Microwave Breast Imaging With Prior Ultrasound Information

Qin, Yingying; Rodet, Thomas; Lambert, Marc; Lesselier, Dominique

doi:10.1109/ojap.2020.3019953

Cited by 26 publications

(6 citation statements)

References 38 publications

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“…Ideally, the considered imaging modality should support clinicians' decision-making by providing a probability map, showing for each pixel/voxel the probability of it containing a tumor. Other researchers [24][25][26][27][28][29][30][31] have developed various approaches to provide similar information on the dimensions and location of the breast tumor in MWI. In [26][27][28]30] additional ultrasound data is incorporated to help with detection of tissue boundaries.…”

Section: Introductionmentioning

confidence: 99%

“…Other researchers [24][25][26][27][28][29][30][31] have developed various approaches to provide similar information on the dimensions and location of the breast tumor in MWI. In [26][27][28]30] additional ultrasound data is incorporated to help with detection of tissue boundaries. In [26] for example, a multi-input multitask convolutional neural network (CNN) is provided that takes both electromagnetic (EM) data from MWI and ultrasound data, near the output the model splits into two separate processing paths to produce two outputs: a regression output and segmentation output of several tissue classes, including tumor class.…”

Section: Introductionmentioning

confidence: 99%

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Microwave Breast Sensing via Deep Learning for Tumor Spatial Localization by Probability Maps

Borghouts,

Ambrosanio,

Franceschini

et al. 2023

Bioengineering

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Background: microwave imaging (MWI) has emerged as a promising modality for breast cancer screening, offering cost-effective, rapid, safe and comfortable exams. However, the practical application of MWI for tumor detection and localization is hampered by its inherent low resolution and low detection capability. Methods: this study aims to generate an accurate tumor probability map directly from the scattering matrix. This direct conversion makes the probability map independent of specific image formation techniques and thus potentially complementary to any image formation technique. An approach based on a convolutional neural network (CNN) is used to convert the scattering matrix into a tumor probability map. The proposed deep learning model is trained using a large realistic numerical dataset of two-dimensional (2D) breast slices. The performance of the model is assessed through visual inspection and quantitative measures to assess the predictive quality at various levels of detail. Results: the results demonstrate a remarkably high accuracy (0.9995) in classifying profiles as healthy or diseased, and exhibit the model’s ability to accurately locate the core of a single tumor (within 0.9 cm for most cases). Conclusion: overall, this research demonstrates that an approach based on neural networks (NN) for direct conversion from scattering matrices to tumor probability maps holds promise in advancing state-of-the-art tumor detection algorithms in the MWI domain.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Microwave Breast Sensing via Deep Learning for Tumor Spatial Localization by Probability Maps

Borghouts,

Ambrosanio,

Franceschini

et al. 2023

Bioengineering

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“…Moreover, another main limitation, especially for quantitative microwave imaging, is related to the well-known nonlinearity and ill-posedness issues and their consequences affecting these kinds of inverse problems, such as the presence of local minima [ 16 ], and whose difficulty further increases when dealing with three-dimensional modelling, which is computationally demanding. In particular, the impact of the ill-posedness can be partially mitigated by exploiting some a priori information [ 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Deep Learning Approach for Quantitative Microwave Breast Imaging in Real-Time Applications

et al. 2022

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(1) Background: In this paper, an artificial neural network approach for effective and real-time quantitative microwave breast imaging is proposed. It proposes some numerical analyses for the optimization of the network architecture and the improvement of recovery performance and processing time in the microwave breast imaging framework, which represents a fundamental preliminary step for future diagnostic applications. (2) Methods: The methodological analysis of the proposed approach is based on two main aspects: firstly, the definition and generation of a proper database adopted for the training of the neural networks and, secondly, the design and analysis of different neural network architectures. (3) Results: The methodology was tested in noisy numerical scenarios with different values of SNR showing good robustness against noise. The results seem very promising in comparison with conventional nonlinear inverse scattering approaches from a qualitative as well as a quantitative point of view. (4) Conclusion: The use of quantitative microwave imaging and neural networks can represent a valid alternative to (or completion of) modern conventional medical imaging techniques since it is cheaper, safer, fast, and quantitative, thus suitable to assist medical decisions.

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“…[7] and [8] incorporate prior information about the boundaries between different tissues derived from magnetic resonance imaging (MRI). Recently, structural information derived from ultrasound rather than expensive MRI has been used as prior information for quantitative microwave imaging [9]- [11]. As another approach, there are methods for direct shape reconstruction from microwave scattering data, such as linear sampling methods and level set methods [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Overcoming Insufficient Microwave Scattering Data in Microwave Tomographic Imaging

Simonov

2021

IEEE Access

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One of the most challenging problems in microwave tomography is the reconstruction of dielectric properties inside parts of the human body. This is a crucial task for medical applications such as early cancer diagnostics and focused microwave thermotherapy. If not enough antennas are used, the amount of microwave scattering measurement data will be insufficient for satisfactory imaging with the conventional approach. This paper suggests a method for overcoming insufficient microwave scattering data using simultaneous reconstruction of the body shape and its interior dielectric properties, given the restriction that the shape must be regular and smooth. This restriction allows for reduced uncertainty in the reconstructed images. We developed a 2.5D electromagnetic solver and an iterative method for estimating body shape in the process of image reconstruction. This approach provides successful imaging even with insufficient scattering data. In addition, the simultaneous reconstruction leads to improved accuracy in both restorations.INDEX TERMS Microwave imaging, microwave tomography, electromagnetic scattering, inverse problem, shape reconstruction, biomedical imaging.

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Microwave Breast Imaging With Prior Ultrasound Information

Cited by 26 publications

References 38 publications

Microwave Breast Sensing via Deep Learning for Tumor Spatial Localization by Probability Maps

Microwave Breast Sensing via Deep Learning for Tumor Spatial Localization by Probability Maps

An End-to-End Deep Learning Approach for Quantitative Microwave Breast Imaging in Real-Time Applications

Overcoming Insufficient Microwave Scattering Data in Microwave Tomographic Imaging

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