A Modified Exact Reconstruction Algorithm to Determine the Complex Permittivity Perturbations of a Cancer Affected Biological Target using Microwave Tomography Technique

Microwave Tomography Technique (MTT) is an emerging technology that is showing its effectiveness in detecting Cancer at early stage. Due to absolute random and non-deterministic characteristics of Cancer cells, more advancements are required in MTT to accurately detect the presence as well as the location of the affected region. Considering this fundamental issue, in this paper, we have proposed a modified Exact Reconstruction Algorithm (mERA) which is capable enough to provide a detailed analysis of all kinds of complex dielectric perturbations of a cancer affected biological target. In MTT, the detection of presence of a cancerous tumor inside any organ of human body has been done using different image reconstruction algorithms. On the other hand, this algorithm uses a selective data segregation mechanism to generate the perturbed complex cell permittivities of the affected organ tissues. Through this study, it has also been verified that how efficiently our proposed approach can able to detect all types of dielectric variations that may be large (20%), small (5%), positive or may be negative and even in a mixed kind of scenario where affected cells possess the mixture of all types of perturbations simultaneously. As cancerous cell shows peculiar behaviour inside human body and its nature varies from person to person and even in-between different stages (stage 1, stage 2, stage 3, stage 4) of cancer, the algorithm is designed in such a fashion that it can able to detect the presence of tumor considering all such possibilities into account. The results validate its high accuracy and effectiveness in the field of cancer diagnosis.

Methods of subsurface microwave tomography with application of sensors based on pieces of twin-wire line.

In this paper, methods of near-field microwave tomography of the subsurface structure of dielectric inhomogeneities are proposed and studied based on the use of resonance probes with pieces of twin-wire lines as sensors. In frameworks of the quasi-static approximation, the integral equation of the inverse problem that relates measured variations of the complex capacity of the resonance system of probes placed above a medium with the inhomogeneous distribution of the complex permittivity. Based on this equation, methods and algorithms of tomography and holography have been proposed and worked out that used data of 2D scanning with variable offset between the sensor wires: (a) with the fixed direction of wires of sensor; (b) in two orthogonally related directions of sensor wires; (c) with the sensor of crossed twin-wire lines. Results of the numerical simulation demonstrate the efficiency of developed algorithms of subsurface tomography and holography.

Affective Colormap Design for Accurate Visual Comprehension in Industrial Tomography

The design of colormaps can help tomography operators obtain accurate visual comprehension, thereby assisting safety-critical decisions. The research presented here is about deploying colormaps that promote the best affective responses for industrial microwave tomography (MWT). To answer the two research questions related to our study, we firstly conducted a quantitative analysis of 11 frequently-used colormaps on a segmentation task. Secondly, we presented the same colormaps within a crowdsourced study comprising two parts to verify the quantitative outcomes. The first part encoded affective responses from participants into a prevailing four-quadrant valence–arousal grid; the second part recorded participant ratings towards the accuracy of each colormap on MWT segmentation. We concluded that three colormaps are the best suited in the context of MWT tasks. We also found that the colormaps triggering emotions in the positive–exciting quadrant can facilitate more accurate visual comprehension than other affect-related quadrants. A synthetic colormap design guideline was consequently proposed.

A numerical feasibility study on monitoring bone health using microwave tomography: towards a wearable design

Background: Osteoporosis is the major cause of bone weakness and fragility in more than 10 million people in the United States. This disease causes bone fractures in the hip or spine, which result in increasing the risk of disabilities or even death. The current gold standard in osteoporosis diagnostics, X-ray, although reliable, it uses ionizing radiations that makes it unfeasible for early and continuous monitoring applications. Recently, microwave tomography (MWT) has been emerging as a biomedical imaging modality that utilizes non-ionizing electromagnetic signals to screen bones' electrical properties. These properties are highly correlated to bones' density, which makes MWT to be an effective and safe alternative for frequent testing in osteoporosis diagnostics. Results: Both of the conventional and wearable simulated systems were successful in localizing the tibia and fibula bones in the enhanced MWT images. Furthermore, structure extraction of the leg’s model from the blind MWT images had a minimal error compared to the original one (L2-norm: 15.60%). Under five sequentially-incremental bone volume fraction (BVF) scenarios simulating bones' treatment procedure, bones were detected successfully and their density were found to be inversely proportional to the real-part of the relative permittivity values. Conclusions: This study paves the way towards implementing a safe and user-friendly MWT system that can be wearable to monitor bone degradation or treatment for osteoporosis cases. Methods: An anatomically-realistic finite-element (FE) model representing the human leg was initially generated and filled with corresponding tissues' (skin, fat, muscles, and bones) dielectric properties. Then, numerically, the forward and inverse MWT problems were solved within the framework of the finite-element method contrast source inversion algorithm (FEM-CSI). Furthermore, image reconstruction enhancements were investigated by utilizing prior information about different tissues as an inhomogeneous background as well as by adjusting the imaging domain and antennas locations based on the prior structural information. In addition, the utilization of a medically-approved matching medium that can be used in wearable applications, namely an ultrasound gel, was suggested. Additionally, an approach based on k-means clustering was developed to extract the prior structural information from blind reconstructions. Lastly. the enhanced images were used to monitor variations in BVF.

An Investigation of Microwave Tomography Technique to Detect Brain Tumour Through Cross-Section Imaging at Frequency 0.5 GHz to 1.5 GHz

The growing significance of cancerous tissue including brain tumour requires a fast and efficient technology detection. The most current technologies being applied for brain imaging system are Computed Tomography (CT) scan and Magnetic Resonance Imaging (MRI). Whilst these two detection applications are very well established, both systems are expensive, time and space consuming, and raise safety issues to patients due to the radiation and strong magnetic effects. This research aims to assess the feasibility and potential performance of microwave tomography (MWT) for brain imaging with a particular focus on brain tumour detection. The study was conducted using Finite Element Model software, COMSOL Multiphysics to develop a 2D modelling of an antenna array and measure the scattered electric field by solving forward problem. MATLAB software will be used as an inverse problem solver to reconstruct 2D images of the tumour by using Linear Back Projection (LBP) algorithm.