4d0e9c5574d88640722c |
| institution | Kabale University |
| issn | 1891-5469 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Journal of Electrical Bioimpedance |
| spelling | doaj-art-4d9689e1ac3e4d0e9c5574d88640722c2025-01-20T11:09:56ZengSciendoJournal of Electrical Bioimpedance1891-54692024-08-011519910610.2478/joeb-2024-0011Breast cancer recognition by electrical impedance tomography implemented with Gaussian relaxation-time distribution (EIT–GRTD)Setyawan Galih0Sejati Prima Asmara1Ibrahim Kiagus Aufa2Takei Masahiro31Department of Mechanical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba, Japan1Department of Mechanical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba, Japan1Department of Mechanical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba, Japan1Department of Mechanical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba, JapanThe comparison between breast cancer recognition by electrical impedance tomography implemented with Gaussian relaxation time distribution (EIT-GRTD) and conventional EIT has been conducted to evaluate the optimal frequency for cancer detection fcancer. The EIT-GRTD has two steps, which are 1) the determination of the fcancer and 2) the refinement of breast reconstruction through time-constant enhancement. This paper employs two-dimensional numerical simulations by a finite element method (FEM) software to replicate the process of breast cancer recognition. The simulation is constructed based on two distinct electrical properties, which are conductivity σ and permitivitty ε, inherent to two major breast tissues: adipose tissues, and breast cancer tissues. In this case, the σ and ε of breast cancer σcancer, εcancer are higher than adipose tissues σadipose, εadipose. The simulation results indicate that the most effective frequency for breast cancer detection based on EIT-GRTD is fcancer = 56,234 Hz. Meanwhile, conventional EIT requires more processing to determine the fcancer based on image results or spatial conductivity analysis. Quantitatively, both EIT-GRTD and conventional EIT can clearly show the position of the cancer in layers 1 and 2 for EIT-GRTD and only layer 1 for conventional EIT.https://doi.org/10.2478/joeb-2024-0011breast cancerimpedancetomographyrecognitiongaussian relaxation-time distribution |
| spellingShingle | Setyawan Galih Sejati Prima Asmara Ibrahim Kiagus Aufa Takei Masahiro Breast cancer recognition by electrical impedance tomography implemented with Gaussian relaxation-time distribution (EIT–GRTD) Journal of Electrical Bioimpedance breast cancer impedance tomography recognition gaussian relaxation-time distribution |
| title | Breast cancer recognition by electrical impedance tomography implemented with Gaussian relaxation-time distribution (EIT–GRTD) |
| title_full | Breast cancer recognition by electrical impedance tomography implemented with Gaussian relaxation-time distribution (EIT–GRTD) |
| title_fullStr | Breast cancer recognition by electrical impedance tomography implemented with Gaussian relaxation-time distribution (EIT–GRTD) |
| title_full_unstemmed | Breast cancer recognition by electrical impedance tomography implemented with Gaussian relaxation-time distribution (EIT–GRTD) |
| title_short | Breast cancer recognition by electrical impedance tomography implemented with Gaussian relaxation-time distribution (EIT–GRTD) |
| title_sort | breast cancer recognition by electrical impedance tomography implemented with gaussian relaxation time distribution eit grtd |
| topic | breast cancer impedance tomography recognition gaussian relaxation-time distribution |
| url | https://doi.org/10.2478/joeb-2024-0011 |
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