Quantitative Microwave Imaging for Breast Cancer Detection Using a Planar 2.45 GHz System

Henriksson, Tommy; Joachimowicz, Nadine; Conessa, Christophe; Bolomey, Jean-Charles

doi:10.1109/tim.2010.2045540

Cited by 103 publications

(66 citation statements)

References 27 publications

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“…Henriksson et al made use of a microwave camera, at 2.45 GHz, to quantitatively restore the dielectric properties of materials [77]. In a quantitative approach, in place of approximations, the non-linear diffraction problem is solved at the cost of heavy computations.…”

Section: Breast Nri At Experimental Levelmentioning

confidence: 99%

Near-Field Radar Microwave Imaging as an Add-on Modality to Mammography

Ghanbarzadeh-Dagheyan¹,

Molaei²,

Obermeier³

et al. 2017

New Perspectives in Breast Imaging

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According to global statistics, there is a high incidence of cancer in western countries; and, due to the limited resources available in most health care systems, it seems like one of the most feasible options to fight against cancer might be strict prevention policiessuch as eliminating carcinogens in people's daily lives. Nevertheless, early cancer detection and effective treatment are still necessary, and understanding their efficacy and limitations are important issues that need to be addressed in order to ultimately enhance patients' survival rate. In the case of breast cancer, some of the problems faced by conventional mammography have been addressed in the literature; they include high rate of false-positive and false-negative results, as well as the possibility of overdiagnosis. New technologies, such as digital breast tomosynthesis (DBT), have been able to improve the sensitivity and specificity by using 3D imaging. However, the low contrast (1%) existing between tumors and healthy fibroglandular tissue at X-ray frequencies has been identified as one of the main causes of misdiagnosis in both conventional 2D mammography and DBT. Near-field radar imaging (NRI) provides a unique opportunity to overcome this problem, since the contrast existing between the aforementioned tissues is intrinsically higher (10%) at microwave frequencies. Moreover, the low resolution and highly complex scattering patterns of microwave systems can be enhanced by using prior information from other modalities, such as the DBT. Therefore, a multimodal DBT/NRI imaging system is proposed to exploit their individual strengths while minimizing their weaknesses. In this work, the foundation of this idea is reviewed, and a preliminary design and experimental validation of the NRI system, used as a DBT complement, is introduced.

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Section: Breast Nri At Experimental Levelmentioning

confidence: 99%

Near-Field Radar Microwave Imaging as an Add-on Modality to Mammography

Ghanbarzadeh-Dagheyan¹,

Molaei²,

Obermeier³

et al. 2017

New Perspectives in Breast Imaging

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“…These algorithms have been tested with biomedical experimental data, e.g., for various biological phantoms [16,[22][23][24] and a human forearm [16,25,26] in 2D, for plastic rods in saline [27] in pseudo-3D, for a canine thorax [28] in a 3D scalar approximation and for dielectric balls [29] and a pig hind-leg [30] in fully-vectorial 3D. Quantitative imaging of the breast is reported, e.g., employing 2D single-frequency algorithms with synthetic data [26,31] or with phantom and/or clinical data [32][33][34], a 3D single-frequency algorithm in a scalar approximation with synthetic data [35], a 3D TD algorithm with synthetic and phantom data [36], a 3D multiple-frequency vectorial algorithm with synthetic data [37][38][39][40] and 3D single-frequency fully vectorial algorithms with synthetic data [41][42][43] and with single-polarized clinical data [3]. In this paper we consider a 3D single-frequency fully vectorial imaging algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…It is solved by letting a nonlinear optimization scheme minimize a data-fit cost function, where some kind of regularization is provided to cope with the ill-posedness. The Gauss-Newton method has been employed for microwave breast imaging by several authors, e.g., [3,14,26,33,34,[37][38][39][40][41][42][43][44][45] and it is used in this paper as well. The cost function in [3,14,33,34,[37][38][39][40]44] is the non-regularized data-fitthe method is often referred to as the Distorted Born Iterative Method (DBIM) [11].…”

Section: Introductionmentioning

confidence: 99%

“…Its minimum generally is not well-defined, especially when the number of reconstruction variables is large. In this case a regularization is applied to the permittivity update in each iteration of the optimization: Tikhonov regularization -similar to [11,13] -in [14,34,37], a combination of L1 and L2 norm sparsity promoting regularization in [38] and other compressive sensing schemes in, e.g., [44] and a conjugate gradient for least squares (CGLS) algorithm in [3,33,39,40]. In the present paper, as in [26,42,43,45] for microwave breast imaging and as in, e.g., [19,30,[46][47][48][49][50][51][52] for microwave imaging, we adopt a regularized cost function consisting of the data fit term and a regularization term.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

3d Microwave Tomography With Huber Regularization Applied to Realistic Numerical Breast Phantoms

Bai¹,

Franchois²,

Pižurica³

2016

PIER

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Abstract-Quantitative active microwave imaging for breast cancer screening and therapy monitoring applications requires adequate reconstruction algorithms, in particular with regard to the nonlinearity and ill-posedness of the inverse problem. We employ a fully vectorial three-dimensional nonlinear inversion algorithm for reconstructing complex permittivity profiles from multi-view single-frequency scattered field data, which is based on a Gauss-Newton optimization of a regularized cost function. We tested it before with various types of regularizing functions for piecewise-constant objects from Institut Fresnel and with a quadratic smoothing function for a realistic numerical breast phantom. In the present paper we adopt a cost function that includes a Huber function in its regularization term, relying on a Markov Random Field approach. The Huber function favors spatial smoothing within homogeneous regions while preserving discontinuities between contrasted tissues. We illustrate the technique with 3D reconstructions from synthetic data at 2 GHz for realistic numerical breast phantoms from the University of Wisconsin-Madison UWCEM online repository: we compare Huber regularization with a multiplicative smoothing regularization and show reconstructions for various positions of a tumor, for multiple tumors and for different tumor sizes, from a sparse and from a denser data configuration.

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“…This is indicative of the attention on this subject from academic, industrial, and governmental researchers and experts. As a matter of fact, the range of potential applications is wide and it spans from the more traditional (e.g., geophysical investigations and remote sensing [3][4][5], nondestructive testing and evaluation [6][7][8][9][10], and medical imaging [11][12][13][14][15][16][17][18]) to the latest ones mainly related to security and surveillance (e.g., throughthe-wall imaging [19][20][21][22][23][24]) up to more recent applications [25].…”

Section: Introductionmentioning

confidence: 99%

Microwave Imaging Within the Interval Analysis Framework

Rocca¹,

Carlin²,

Manica³

et al. 2013

PIER

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Abstract-An approach based on the use of the arithmetic of intervals and Interval Analysis for the solution of inverse scattering problems is presented and assessed. By exploiting the property of the Interval Analysis to find the global minimum of a functional in a n-dimensional space, the proposed approach adopts a branch and bound process to discard the regions of the solutions space not containing the global solution, while keeping those where a feasible solution is expected until a suitable converge criterion is reached. A representative set of results concerned with the reconstruction of circular dielectric objects within the first-order Born approximation are reported and discussed to show potentialities and current limitations of the proposed approach.

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Quantitative Microwave Imaging for Breast Cancer Detection Using a Planar 2.45 GHz System

Cited by 103 publications

References 27 publications

Near-Field Radar Microwave Imaging as an Add-on Modality to Mammography

Near-Field Radar Microwave Imaging as an Add-on Modality to Mammography

3d Microwave Tomography With Huber Regularization Applied to Realistic Numerical Breast Phantoms

Microwave Imaging Within the Interval Analysis Framework

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