scholarly journals Experimental Validation of Microwave Tomography with the DBIM-TwIST Algorithm for Brain Stroke Detection and Classification

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 840 ◽  
Author(s):  
Olympia Karadima ◽  
Mohammed Rahman ◽  
Ioannis Sotiriou ◽  
Navid Ghavami ◽  
Pan Lu ◽  
...  
Keyword(s):  
Inverse Problem ◽  
Ischemic Stroke ◽  
Dielectric Properties ◽  
Experimental Validation ◽  
Initial Guess ◽  
Microwave Tomography ◽  
Iterative Shrinkage ◽  
Brain Stroke ◽  
Brain Stroke Detection ◽  
Iterative Shrinkage Thresholding

We present an initial experimental validation of a microwave tomography (MWT) prototype for brain stroke detection and classification using the distorted Born iterative method, two-step iterative shrinkage thresholding (DBIM-TwIST) algorithm. The validation study consists of first preparing and characterizing gel phantoms which mimic the structure and the dielectric properties of a simplified brain model with a haemorrhagic or ischemic stroke target. Then, we measure the S-parameters of the phantoms in our experimental prototype and process the scattered signals from 0.5 to 2.5 GHz using the DBIM-TwIST algorithm to estimate the dielectric properties of the reconstruction domain. Our results demonstrate that we are able to detect the stroke target in scenarios where the initial guess of the inverse problem is only an approximation of the true experimental phantom. Moreover, the prototype can differentiate between haemorrhagic and ischemic strokes based on the estimation of their dielectric properties.

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

2018 ◽  
Vol 5 (7) ◽  
pp. 180319
Author(s):  
Awais Munawar Qureshi ◽  
Zartasha Mustansar ◽  
Samah Mustafa
Keyword(s):  
Finite Element ◽  
Dielectric Properties ◽  
Electric Field ◽  
Three Dimensional ◽  
Human Head ◽  
Head Model ◽  
Different Types ◽  
Brain Stroke ◽  
Human Head Model ◽  
Brain Stroke Detection

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.

scholarly journals Brain Stroke Detection by Microwaves Using Prior Information from Clinical Databases

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Natalia Irishina ◽  
Aurora Torrente
Keyword(s):  
Breast Cancer ◽  
Dielectric Properties ◽  
Prior Information ◽  
Brain Mri ◽  
Brain Lesion ◽  
Screening Method ◽  
Clinical Databases ◽  
Structural Inversion ◽  
Brain Stroke ◽  
Brain Stroke Detection

Microwave tomographic imaging is an inexpensive, noninvasive modality of media dielectric properties reconstruction which can be utilized as a screening method in clinical applications such as breast cancer and brain stroke detection. For breast cancer detection, the iterative algorithm of structural inversion with level sets provides well-defined boundaries and incorporates an intrinsic regularization, which permits to discover small lesions. However, in case of brain lesion, the inverse problem is much more difficult due to the skull, which causes low microwave penetration and highly noisy data. In addition, cerebral liquid has dielectric properties similar to those of blood, which makes the inversion more complicated. Nevertheless, the contrast in the conductivity and permittivity values in this situation is significant due to blood high dielectric values compared to those of surrounding grey and white matter tissues. We show that using brain MRI images as prior information about brain's configuration, along with known brain dielectric properties, and the intrinsic regularization by structural inversion, allows successful and rapid stroke detection even in difficult cases. The method has been applied to 2D slices created from a database of 3D real MRI phantom images to effectively detect lesions larger than 2.5 × 10−2 m diameter.

Accelerated 3D blind separation of convolved mixtures based on the fast iterative shrinkage thresholding algorithm for adaptive multiple subtraction

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. V99-V113 ◽  
Author(s):  
Zhong-Xiao Li ◽  
Zhen-Chun Li
Keyword(s):  
Field Data ◽  
Optimization Problem ◽  
Computational Cost ◽  
Computation Time ◽  
Blind Separation ◽  
L1 Norm ◽  
Norm Minimization ◽  
Iterative Shrinkage ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

After multiple prediction, adaptive multiple subtraction is essential for the success of multiple removal. The 3D blind separation of convolved mixtures (3D BSCM) method, which is effective in conducting adaptive multiple subtraction, needs to solve an optimization problem containing L1-norm minimization constraints on primaries by the iterative reweighted least-squares (IRLS) algorithm. The 3D BSCM method can better separate primaries and multiples than the 1D/2D BSCM method and the method with energy minimization constraints on primaries. However, the 3D BSCM method has high computational cost because the IRLS algorithm achieves nonquadratic optimization with an LS optimization problem solved in each iteration. In general, it is good to have a faster 3D BSCM method. To improve the adaptability of field data processing, the fast iterative shrinkage thresholding algorithm (FISTA) is introduced into the 3D BSCM method. The proximity operator of FISTA can solve the L1-norm minimization problem efficiently. We demonstrate that our FISTA-based 3D BSCM method achieves similar accuracy of estimating primaries as that of the reference IRLS-based 3D BSCM method. Furthermore, our FISTA-based 3D BSCM method reduces computation time by approximately 60% compared with the reference IRLS-based 3D BSCM method in the synthetic and field data examples.

scholarly journals Fast Algorithms for the Anisotropic LLT Model in Image Denoising

2011 ◽  
Vol 1 (3) ◽  
pp. 264-283 ◽  
Author(s):  
Zhi-Feng Pang ◽  
Li-Lian Wang ◽  
Yu-Fei Yang
Keyword(s):  
Convergence Rate ◽  
Image Denoising ◽  
Projection Method ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Minimization Problems ◽  
Iterative Shrinkage ◽  
Speed Up ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

AbstractIn this paper, we propose a new projection method for solving a general minimization problems with twoL1-regularization terms for image denoising. It is related to the split Bregman method, but it avoids solving PDEs in the iteration. We employ the fast iterative shrinkage-thresholding algorithm (FISTA) to speed up the proposed method to a convergence rateO(k−2). We also show the convergence of the algorithms. Finally, we apply the methods to the anisotropic Lysaker, Lundervold and Tai (LLT) model and demonstrate their efficiency.

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