volume | PIER Journals

Abstract

Breast cancer is considered one of the major cancers among women. Early identification of breast cancer is essential for improving treatment outcomes, necessitating the application of accurate, non-invasive imaging methods. This paper presents a comparative evaluation of Electrical Impedance Tomography (EIT) and Ultrasound Tomography (UST) for breast tumour diagnosis, employing a simulated multilayer breast model. The forward problem, which entails the determination of electrical conductivity and acoustic pressure distribution, was addressed through finite element analysis utilizing COMSOL Multiphysics software. The inverse problem of EIT was solved using Total Variation regularization with Primal-Dual Interior Point Method (TV-PDIPM) and that of ultrasound by employing attenuation-weighted bilinear interpolation to effectively resolve propagation losses through tissue layers, subsequently leading to segmentation. The images reconstructed and segmented from both modalities were subjected to quantitative evaluation employing metrics such as accuracy, Dice coefficient, sensitivity, specificity, and correlation coefficient (CC). The findings indicate that both approaches offers complimentary information regarding tumor, with each approach presenting distinct advantages based on tissue characteristics and image clarity.

1. World Health Organization, ``Breast Cancer key facts", https://www.who.int/news-room/fact-sheets/detail/breast-cancer, Mar. 2024.

2. Zeeshan, Muhammad, Basit Salam, Qazi Saad B. Khalid, Shahbaz Alam, and Raza Sayani, "Diagnostic accuracy of digital mammography in the detection of breast cancer," Cureus, Vol. 10, No. 4, e2448, 2018. Google Scholar

3. Annapoorani, G. and D. Vaishali, "Design and analysis of forward problem of electrical impedance tomography for breast cancer screening," AIP Conference Proceedings, Vol. 3159, No. 1, 020019, 2025.

4. Akhtari-Zavare, Mehrnoosh and Latiffah A. Latiff, "Electrical impedance tomography as a primary screening technique for breast cancer detection," Asian Pacific Journal of Cancer Prevention, Vol. 16, No. 14, 5595-5597, 2015. Google Scholar

5. Al Ahmad, Mahmoud, Zeina Al Natour, Farah Mustafa, and Tahir A. Rizvi, "Electrical characterization of normal and cancer cells," IEEE Access, Vol. 6, 25979-25986, 2018. Google Scholar

6. Wu, Jian, Pin Wang, Yan Tang, Hong Liu, Haobin Wang, Wenjie Zhang, Yan Zhang, Liping Chen, Zhangbo Xu, and Xinmin Yao, "A new method to rapidly identify benign and malignant breast lumps through bioelectrical impedance spectroscopy," Medical Physics, Vol. 46, No. 5, 2522-2525, 2019. Google Scholar

7. Ye, Gang, Kim Hwa Lim, Rhett T. George Jr., Gary A. Ybarra, William T. Joines, and Qing Huo Liu, "3D EIT for breast cancer imaging: System, measurements, and reconstruction," Microwave and Optical Technology Letters, Vol. 50, No. 12, 3261-3271, 2008. Google Scholar

8. Hong, Sunjoo, Kwonjoon Lee, Unsoo Ha, Hyunki Kim, Yongsu Lee, Youchang Kim, and Hoi-Jun Yoo, "A 4.9 mΩ-sensitivity mobile electrical impedance tomography IC for early breast-cancer detection system," IEEE Journal of Solid-State Circuits, Vol. 50, No. 1, 245-257, 2014. Google Scholar

9. Lee, Jaehyuk, Surin Gweon, Kwonjoon Lee, Soyeon Um, Kyoung-Rog Lee, and Hoi-Jun Yoo, "A 9.6-mW/Ch 10-MHz wide-bandwidth electrical impedance tomography IC with accurate phase compensation for early breast cancer detection," IEEE Journal of Solid-State Circuits, Vol. 56, No. 3, 887-898, 2020. Google Scholar

10. Murphy, Ethan K., Aditya Mahara, and Ryan J. Halter, "Absolute reconstructions using rotational electrical impedance tomography for breast cancer imaging," IEEE Transactions on Medical Imaging, Vol. 36, No. 4, 892-903, Apr. 2017. Google Scholar

11. Yang, Lu, Hongtao Wu, Kai Liu, Bai Chen, Shan Huang, and Jiafeng Yao, "A multicircle planar electrical impedance tomography sensor for 3-D miniature imaging," IEEE Sensors Journal, Vol. 23, No. 9, 9697-9706, May 2023. Google Scholar

12. Yin, Xipeng, Yunjie Yang, Jiabin Jia, and Chao Tan, "3D image reconstruction on a miniature planar EIT sensor using sparsity with median filter," 2017 IEEE SENSORS, 1-3, Glasgow, UK, Oct. 2017.

13. He, Jie, Zhiyang Hong, Xiaowu Sun, Qi Deng, Mengyuan Zhu, Chengjun Zhu, Kai Liu, Bo Sun, and Jiafeng Yao, "Three-dimensional image reconstruction of breast tumor by electrical impedance tomography based on dimensional grey wolf optimization algorithm," IEEE Transactions on Instrumentation and Measurement, Vol. 74, 4504310, 2025. Google Scholar

14. Duric, Nebojsa, Peter Littrup, Lou Poulo, Alex Babkin, Roman Pevzner, Earle Holsapple, Olsi Rama, and Carri Glide, "Detection of breast cancer with ultrasound tomography: First results with the Computed Ultrasound Risk Evaluation (CURE) prototype," Medical Physics, Vol. 34, No. 2, 773-785, 2007. Google Scholar

15. Chen, Hai-long, Jiao-qun Zhou, Qiang Chen, and Yong-chuan Deng, "Comparison of the sensitivity of mammography, ultrasound, magnetic resonance imaging and combinations of these imaging modalities for the detection of small (≤2 cm) breast cancer," Medicine, Vol. 100, No. 26, e26531, Jul. 2021. Google Scholar

16. Mercado, Karla Patricia E., Developing High-Frequency Quantitative Ultrasound Techniques to Characterize Three-Dimensional Engineered Tissues, University of Rochester, Rochester, NY, USA, 2015.

17. Pusppanathan, Jaysuman, Ruzairi Abdul Rahim, Fatin Aliah Phang, Elmy Johana Mohamad, Nor Muzakkir Nor Ayob, Mohd Hafiz Fazalul Rahiman, and Chan Kok Seong, "Single-plane dual-modality tomography for multiphase flow imaging by integrating electrical capacitance and ultrasonic sensors," IEEE Sensors Journal, Vol. 17, No. 19, 6368-6377, 2017. Google Scholar

18. Tadesse, Getu Ferenji, Eyachew Misganew Tegaw, and Ejigu Kebede Abdisa, "Diagnostic performance of mammography and ultrasound in breast cancer: A systematic review and meta-analysis," Journal of Ultrasound, Vol. 26, No. 2, 355-367, 2023. Google Scholar

19. Somersalo, Erkki, Margaret Cheney, and David Isaacson, "Existence and uniqueness for electrode models for electric current computed tomography," SIAM Journal on Applied Mathematics, Vol. 52, No. 4, 1023-1040, 1992. Google Scholar

20. Halter, Ryan J., Tian Zhou, Paul M. Meaney, Alex Hartov, Richard J. Barth, Kari M. Rosenkranz, Wendy A. Wells, Christine A. Kogel, Andrea Borsic, Elizabeth J. Rizzo, and Keith D. Paulsen, "The correlation of in vivo and ex vivo tissue dielectric properties to validate electromagnetic breast imaging: Initial clinical experience," Physiological Measurement, Vol. 30, No. 6, S121, 2009. Google Scholar

21. Fernández-Aranzamendi, Elizabeth G., Patricia R. Castillo-Araníbar, Ebert G. San Román Castillo, Belén S. Oller, Luz Ventura-Zaa, Gelber Eguiluz-Rodriguez, Vicente González-Posadas, and Daniel Segovia-Vargas, "Dielectric characterization of ex-vivo breast tissues: Differentiation of tumor types through permittivity measurements," Cancers, Vol. 16, No. 4, 793, 2024. Google Scholar

22. Davidson, J. L., P. Wright, S. T. Ahsan, R. L. Robinson, C. J. D. Pomfrett, and H. McCann, "fEITER --- A new EIT instrument for functional brain imaging," Journal of Physics: Conference Series, Vol. 224, No. 1, 012025, 2010.

23. Li, Feng, Yushan Liu, Zhimin Qiao, and Yongwei Li, "Convolutional attention network for electrical/ultrasound dual-modal fusion 2-D image reconstruction," IEEE Transactions on Instrumentation and Measurement, Vol. 73, 4503013, 2024. Google Scholar

24. Annapoorani, G., R. Vani, and D. Vaishali, "Multi-modal finite element modelling for enhanced breast cancer detection using electrical impedance tomography and ultrasound tomography," 2023 Second International Conference on Advances in Computational Intelligence and Communication (ICACIC), 1-6, Puducherry, India, Dec. 2023.

25. Wu, Yimin, Yandan Jiang, Haifeng Ji, Baoliang Wang, and Zhiyao Huang, "A joint image reconstruction method for capacitively coupled electrical impedance tomography," IEEE Transactions on Instrumentation and Measurement, Vol. 73, 1-13, 2023. Google Scholar

26. Ganesan, Annapoorani and Vaishali Durgamahanthi, "Non-invasive breast cancer detection using electrical impedance tomography: Design, analysis and comparison of reconstruction algorithms," Traitement du Signal, Vol. 40, No. 6, 2809-2817, 2023. Google Scholar

27. Sakai, K., P. N. Darma, P. A. Sejati, R. Wicaksono, H. Hayashi, and M. Takei, "Gastric functional monitoring by gastric electrical impedance tomography (gEIT) suit with dual-step fuzzy clustering," Scientific Reports, Vol. 13, No. 1, 514, 2023. Google Scholar

Mrs. Annapoorani Ganesan

Prof. Vani Rajamanickam

Dr. Vaishali Durgamahant