A Multifrequency Inverse-Scattering Technique for Brain Stroke Microwave Diagnostics

In this paper, a microwave approach for brain stroke imaging is discussed. The developed technique relies on a Newton iterative scheme performing a regularization in Lebesgue spaces with non-constant exponent, which takes advantage of the simultaneous processing of multiple frequencies to enhance the reconstruction quality. The effectiveness of the approach is evaluated considering numerically simulated data concerning realistic 3D phantoms.

[1]  A. Abbosh,et al.  Stroke Diagnosis Using Microwave Techniques: Review of Systems and Algorithms , 2023, IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology.

[2]  S. Costanzo,et al.  Enhanced Machine Learning Approach for Accurate and Fast Resolution of Inverse Scattering Problem in Breast Cancer Detection , 2022, Electronics.

[3]  A. Abbosh,et al.  Calibrated Frequency-Division Distorted Born Iterative Tomography for Real-Life Head Imaging , 2021, IEEE Transactions on Medical Imaging.

[4]  Igor Bisio,et al.  Two Ways for Early Detection of a Stroke Through a Wearable Smart Helmet: Signal Processing vs. Electromagnetism , 2021, IEEE Wireless Communications.

[5]  Amin Abbosh,et al.  Flexible Electromagnetic Cap for Three-Dimensional Electromagnetic Head Imaging , 2021, IEEE Transactions on Biomedical Engineering.

[6]  L. Alon,et al.  A stroke detection and discrimination framework using broadband microwave scattering on stochastic models with deep learning , 2021, Scientific Reports.

[7]  M. Pastorino,et al.  Nonlinear S-Parameters Inversion for Stroke Imaging , 2021, IEEE Transactions on Microwave Theory and Techniques.

[8]  A. Abbosh,et al.  Expedited Stroke Imaging With Electromagnetic Polar Sensitivity Encoding , 2020, IEEE Transactions on Antennas and Propagation.

[9]  Claudio Estatico,et al.  Microwave Detection of Brain Injuries by Means of a Hybrid Imaging Method , 2020, IEEE Open Journal of Antennas and Propagation.

[10]  Joe LoVetri,et al.  Improved Tumor Detection via Quantitative Microwave Breast Imaging Using Eigenfunction-Based Prior , 2020, IEEE Transactions on Computational Imaging.

[11]  Igor Bisio,et al.  Variable-Exponent Lebesgue-Space Inversion for Brain Stroke Microwave Imaging , 2020, IEEE Transactions on Microwave Theory and Techniques.

[12]  Navid Ghavami,et al.  Experimental Validation of Microwave Tomography with the DBIM-TwIST Algorithm for Brain Stroke Detection and Classification , 2020, Sensors.

[13]  Lorenzo Crocco,et al.  Design and Numerical Characterization of a Low-Complexity Microwave Device for Brain Stroke Monitoring , 2018, IEEE Transactions on Antennas and Propagation.

[14]  Andrea Massa,et al.  Real‐time brain stroke detection through a learning‐by‐examples technique—An experimental assessment , 2017 .

[15]  Serguei Semenov,et al.  Electromagnetic Tomography for Detection, Differentiation, and Monitoring of Brain Stroke: A Virtual Data and Human Head Phantom Study. , 2017, IEEE Antennas and Propagation Magazine.

[16]  Francesca Rapetti,et al.  Numerical Modeling and High-Speed Parallel Computing: New Perspectives on Tomographic Microwave Imaging for Brain Stroke Detection and Monitoring. , 2017, IEEE Antennas and Propagation Magazine.

[17]  Keith D Paulsen,et al.  Two‐step inversion with a logarithmic transformation for microwave breast imaging , 2017, Medical physics.

[18]  Shireen D. Geimer,et al.  Electrical Characterization of Glycerin: Water Mixtures: Implications for Use as a Coupling Medium in Microwave Tomography , 2017, IEEE Transactions on Microwave Theory and Techniques.

[19]  Matteo Pastorino,et al.  A Multifrequency Inexact-Newton Method in $\bm{L^p}$ Banach Spaces for Buried Objects Detection , 2015 .

[20]  Andreas Fhager,et al.  Microwave-Based Stroke Diagnosis Making Global Prehospital Thrombolytic Treatment Possible , 2014, IEEE Transactions on Biomedical Engineering.

[21]  Amin M. Abbosh,et al.  Modeling Human Head Tissues Using Fourth-Order Debye Model in Convolution-Based Three-Dimensional Finite-Difference Time-Domain , 2014, IEEE Transactions on Antennas and Propagation.

[22]  M. Fujii.  Maximum Frequency Range Limit of Multi-Pole Debye Models of Human Body Tissues , 2012, IEEE Microwave and Wireless Components Letters.

[23]  P. Hästö,et al.  Lebesgue and Sobolev Spaces with Variable Exponents , 2011 .

[24]  Niels Kuster,et al.  The Virtual Family—development of surface-based anatomical models of two adults and two children for dosimetric simulations , 2010, Physics in medicine and biology.

[25]  D. Corfield,et al.  Microwave Tomography for Brain Imaging: Feasibility Assessment for Stroke Detection , 2008 .

[26]  G. Massa,et al.  Spatial Domain Indirect Holography for a Full Phaseless Approach to Biomedical Microwave Imaging , 2022, IEEE Open Journal of Antennas and Propagation.

[27]  P. Kosmas,et al.  Experimental Validation of the DBIM-TwIST Algorithm for Brain Stroke Detection and Differentiation Using a Multi-Layered Anatomically Complex Head Phantom , 2022, IEEE Open Journal of Antennas and Propagation.

[28]  L. Crocco,et al.  Experimental Assessment of Real-Time Brain Stroke Monitoring via a Microwave Imaging Scanner , 2022, IEEE Open Journal of Antennas and Propagation.

[29]  G. Fumera,et al.  Microwave Imaging of the Neck by Means of Artificial Neural Networks for Tumor Detection , 2021, IEEE Open Journal of Antennas and Propagation.

[30]  Mahsa Baktashmotlagh,et al.  Closing the Gap of Simulation to Reality in Electromagnetic Imaging of Brain Strokes via Deep Neural Networks , 2021, IEEE Transactions on Computational Imaging.