Physical Insight Unveils New Imaging Capabilities of Orthogonality Sampling Method

Bevacqua, Martina T.; Isernia, Tommaso; Palmeri, Roberta; Akinci, Mehemet Nuri; Crocco, Lorenzo

doi:10.1109/tap.2019.2963229

Cited by 34 publications

(29 citation statements)

References 34 publications

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“…The OSM provides an approximation of the geometrical properties of the unknown targets by plotting an indicator function I(r p ) on an arbitrary grid of points r p = (x p , y p ) sampling the imaging domain Ω [12].…”

Section: A Qualitative Inversion: Orthogonality Sampling Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Enhanced Qualitative Microwave Imaging: Rationale and Initial Assessment

Yago

Cavagnaro

Crocco

2021

2021 15th European Conference on Antennas and Propagation (EuCAP)

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Section: A Qualitative Inversion: Orthogonality Sampling Methodsmentioning

confidence: 99%

“…As far as the qualitative imaging approach is concerned, the proposed processing framework exploits the orthogonality sampling method (OSM) [12]. In OSM, the evaluation of the indicator function does not require the explicit solution of an inverse problem, so that there is no need for determining a regularization parameter.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Enhanced Qualitative Microwave Imaging: Rationale and Initial Assessment

Yago

Cavagnaro

Crocco

2021

2021 15th European Conference on Antennas and Propagation (EuCAP)

Self Cite

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“…In the cases presented in this paper, ߳ ሺ߱ሻ is taken as the Debye model of the external coupling medium, which has similar properties to the internal head tissues. It is worth noting that the SAFT formula in (6) is similar to the orthogonality sampling method (OSM), introduced in [32] and recently physically interpreted in [33], [34]. Basically, in the OSM, the phase shifting term in the SAFT indicator function (6) is replaced by another phase factor, which is related to the far field pattern of the Green's function.…”

Section: Construction Of An Approximate Qualitative Mapmentioning

confidence: 99%

Microwave Detection of Brain Injuries by Means of a Hybrid Imaging Method

Fedeli

Estatico

Pastorino

et al. 2020

IEEE Open J. Antennas Propag.

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Brain injuries represent a critical situation, where both detection and monitoring should be quick and accurate at the same time. Microwave techniques are thus gaining attention in the diagnostic process of these diseases. However, the detection of inhomogeneities and variations inside the human brain by using electromagnetic fields at microwave frequencies is a very challenging inverse problem. An innovative hybrid microwave imaging method is introduced in this contribution, which combines the benefits of a fast qualitative processing technique with an accurate tomographic reconstruction of the dielectric properties of the human head. This method has been successfully applied to obtain microwave images from both synthetic data and laboratory measurements. Numerical simulations involve three-dimensional realistic models of stroke-affected heads, whereas simplified cylindrical phantoms have been exploited for the experimental validation of the approach. In both conditions, the proposed technique yields promising results, which may be considered a preliminary step towards the realization of a clinical imaging prototype.

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“…However, to retrieve the dielectric profile of the target, most of standard CNN-based architectures do not work directly with the measurements of scattered fields collected by the receiving antennas, but require a preliminary image retrieved by other techniques [41], [42], [45], [51], [54]. This image may be obtained with other inverse-scattering approaches, e.g., orthogonality sampling method (OSM) [55], back propagation (BP) [56] and direct sampling method [57].…”

Section: Introductionmentioning

confidence: 99%

Microwave Imaging of the Neck by Means of Artificial Neural Networks for Tumor Detection

Dachena

Fedeli²,

Fanti

et al. 2021

IEEE Open J. Antennas Propag.

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In this paper, a microwave imaging approach based on artificial neural networks (ANNs) for neck tumor detection is presented. The aim of this technique is to retrieve the geometric and dielectric properties of the neck to identify the possible presence of tumors, starting from scattered electric field data. A fully-connected neural network is developed to test the feasibility of the proposed approach. Moreover, a numerical model including the main features of a cross section of the neck is specifically designed in order to create a suitable training dataset. Subsequently, for the optimization of the ANN architecture and performance evaluation, a numerical analysis is conducted. A set of simulated cases, based on realistic neck phantoms, is tested to evaluate the robustness of the network. Preliminary results show the possibility to identify and locate neck tumors.

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Physical Insight Unveils New Imaging Capabilities of Orthogonality Sampling Method

Cited by 34 publications

References 34 publications

Deep Learning-Enhanced Qualitative Microwave Imaging: Rationale and Initial Assessment

Deep Learning-Enhanced Qualitative Microwave Imaging: Rationale and Initial Assessment

Microwave Detection of Brain Injuries by Means of a Hybrid Imaging Method

Microwave Imaging of the Neck by Means of Artificial Neural Networks for Tumor Detection

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