3D Huygens Principle Based Microwave Imaging Through MammoWave Device: Validation Through Phantoms

This work focuses on developing a 3D microwave imaging (MWI) algorithm based on Huygens principle (HP). Specifically, a novel, fast MWI device (MammoWave) has been presented and exploited for its capabilities of extending image reconstruction from 2D to 3D. For this purpose, dedicated phantoms containing 3D structured inclusion have been prepared with mixtures having different dielectric properties. Phantom measurements have been performed at multiple planes along the z-axis by simultaneously changing the transmitter and receiver antenna height via the graphic user interface (GUI) integrated with MammoWave. We have recorded the complex S21 multi-quote data at multiple planes along the z-axis. The complex multidimensional raw data has been processed via an enhanced HP based image algorithm for 3D image reconstruction. This paper demonstrates the successful detection and 3D visualization of the inclusion with varying dimensions at multiple planes/cross-sections along the z-axis with a dimensional error lower than 7.5%. Moreover, the paper shows successful detection and 3D visualization of the inclusion in a skull-mimicking phantom having a cylindrically shaped inclusion, with the location of the detected inclusion in agreement with the experimental setup. Additionally, the localization of a 3D structured spherical inclusion has been shown in a more complex scenario using a 3-layer cylindrically shaped phantom, along with the corresponding 3D image reconstruction and visualization.

[1]  N. Ghavami,et al.  3D Microwave Imaging Using Huygens Principle: A Phantom-based Validation , 2021, 2021 Photonics & Electromagnetics Research Symposium (PIERS).

[2]  N. Ghavami,et al.  Breast lesion detection through MammoWave device: Empirical detection capability assessment of microwave images’ parameters , 2021, PloS one.

[3]  N. Ghavami,et al.  Free-Space Operating Microwave Imaging Device for Bone Lesion Detection: A Phantom Investigation , 2020, IEEE Antennas and Wireless Propagation Letters.

[4]  Mohammad Ghavami,et al.  Developing Artefact Removal Algorithms to Process Data from a Microwave Imaging Device for Haemorrhagic Stroke Detection , 2020, Sensors.

[5]  Mario R. Casu,et al.  A Prototype Microwave System for 3D Brain Stroke Imaging , 2020, Sensors.

[6]  Gianluigi Tiberi,et al.  UWB Microwave Imaging for Inclusions Detection: Methodology for Comparing Artefact Removal Algorithms , 2020, BODYNETS.

[7]  Lorenzo Sani,et al.  UWB device for breast microwave imaging: phantom and clinical validations , 2019, Measurement.

[8]  M. O'Halloran,et al.  Comparison of radar-based microwave imaging algorithms applied to experimental breast phantoms , 2017, 2017 XXXIInd General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS).

[9]  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.

[10]  R. Narayanan,et al.  Medical microwave imaging and analysis , 2017 .

[11]  Ian Craddock,et al.  MARIA M4: clinical evaluation of a prototype ultrawideband radar scanner for breast cancer detection , 2016, Journal of medical imaging.

[12]  Rosa Scapaticci,et al.  A Compressive Sensing Approach for 3D Breast Cancer Microwave Imaging With Magnetic Nanoparticles as Contrast Agent , 2016, IEEE Transactions on Medical Imaging.

[13]  David J. Edwards,et al.  Non-iterative beamforming based on Huygens principle for multistatic ultrawide band radar: application to breast imaging , 2015 .

[14]  L. Jofre,et al.  3-D Microwave Magnitude Combined Tomography for Breast Cancer Detection Using Realistic Breast Models , 2012, IEEE Antennas and Wireless Propagation Letters.

[15]  Paul M. Meaney,et al.  Fast 3-D Tomographic Microwave Imaging for Breast Cancer Detection , 2012, IEEE Transactions on Medical Imaging.

[16]  G. Tiberi,et al.  UWB Microwave Imaging of Objects With Canonical Shape , 2012, IEEE Transactions on Antennas and Propagation.

[17]  N. Nikolova Microwave Imaging for Breast Cancer , 2011, IEEE Microwave Magazine.

[18]  David J. Edwards,et al.  Ultrawideband microwave imaging of cylindrical objects with inclusions , 2011 .

[19]  S. Semenov Microwave tomography: review of the progress towards clinical applications , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[20]  David J. Edwards,et al.  Ultra wideband: applications, technology and future perspectives , 2005 .

[21]  X. Li,et al.  Confocal microwave imaging for breast cancer detection: localization of tumors in three dimensions , 2002, IEEE Transactions on Biomedical Engineering.

[22]  Xu Li,et al.  Microwave imaging via space-time beamforming for early detection of breast cancer , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[23]  Anthony J. Devaney,et al.  Holography and the inverse source problem. Part II: Inhomogeneous media , 1985 .

[24]  R. P. Porter,et al.  Holography and the inverse source problem , 1982 .