A Robust and Artifact Resistant Algorithm of Ultrawideband Imaging System for Breast Cancer Detection.

Goal: Ultrawideband radar imaging is regarded as one of the most promising alternatives for breast cancer detection. A range of algorithms reported in literature show satisfactory tumor detection capabilities. However, most of algorithms suffer significant deterioration or even fail when the early-stage artifact, including incident signals and skin-fat interface reflections, cannot be perfectly removed from received signals. Furthermore, fibro-glandular tissue poses another challenge for tumor detection, due to the small dielectric contrast between glandular and cancerous tissues. Methods: This paper introduces a novel Robust and Artifact Resistant (RAR) algorithm, in which a neighborhood pairwise correlation-based weighting is designed to overcome the adverse effects from both artifact and glandular tissues. In RAR, backscattered signals are time-shifted, summed, and weighted by the maximum combination of the neighboring pairwise correlation coefficients between shifted signals, forming the intensity of each point within an imaging area. Results: The effectiveness was investigated using 3-D anatomically and dielectrically accurate finite-difference-time-domain numerical breast models. The use of neighborhood pairwise correlation provided robustness against artifact, and enabled the detection of multiple scatterers. RAR is compared with four well-known algorithms: delay-and-sum, delay-multiply-and-sum, modified-weighted-delay-and-sum, and filtered-delay-and-sum. Conclusion: It has shown that RAR exhibits improved identification capability, robust artifact resistance, and high detectability over its counterparts in most scenarios considered, while maintaining computational efficiency. Simulated tumors in both homogeneous and heterogonous, from mildly to moderately dense breast phantoms, combining an entropy-based artifact removal algorithm, were successfully identified and localized. Significance: These results show the strong potential of RAR for breast cancer screening

A novel approach to jointly address localization and classification of breast cancer using bio-inspired approach

Localization of the cancerous region as well as classification of the type of the cancer is highly inter-linked with each other. However, investigation towards existing approaches depicts that these problems are always iindividually solved where there is still a big research gap for a generalized solution towards addressing both the problems. Therefore, the proposed manuscript presents a simple, novel, and less-iterative computational model that jointly address the localization-classification problems taking the case study of early diagnosis of breast cancer. The proposed study harnesses the potential of simple bio-inspired optimization technique in order to obtained better local and global best outcome to confirm the accuracy of the outcome. The study outcome of the proposed system exhibits that proposed system offers higher accuracy and lower response time in contrast with other existing classifiers that are freqently witnessed in existing approaches of classification in medical image process

Design of a Multiband RF Slotted-Antenna for Biosensing Applications

There is an expanding demand for adaptive contactless label-free biosensors for point-of-care, multi-user, health risk-free applications. This paper introduces the design of an elliptically-slotted patch antenna (ESPA) for bio-sensing applications. The resonance frequency difference of the ESPA is 2.5% compared with the basic slot-less patch antenna of 6.6%. Hence, the proposed model compares with the conventional slot-less patch antenna and exhibited a vast improvement in its bandwidth efficiency by over 62%. The simulated ESPA design yields a total gain of 7.5 dBi and can be utilized for simultaneous bio-sample detection and signal transmission applications. The miniaturized size of this system promises a portable label-free, reliable, realtime detection with a cost-effective fabrication

Harmonic Detection and Selectively Focusing Electromagnetic Waves onto Nonlinear Targets using Time-Reversal

Ultra-wide band radar is a growing interest for the enhanced capability of ranging, imaging, and multipath propagation. An ultra-wide band pulse imposed on a system provides a near impulse like response and is, therefore, more descriptive than a conventional monotonic pulse. Combining pulse inversion with ultra-wide band DORT (a French acronym for the decomposition of the time reversal operator) is a technique which could be used to for greater visibility of nonlinear targets via harmonic detection in the presence of larger linear scatterers. Energy can be selectively focused onto nonlinear scatterers in complex, inhomogeneous environments. This thesis1, will expand upon previous work, and demonstrate nonlinear detection and selective focusing with pulse inversion combined with DORT

Radar-based breast cancer detection using a hemispherical antenna array - experimental results

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Multi-antenna multi-frequency microwave imaging systems for biomedical applications

Medical imaging refers to several different technologies that are used to view the human body in order to diagnose, monitor, or treat medical conditions. Each type of technology gives different information about the area of the body being studied depending on the radiation used to illuminate de body. Nowadays there are still several lesions that cannot be detected with the current methods in a curable stage of the disease. Moreover they present some drawbacks that limit its use, such as health risk, high price, patient discomfort, etc. In the last decades, active microwave imaging systems are being considered for the internal inspection of light-opaque materials thanks to its capacity to penetrate and differentiate their constituents based on the contrast in dielectric properties with a sub-centimeter resolution. Moreover, they are safe, relatively low-cost and portable. Driven by the promising precedents of microwaves in other fields, an active electromagnetic research branch was focused to medical microwave imaging. The potential in breast cancer detection, or even in the more challenging brain stroke detection application, were recently identified. Both applications will be treated in this Thesis. Intensive research in tomographic methods is now devoted to develop quantitative iterative algorithms based on optimizing schemes. These algorithms face a number of problems when dealing with experimental data due to noise, multi-path or modeling inaccuracies. Primarily focused in robustness, the tomographic algorithm developed and assessed in this thesis proposes a non-iterative and non-quantitative implementation based on a modified Born method. Taking as a reference the efficient, real-time and robust 2D circular tomographic method developed in our department in the late 80s, this thesis proposes a novel implementation providing an update to the current state-of-the-art. The two main contributions of this work are the 3D formulation and the multi-frequency extension, leading to the so-called Magnitude Combined (MC) Tomographic algorithm. First of all, 2D algorithms were only applicable to the reconstruction of objects that can be assumed uniform in the third dimension, such as forearms. For the rest of the cases, a 3D algorithm was required. Secondly, multi-frequency information tends to stabilize the reconstruction removing the frequency selective artifacts while maintaining the resolution of the higher frequency of the band. This thesis covers the formulation of the MC tomographic algorithm and its assessment with medically relevant scenarios in the framework of breast cancer and brain stroke detection. In the numerical validation, realistic models from magnetic resonances performed to real patients have been used. These models are currently the most realistic ones available to the scientific community. Special attention is devoted to the experimental validation, which constitutes the main challenge of the microwave imaging systems. For this reason, breast phantoms using mixtures of chemicals to mimic the dielectric properties of real tissues have been manufactured and an acquisition system to measure these phantoms has been created. The results show that the proposed algorithm is able to provide robust images of medically realistic scenarios and detect a malignant breast lesion and a brain hemorrhage, both at an initial stage

FPGA Acceleration of Domain-specific Kernels via High-Level Synthesis

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