Real-Time 3D Microwave Medical Imaging With Enhanced Variational Born Iterative Method

被引:16
作者
Fang, Yuan [1 ]
Bakian-Dogaheh, Kazem [1 ]
Moghaddam, Mahta [1 ]
机构
[1] Univ Southern Calif, Ming Hsieh Dept Elect Engn, Microwave Syst Sensors & Imaging Lab MiXIL, Los Angeles, CA 90089 USA
关键词
Graphics processing unit image reconstruction; microwave imaging; real and imaginary sepa-ration; real time acceleration; variational Born iterative method; GAUSS-NEWTON METHOD; TOMOGRAPHY; INVERSION; PARAMETER; CONTRAST;
D O I
10.1109/TMI.2022.3210494
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this paper, we present a new variational Born iterative method (VBIM) for real-time microwave imaging (MWI) applications. The S-parameter volume integral equation and waveport vector Green's function are implemented to utilize the measured signal of the MWI system. Meanwhile, the real and imaginary separation (RIS) approach is used at each iterative step to simultaneously reconstruct the dielectric permittivity and conductivity of unknown objects. Compared with the Born iterative method and distorted Born iterative method, VBIM requires less computational time to reach the convergence threshold. The graphics processing unit based acceleration technique is implemented for real-time imaging. To demonstrate the efficiency and accuracy of this VBIM-RIS method, synthetic analysis of a complex multi-layer spherical phantom is first conducted. Then, the algorithm is tested with measured data using our new MWI system prototype. Finally, a synthetic brain-tumor phantom model under a thermal therapy procedure is monitored to exemplify the real-time imaging with about 5 seconds per reconstruction frame.
引用
收藏
页码:268 / 280
页数:13
相关论文
共 57 条
[1]   Application of the Multiplicative Regularized Gauss-Newton Algorithm for Three-Dimensional Microwave Imaging [J].
Abubakar, Aria ;
Habashy, Tarek M. ;
Pan, Guangdong ;
Li, Mao-Kun .
IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, 2012, 60 (05) :2431-2441
[2]   Variational iterative method for scattering problems [J].
Adhikari, SK .
CHEMICAL PHYSICS LETTERS, 1996, 258 (5-6) :595-600
[3]  
[Anonymous], 2016, VHP FEMALE V2 2 FEM
[4]   Use of Field-Perturbing Elements to Increase Nonredundant Data for Microwave Imaging Systems [J].
Asefi, Mohammad ;
LoVetri, Joe .
IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, 2017, 65 (09) :3172-3179
[5]   Microwave Medical Imaging Based on Sparsity and an Iterative Method With Adaptive Thresholding [J].
Azghani, Masoumeh ;
Kosmas, Panagiotis ;
Marvasti, Farokh .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2015, 34 (02) :357-365
[6]   (Quasi-)Real-Time Inversion of Airborne Time-Domain Electromagnetic Data via Artificial Neural Network [J].
Bai, Peng ;
Vignoli, Giulio ;
Viezzoli, Andrea ;
Nevalainen, Jouni ;
Vacca, Giuseppina .
REMOTE SENSING, 2020, 12 (20) :1-11
[7]  
Bakian-Dogaheh K., 2022, AWPL09222016 U SO CA
[8]   Variable-Exponent Lebesgue-Space Inversion for Brain Stroke Microwave Imaging [J].
Bisio, Igor ;
Estatico, Claudio ;
Fedeli, Alessandro ;
Lavagetto, Fabio ;
Pastorino, Matteo ;
Randazzo, Andrea ;
Sciarrone, Andrea .
IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, 2020, 68 (05) :1882-1895
[9]   Brain Stroke Microwave Imaging by Means of a Newton-Conjugate-Gradient Method in Lp Banach Spaces [J].
Bisio, Igor ;
Estatico, Claudio ;
Fedeli, Alessandro ;
Lavagetto, Fabio ;
Pastorino, Matteo ;
Randazzo, Andrea ;
Sciarrone, Andrea .
IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, 2018, 66 (08) :3668-3682
[10]  
Chen G., 2016, THESIS U SO CALIFORN