An End-to-End Deep Learning Approach for Quantitative Microwave Breast Imaging in Real-Time Applications

Overview

Journal Bioengineering (Basel)

Date 2022 Nov 10

PMID 36354562

Authors

Michele Ambrosanio

Stefano Franceschini

Vito Pascazio

Fabio Baselice

Affiliations

Soon will be listed here.

Abstract

(1) Background: In this paper, an artificial neural network approach for effective and real-time quantitative microwave breast imaging is proposed. It proposes some numerical analyses for the optimization of the network architecture and the improvement of recovery performance and processing time in the microwave breast imaging framework, which represents a fundamental preliminary step for future diagnostic applications. (2) Methods: The methodological analysis of the proposed approach is based on two main aspects: firstly, the definition and generation of a proper database adopted for the training of the neural networks and, secondly, the design and analysis of different neural network architectures. (3) Results: The methodology was tested in noisy numerical scenarios with different values of SNR showing good robustness against noise. The results seem very promising in comparison with conventional nonlinear inverse scattering approaches from a qualitative as well as a quantitative point of view. (4) Conclusion: The use of quantitative microwave imaging and neural networks can represent a valid alternative to (or completion of) modern conventional medical imaging techniques since it is cheaper, safer, fast, and quantitative, thus suitable to assist medical decisions.

Citing Articles

Training Universal Deep-Learning Networks for Electromagnetic Medical Imaging Using a Large Database of Randomized Objects.

Xue F, Guo L, Bialkowski A, Abbosh A Sensors (Basel). 2024; 24(1).

PMID: 38202870 PMC: 10780526. DOI: 10.3390/s24010008.

Microwave Breast Sensing via Deep Learning for Tumor Spatial Localization by Probability Maps.

Borghouts M, Ambrosanio M, Franceschini S, Autorino M, Pascazio V, Baselice F Bioengineering (Basel). 2023; 10(10).

PMID: 37892883 PMC: 10603986. DOI: 10.3390/bioengineering10101153.

A Deep Learning Approach for Diagnosis Support in Breast Cancer Microwave Tomography.

Franceschini S, Autorino M, Ambrosanio M, Pascazio V, Baselice F Diagnostics (Basel). 2023; 13(10).

PMID: 37238177 PMC: 10217126. DOI: 10.3390/diagnostics13101693.

Artificial Intelligence for Personalized Genetics and New Drug Development: Benefits and Cautions.

Gallo C Bioengineering (Basel). 2023; 10(5).

PMID: 37237683 PMC: 10215432. DOI: 10.3390/bioengineering10050613.

Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural Network.

Kasgari A, Safavi S, Nouri M, Hou J, Sarshar N, Ranjbarzadeh R Bioengineering (Basel). 2023; 10(4).

PMID: 37106681 PMC: 10135568. DOI: 10.3390/bioengineering10040495.

References

OLoughlin D, OHalloran M, Moloney B, Glavin M, Jones E, Elahi M . Microwave Breast Imaging: Clinical Advances and Remaining Challenges. IEEE Trans Biomed Eng. 2018; 65(11):2580-2590. DOI: 10.1109/TBME.2018.2809541. View

Aldhaeebi M, Alzoubi K, Almoneef T, Bamatraf S, Attia H, Ramahi O . Review of Microwaves Techniques for Breast Cancer Detection. Sensors (Basel). 2020; 20(8). PMC: 7219673. DOI: 10.3390/s20082390. View

Fitzmaurice C, Dicker D, Pain A, Hamavid H, Moradi-Lakeh M, MacIntyre M . The Global Burden of Cancer 2013. JAMA Oncol. 2015; 1(4):505-27. PMC: 4500822. DOI: 10.1001/jamaoncol.2015.0735. View

Ashtari A, Noghanian S, Sabouni A, Aronsson J, Thomas G, Pistorius S . Using a priori information for regularization in breast microwave image reconstruction. IEEE Trans Biomed Eng. 2010; 57(9):2197-208. DOI: 10.1109/TBME.2010.2051439. View

Semenov S, Svenson R, Bulyshev A, Souvorov A, Nazarov A, Sizov Y . Three-dimensional microwave tomography: experimental prototype of the system and vector born reconstruction method. IEEE Trans Biomed Eng. 1999; 46(8):937-46. DOI: 10.1109/10.775403. View

Aggarwal H, Mani M, Jacob M . MoDL: Model-Based Deep Learning Architecture for Inverse Problems. IEEE Trans Med Imaging. 2018; 38(2):394-405. PMC: 6760673. DOI: 10.1109/TMI.2018.2865356. View

Chandra R, Zhou H, Balasingham I, Narayanan R . On the Opportunities and Challenges in Microwave Medical Sensing and Imaging. IEEE Trans Biomed Eng. 2015; 62(7):1667-82. DOI: 10.1109/TBME.2015.2432137. View

Mojabi P, LoVetri J . Enhancement of the Krylov subspace regularization for microwave biomedical imaging. IEEE Trans Med Imaging. 2009; 28(12):2015-9. DOI: 10.1109/TMI.2009.2027703. View

Shea J, Kosmas P, Van Veen B, Hagness S . Contrast-enhanced microwave imaging of breast tumors: a computational study using 3-D realistic numerical phantoms. Inverse Probl. 2010; 26(7):74009. PMC: 2951614. DOI: 10.1088/0266-5611/26/7/074009. View

10.

Khoshdel V, Asefi M, Ashraf A, LoVetri J . Full 3D Microwave Breast Imaging Using a Deep-Learning Technique. J Imaging. 2021; 6(8). PMC: 8321110. DOI: 10.3390/jimaging6080080. View

11.

Chew W, Wang Y . Reconstruction of two-dimensional permittivity distribution using the distorted Born iterative method. IEEE Trans Med Imaging. 1990; 9(2):218-25. DOI: 10.1109/42.56334. View

12.

Burfeindt M, Colgan T, Mays R, Shea J, Behdad N, Van Veen B . MRI-Derived 3-D-Printed Breast Phantom for Microwave Breast Imaging Validation. IEEE Antennas Wirel Propag Lett. 2014; 11:1610-1613. PMC: 4133125. DOI: 10.1109/LAWP.2012.2236293. View

13.

Siegel R, Miller K, Jemal A . Cancer statistics, 2015. CA Cancer J Clin. 2015; 65(1):5-29. DOI: 10.3322/caac.21254. View

14.

Herranz M, Ruibal A . Optical imaging in breast cancer diagnosis: the next evolution. J Oncol. 2013; 2012:863747. PMC: 3529498. DOI: 10.1155/2012/863747. View

15.

Preece A, Craddock I, Shere M, Jones L, Winton H . MARIA M4: clinical evaluation of a prototype ultrawideband radar scanner for breast cancer detection. J Med Imaging (Bellingham). 2016; 3(3):033502. PMC: 4951628. DOI: 10.1117/1.JMI.3.3.033502. View

16.

Grzegorczyk T, Meaney P, Kaufman P, diFlorio-Alexander R, Paulsen K . Fast 3-d tomographic microwave imaging for breast cancer detection. IEEE Trans Med Imaging. 2012; 31(8):1584-92. PMC: 3766371. DOI: 10.1109/TMI.2012.2197218. View

17.

Ambrosanio M, Kosmas P, Pascazio V . A Multithreshold Iterative DBIM-Based Algorithm for the Imaging of Heterogeneous Breast Tissues. IEEE Trans Biomed Eng. 2018; 66(2):509-520. DOI: 10.1109/TBME.2018.2849648. View

18.

Lazebnik M, Popovic D, McCartney L, Watkins C, Lindstrom M, Harter J . A large-scale study of the ultrawideband microwave dielectric properties of normal, benign and malignant breast tissues obtained from cancer surgeries. Phys Med Biol. 2007; 52(20):6093-115. DOI: 10.1088/0031-9155/52/20/002. View

19.

Porter E, Coates M, Popovic M . An Early Clinical Study of Time-Domain Microwave Radar for Breast Health Monitoring. IEEE Trans Biomed Eng. 2015; 63(3):530-9. DOI: 10.1109/TBME.2015.2465867. View

20.

Kapur A, Carson P, Eberhard J, Goodsitt M, Thomenius K, Lokhandwalla M . Combination of digital mammography with semi-automated 3D breast ultrasound. Technol Cancer Res Treat. 2004; 3(4):325-34. PMC: 2921830. DOI: 10.1177/153303460400300402. View