Full 3D Microwave Breast Imaging Using a Deep-Learning Technique
Abstract
:1. Introduction
2. 3D CSI-Deep-Learning Methodology
2.1. Microwave Imaging via Contrast Source Inversion
2.2. Machine Learning Approach to Reconstruction
3. Numerical Experiments
3.1. Datasets
3.2. Network Training
3.3. Quantitative Assessment
3.4. Assessment of Robustness
3.4.1. Robustness to Changes in Frequency
3.4.2. Robustness to Changes in Breast Phantom Geometry
3.4.3. Robustness to Imperfections in Prior Information
3.4.4. Robustness to Breast Phantom with No Tumor
4. Experimental Tests and Results
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Permittivity | |||
---|---|---|---|
Air | Fat | Fibroglandular | Tumor |
1 − 0.001j | 3 − 0.6j | 20 − 21.6j | 56.3 − 30j |
RMS Error | AUC | |||
---|---|---|---|---|
CSI | CNN | CSI | CNN | |
Synthetic Data | 1.4356 | 1.161 | 0.935 | 0.957 |
Exprimental Data | 1.250 | 1.172 | 0.794 | 0.938 |
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Khoshdel, V.; Asefi, M.; Ashraf, A.; LoVetri, J. Full 3D Microwave Breast Imaging Using a Deep-Learning Technique. J. Imaging 2020, 6, 80. https://doi.org/10.3390/jimaging6080080
Khoshdel V, Asefi M, Ashraf A, LoVetri J. Full 3D Microwave Breast Imaging Using a Deep-Learning Technique. Journal of Imaging. 2020; 6(8):80. https://doi.org/10.3390/jimaging6080080
Chicago/Turabian StyleKhoshdel, Vahab, Mohammad Asefi, Ahmed Ashraf, and Joe LoVetri. 2020. "Full 3D Microwave Breast Imaging Using a Deep-Learning Technique" Journal of Imaging 6, no. 8: 80. https://doi.org/10.3390/jimaging6080080