Feasibility of brain stroke imaging with microwaves

Dilman, İsmail; Yıldırım, Uğur; Coşğun, Sema; Dogu, Semih; Çayören, Mehmet; Akduman, İbrahim

doi:10.1109/apace.2016.7916454

Cited by 13 publications

(10 citation statements)

References 9 publications

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“…The relatively long wavelength allows for penetration in living tissue as deep as ten centimeters, especially at the low-gigahertz range. This makes microwave technology a promising candidate in diagnostic applications such as breast-cancer screening [ 1 , 2 , 3 , 4 , 5 ], bone-disease monitoring [ 6 , 7 ], brain-stroke [ 8 , 9 , 10 ] and lung-cancer diagnostics [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Active Sensor for Microwave Tissue Imaging with Bias-Switched Arrays

Foroutan

Nikolova

2018

Sensors

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A prototype of a bias-switched active sensor was developed and measured to establish the achievable dynamic range in a new generation of active arrays for microwave tissue imaging. The sensor integrates a printed slot antenna, a low-noise amplifier (LNA) and an active mixer in a single unit, which is sufficiently small to enable inter-sensor separation distance as small as 12 mm. The sensor’s input covers the bandwidth from 3 GHz to 7.5 GHz. Its output intermediate frequency (IF) is 30 MHz. The sensor is controlled by a simple bias-switching circuit, which switches ON and OFF the bias of the LNA and the mixer simultaneously. It was demonstrated experimentally that the dynamic range of the sensor, as determined by its ON and OFF states, is 109 dB and 118 dB at resolution bandwidths of 1 kHz and 100 Hz, respectively.

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Section: Introductionmentioning

confidence: 99%

Active Sensor for Microwave Tissue Imaging with Bias-Switched Arrays

Foroutan

Nikolova

2018

Sensors

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“…However, in this study the incorporated frequency-dependent closed-form models are evaluated at 1 GHz only. This is because 1 GHz is the most appropriate frequency for a MW head-imaging system, suggested by the previously referenced literature [ 14 , 23 , 25 , 27 , 35 , 38 , 40 , 42 ] and the evaluations we made during our research [ 43 – 45 ]. This frequency allows a good penetration of MW signals into a human head, while providing a reasonable spatial resolution of brain images.…”

Section: Methodsmentioning

confidence: 93%

“…It can efficiently operate in the frequency range of 0.92–1.12 GHz with a reflection coefficient less than −10 dB in free space. In this study, the dipole antenna was designed for a 1 GHz centre frequency because this value is reported as the most suitable frequency by the literature to date for the design of an effective MW head-imaging system [ 14 , 23 , 25 , 27 , 35 , 38 , 40 , 42 ]. Therefore, subsequent simulations have been performed at the same frequency.…”

Section: Methodsmentioning

confidence: 99%

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Finite-element analysis of microwave scattering from a three-dimensional human head model for brain stroke detection

2018

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In this paper, a detailed analysis of microwave (MW) scattering from a three-dimensional (3D) anthropomorphic human head model is presented. It is the first time that the finite-element method (FEM) has been deployed to study the MW scattering phenomenon of a 3D realistic head model for brain stroke detection. A major contribution of this paper is to add anatomically more realistic details to the human head model compared with the literature available to date. Using the MRI database, a 3D numerical head model was developed and segmented into 21 different types through a novel tissue-mapping scheme and a mixed-model approach. The heterogeneous and frequency-dispersive dielectric properties were assigned to brain tissues using the same mapping technique. To mimic the simulation set-up, an eight-elements antenna array around the head model was designed using dipole antennae. Two types of brain stroke (haemorrhagic and ischaemic) at various locations inside the head model were then analysed for possible detection and classification. The transmitted and backscattered signals were calculated by finding out the solution of the Helmholtz wave equation in the frequency domain using the FEM. FE mesh convergence analysis for electric field values and comparison between different types of iterative solver were also performed to obtain error-free results in minimal computational time. At the end, specific absorption rate analysis was conducted to examine the ionization effects of MW signals to a 3D human head model. Through computer simulations, it is foreseen that MW imaging may efficiently be exploited to locate and differentiate two types of brain stroke by detecting abnormal tissues’ dielectric properties. A significant contrast between electric field values of the normal and stroke-affected brain tissues was observed at the stroke location. This is a step towards generating MW scattering information for the development of an efficient image reconstruction algorithm.

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“…In the same year, a MoM–CSI methodology was implemented by Dilman et al (2016) to evaluate the feasibility of microwave imaging for hemorrhagic stroke detection. This study utilized a 2-D realistic head model and introduced the noise effects along with the incorporation of different background mediums.…”

Section: Introductionmentioning

confidence: 99%

Levels of detail analysis of microwave scattering from human head models for brain stroke detection

Qureshi¹,

Mustansar²

2017

PeerJ

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In this paper, we have presented a microwave scattering analysis from multiple human head models. This study incorporates different levels of detail in the human head models and its effect on microwave scattering phenomenon. Two levels of detail are taken into account; (i) Simplified ellipse shaped head model (ii) Anatomically realistic head model, implemented using 2-D geometry. In addition, heterogenic and frequency-dispersive behavior of the brain tissues has also been incorporated in our head models. It is identified during this study that the microwave scattering phenomenon changes significantly once the complexity of head model is increased by incorporating more details using magnetic resonance imaging database. It is also found out that the microwave scattering results match in both types of head model (i.e., geometrically simple and anatomically realistic), once the measurements are made in the structurally simplified regions. However, the results diverge considerably in the complex areas of brain due to the arbitrary shape interface of tissue layers in the anatomically realistic head model.After incorporating various levels of detail, the solution of subject microwave scattering problem and the measurement of transmitted and backscattered signals were obtained using finite element method. Mesh convergence analysis was also performed to achieve error free results with a minimum number of mesh elements and a lesser degree of freedom in the fast computational time. The results were promising and the E-Field values converged for both simple and complex geometrical models. However, the E-Field difference between both types of head model at the same reference point differentiated a lot in terms of magnitude. At complex location, a high difference value of 0.04236 V/m was measured compared to the simple location, where it turned out to be 0.00197 V/m. This study also contributes to provide a comparison analysis between the direct and iterative solvers so as to find out the solution of subject microwave scattering problem in a minimum computational time along with memory resources requirement.It is seen from this study that the microwave imaging may effectively be utilized for the detection, localization and differentiation of different types of brain stroke. The simulation results verified that the microwave imaging can be efficiently exploited to study the significant contrast between electric field values of the normal and abnormal brain tissues for the investigation of brain anomalies. In the end, a specific absorption rate analysis was carried out to compare the ionizing effects of microwave signals to different types of head model using a factor of safety for brain tissues. It is also suggested after careful study of various inversion methods in practice for microwave head imaging, that the contrast source inversion method may be more suitable and computationally efficient for such problems.

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Feasibility of brain stroke imaging with microwaves

Cited by 13 publications

References 9 publications

Active Sensor for Microwave Tissue Imaging with Bias-Switched Arrays

Active Sensor for Microwave Tissue Imaging with Bias-Switched Arrays

Finite-element analysis of microwave scattering from a three-dimensional human head model for brain stroke detection

Levels of detail analysis of microwave scattering from human head models for brain stroke detection

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