Enhancement of Multifrequency Microwave Tomography Breast Imaging System using Flexible Preconditioner Based Krylov Subspace Methods
##plugins.pubIds.doi.readerDisplayName##:
https://doi.org/10.13052/2022.ACES.J.370602关键词:
CGLS, ill-posedness, Krylov subspace method, microwave tomography, regularization摘要
Microwave Tomography Imaging System (MwTIS) is an emerging tool for medical diagnosis in the non-invasive screening process. This paper addresses the ill-condition problem by proposing two new schemes incorporated into the DBIM image reconstructed algorithm for high frequencies in MwTIS. The first scheme is to propose an optimal step frequency using the degree of ill-posedness value for reducing the frequency diversity problem. The second scheme is to propose Krylov Subspace-based regularization method called Flexible Preconditioned Conjugate Gradient Least Square (FP-CGLS) method to resolve the ill-condition problem. The iteratively updated preconditioner matrix in the proposed FP-CGLS method reduces the number of iterations and it is stable in high-level Gaussian noise. The efficiency of the proposed FP-CGLS method is validated by imposing Gaussian noise up to 30% in scattered breast phantom in the multifrequency range of 2 GHz -3 GHz It achieves an enhanced reconstructed image at 12 iterations with a relative error of 0.1802 for 20% of Gaussian noise and for the same scheme the existing CGLS method has a 0.4480 relative error at the 77 iterations. Further, the FP-CGLS along with the DBIM method produces a reconstructed image with the accuracy of 0.8760 in four DBIM iterations.
##plugins.generic.usageStats.downloads##
参考
N. Abdollahi, I. Jeffrey, and J. LoVetri, “Improved tumor detection via quantitative microwave breast imaging using eigenfunction-based prior,” IEEE Trans. Comput. Imaging, vol. 6, pp. 1194-1202, 2020.
B. Oliveira, D. Godinho, M. O’Halloran, M. Glavin, E. Jones, and R. Conceição, “Diagnosing breast cancer with microwave technology: Remaining challenges and potential solutions with machine learning,” Diagnostics, vol. 8, no. 2, pp. 1-22, 2018.
G. Boverman, C. E. L. Davis, S. D. Geimer, and P. M. Meaney, “Image registration for microwave tomography of the breast using priors from nonsimultaneous previous magnetic resonance images,” IEEE J. Electromagn. RF Microwaves Med. Biol., vol. 2, no. 1, pp. 2-9, Mar. 2018.
Semenov, “Microwave tomography: Review of the progress towards clinical application,” Philos. Trans. A. Math Phys. Eng. Sci., vol. 367, pp. 3021-3042, 2009.
M. Ambrosanio, P. Kosmas, and V. Pascazio, “A multithreshold iterative DBIM-based algorithm for the imaging of heterogeneous breast tissues,” IEEE Trans. Biomed. Eng., vol. 66, no. 2, pp. 509-520, 2019.
Z. Miao and P. Kosmas, “Multiple-frequency DBIM-TwIST algorithm for microwave breast imaging,” IEEE Trans. Antennas Propag., vol. 65, no. 5, pp. 2507-2516, 2017.
M. Azghani, P. Kosmas, and F. Marvasti, “Fast microwave medical imaging based on iterative smoothed adaptive thresholding,” IEEE Antennas Wirel. Propag. Lett., vol. 14, pp. 438-441, 2015.
H. Harada, D. J. N. Wall, T. Takenaka, and M. Tanaka, “Conjugate gradient method applied to inverse scattering problem,” IEEE Trans. Antennas Propag., vol. 43, no. 8, pp. 784-792, 1995.
L. M. Neira, B. D. Van Veen, and S. C. Hagness, “High-resolution microwave breast imaging using a 3-d inverse scattering algorithm with a variable-strength spatial prior constraint,” IEEE Trans. Antennas Propag., vol. 65, no. 11, pp. 6002-6014, Nov. 2017.
Y. Suzuki and S. Kidera, “Resolution enhanced distorted born iterative method using ROI limiting scheme for microwave breast imaging,” IEEE J. Electromagn. RF Microwaves Med. Biol., vol. 5, no. 4, pp. 379-385, 2021.
M. T. Bevacqua and R. Scapaticci, “A compressive sensing approach for 3D breast cancer microwave imaging with magnetic nanoparticles as contrast agent,” IEEE Trans. Med. Imag, vol. 35, no. 2, pp. 665-673, Feb. 2016.
M. A. Aldhaeebi, K. Alzoubi, T. S. Almoneef, S. M. Bamatraf, H. Attia, and O. M. Ramahi, “Review of microwaves techniques for breast cancer detection,” Sensors, vol. 20, no. 390, pp. 1-38, 2020.
C. Kaye, I. Jeffrey, and J. LoVetri, “Improvement of multi-frequency microwave breast imaging through frequency cycling and tissue-dependent mapping,” IEEE Trans. Antennas Propag., vol. 67, no. 11, pp. 7087-7096, Nov. 2019.
R. Scapaticci, P. Kosmas, and L. Crocco, “Wavelet-based regularization for robust microwave imaging in medical applications,” IEEE Trans. Biomed. Eng., vol. 62, no. 4, pp. 1195-1202, 2015.
T. Rubaek, P. M. Meaney, P. Meincke, and K. D. Paulsen, “Nonlinear microwave imaging for breast-cancer screening using Gauss-Newton’s method and the CGLS inversion algorithm,” IEEE Trans. Antennas Propag., vol. 55, no. 8, pp. 2320-2331, 2007.
J. D. Shea, P. Kosmas, S. C. Hagness, and B. D. Van Veen. “Three-dimensional microwave imaging of realistic numerical breast phantoms via a multiple-frequency inverse scattering technique,” Medical Phys., vol. 37, no. 8, pp. 4210-4226, 2010.
D. W. Winters, J. D. Shea, P. Kosmas, B. D. Van Veen, and S. C. Hagness, “Three-dimensional microwave breast imaging: Dispersive dielectric properties estimation using patient-specific basis functions,” IEEE Trans. Med. Imaging, vol. 28, no. 7, pp. 969-981, 2009.
Q. Fang, P. M. Meaney, and K. D. Paulsen, “Singular value analysis of the Jacobian matrix in microwave image reconstruction,” IEEE Trans. Antennas Propag., vol. 54, no. 8, pp. 2371-2380, Aug. 2006.
S. Gazzola, “Fast nonnegative least squares through flexible Krylov subspaces,” SIAM J. Sci. Comput., vol. 39, pp. A655-A679, 2017.
Available from https://uwcem.ece.wisc.edu/phantomRepository.html
P. T. Nguyen, A. Abbosh, and S. Crozier, “Microwave hyperthermia for breast cancer treatment using electromagnetic and thermal focusing tested on realistic breast models and antenna arrays,” IEEE Trans. Antennas Propag., vol. 63, no. 10, pp. 4426-4434, Oct. 2015.
