Optimization-Based Confocal Microwave Imaging in Medical Applications

2015 ◽  
Vol 63 (8) ◽  
pp. 3531-3539 ◽  
Author(s):  
Lei Guo ◽  
Amin M. Abbosh
Keyword(s):  
Microwave Imaging ◽  
Medical Applications ◽  
Confocal Microwave Imaging

scholarly journals Wavelet-Based Regularization for Robust Microwave Imaging in Medical Applications

2015 ◽  
Vol 62 (4) ◽  
pp. 1195-1202 ◽  
Author(s):  
Rosa Scapaticci ◽  
Panagiotis Kosmas ◽  
Lorenzo Crocco
Keyword(s):  
Microwave Imaging ◽  
Medical Applications

A Fern Antipodal Vivaldi Antenna for Near-Field Microwave Imaging Medical Applications

2021 ◽  
pp. 1-1
Author(s):  
Alexandre M. De Oliveira ◽  
Antonio M. De Oliveira ◽  
Marcelo B. Perotoni ◽  
N. Nurhayati ◽  
Henri Baudrand ◽  
...  
Keyword(s):  
Near Field ◽  
Microwave Imaging ◽  
Medical Applications ◽  
Vivaldi Antenna

Detection of Winding Radial Deformation in Power Transformers by Confocal Microwave Imaging

2014 ◽  
Vol 42 (6) ◽  
pp. 605-611 ◽  
Author(s):  
Raziyeh Mosayebi ◽  
Hamid Sheikhzadeh ◽  
Mohammad Sadegh Golsorkhi ◽  
Maryam Sadat Akhavan Hejazi ◽  
Gevorg B. Gharehpetian
Keyword(s):  
Microwave Imaging ◽  
Power Transformers ◽  
Radial Deformation ◽  
Confocal Microwave Imaging

Active Microwave Imaging For Medical Applications: Prelimary Results

1989 ◽  
Author(s):  
Genevieve Gaboriaud ◽  
Jean C. Bolomey ◽  
Christian Pichot ◽  
Patrice Berthaud
Keyword(s):  
Microwave Imaging ◽  
Medical Applications

scholarly journals Evaluation of Image Reconstruction Algorithms for Confocal Microwave Imaging: Application to Patient Data

Sensors ◽  
2018 ◽  
Vol 18 (6) ◽  
pp. 1678 ◽  
Author(s):  
Muhammad Elahi ◽  
Declan O’Loughlin ◽  
Benjamin Lavoie ◽  
Martin Glavin ◽  
Edward Jones ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Patient Data ◽  
Microwave Imaging ◽  
Reconstruction Algorithms ◽  
Imaging Application ◽  
Image Reconstruction Algorithms ◽  
Confocal Microwave Imaging

Mono Pulse Confocal Microwave Imaging in Breast Cancer Detection

2013 ◽  
Vol 748 ◽  
pp. 525-530
Author(s):  
Jin Zu Ji ◽  
Jing Li
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Fdtd Method ◽  
Microwave Imaging ◽  
Breast Cancer Detection ◽  
Assigned Value ◽  
The Difference ◽  
Imaging Algorithms ◽  
Backscattered Signals ◽  
Confocal Microwave Imaging

Mono pulse algorithm for confocal microwave imaging (CMI) for breast cancer detection is presented in this paper. All the antennas are used as receiver but only one is also used as transmitter. The transmitted signal is emitted for only once, thus the gross electromagnetic wave energy to the breast is reduced and the diagnosis time can be saved. Two confocal microwave imaging algorithms are presented in this paper: delay-sum-max and delay-production-sum. Both algorithms use the same delayed backscattered signals as the convectional CMI. The difference is how to use the delayed signals to form the image of the scattering intensity. Delay-sum-max method adds the signal together to generate the different value in confocal point via coherent and incoherent addition. Delay-production-sum algorithm products the delayed signal so as to make the assigned value in the confocal point outside the tumor is nearly zero. The image results can be compliment for more confirm diagnosis. Finite-difference time-domain (FDTD) method is used for simulation on 3 models of different tumor arrangements. The results show both methods are effective in detecting single-tumor, while there are some limitations in dealing with multi-tumor.

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