Computerized Tomography with Radon Transform using Microwaves and Electrostatics

Ramahi, Omar M.; Raean, Saleh Ba; Akbari-Chelaresi, Hamid; Mirjahanmardi, Seyed Hossein; Nayyeri, Vahid

doi:10.1109/rfm56185.2022.10064983

Cited by 4 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our previous works [31][32][33] , it was shown that in the near field, the resolution can dramatically exceed the Abbe diffraction limit. If the source of radiation is placed very close to the breast, the electromagnetic field that www.nature.com/scientificreports/ impinges upon the breast contains all polarizations.…”

Section: Lfem Imaging System Designmentioning

confidence: 97%

Mammography using low-frequency electromagnetic fields with deep learning

Akbari-Chelaresi

Alsaedi

Mirjahanmardi

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

In this paper, a novel technique for detecting female breast anomalous tissues is presented and validated through numerical simulations. The technique, to a high degree, resembles X-ray mammography; however, instead of using X-rays for obtaining images of the breast, low-frequency electromagnetic fields are leveraged. To capture breast impressions, a metasurface, which can be thought of as analogous to X-rays film, has been employed. To achieve deep and sufficient penetration within the breast tissues, the source of excitation is a simple narrow-band dipole antenna operating at 200 MHz. The metasurface is designed to operate at the same frequency. The detection mechanism is based on comparing the impressions obtained from the breast under examination to the reference case (healthy breasts) using machine learning techniques. Using this system, not only would it be possible to detect tumors (benign or malignant), but one can also determine the location and size of the tumors. Remarkably, deep learning models were found to achieve very high classification accuracy.

show abstract

Section: Lfem Imaging System Designmentioning

confidence: 97%