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

Advanced ultrawideband imaging algorithms for breast cancer detection

Ultrawideband (UWB) technology has received considerable attention in recent years as it is regarded to be able to revolutionise a wide range of applications. UWB imaging for breast cancer detection is particularly promising due to its appealing capabilities and advantages over existing techniques, which can serve as an early-stage screening tool, thereby saving millions of lives. Although a lot of progress has been made, several challenges still need to be overcome before it can be applied in practice. These challenges include accurate signal propagation modelling and breast phantom construction, artefact resistant imaging algorithms in realistic breast models, and low-complexity implementations. Under this context, novel solutions are proposed in this thesis to address these key bottlenecks. The thesis first proposes a versatile electromagnetic computational engine (VECE) for simulating the interaction between UWB signals and breast tissues. VECE provides the first implementation of its kind combining auxiliary differential equations (ADE) and convolutional perfectly matched layer (CPML) for describing Debye dispersive medium, and truncating computational domain, respectively. High accuracy and improved computational and memory storage efficiency are offered by VECE, which are validated via extensive analysis and simulations. VECE integrates the state-of-the-art realistic breast phantoms, enabling the modelling of signal propagation and evaluation of imaging algorithms. To mitigate the severe interference of artefacts in UWB breast cancer imaging, a robust and artefact resistant (RAR) algorithm based on neighbourhood pairwise correlation is proposed. RAR is fully investigated and evaluated in a variety of scenarios, and compared with four well-known algorithms. It has been shown to achieve improved tumour detection and robust artefact resistance over its counterparts in most cases, while maintaining high computational efficiency. Simulated tumours in both homogeneous and heterogeneous breast phantoms with mild to moderate densities, combined with an entropy-based artefact removal algorithm, are successfully identified and localised. To further improve the performance of algorithms, diverse and dynamic correlation weighting factors are investigated. Two new algorithms, local coherence exploration (LCE) and dynamic neighbourhood pairwise correlation (DNPC), are presented, which offer improved clutter suppression and image resolution. Moreover, a multiple spatial diversity (MSD) algorithm, which explores and exploits the richness of signals among different transmitter and receiver pairs, is proposed. It is shown to achieve enhanced tumour detection even in severely dense breasts. Finally, two accelerated image reconstruction mechanisms referred to as redundancy elimination (RE) and annulus predication (AP) are proposed. RE removes a huge number of repetitive operations, whereas AP employs a novel annulus prediction to calculate millions of time delays in a highly efficient batch mode. Their efficacy is demonstrated by extensive analysis and simulations. Compared with the non-accelerated method, RE increases the computation speed by two-fold without any performance loss, whereas AP can be 45 times faster with negligible performance degradation

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

Cancer Detection Using Advanced UWB Microwave Technology

Medical diagnosis and subsequent treatment efficacy hinge on innovative imaging modalities. Among these, Microwave Imaging (MWI) has emerged as a compelling approach, offering safe and cost-efficient visualization of the human body. This comprehensive research explores the potential of the Huygens principle-based microwave imaging algorithm, specifically focusing on its prowess in cancer, lesion, and infection detection. Extensive experimentation employing meticulously crafted phantoms validates the algorithm’s robustness. In the context of lung infections, this study harnesses the power of Huygens-based microwave imaging to detect lung-COVID-19 infections. Employing Microstrip and horn antennas within a frequency range of 1 to 5 GHz and a multi-bistatic setup in an anechoic chamber, the research utilizes phantoms mimicking human torso dimensions and dielectric properties. Notably, the study achieves a remarkable detection capability, attaining a signal-to-clutter ratio of 7 dB during image reconstruction using S21 signals.A higher SCR ratio indicates better contrast and clarity of the detected inclusion, which is essential for reliable medical imaging. It is noteworthy that this achievement is realized in free space without necessitating coupling liquid, underscoring the algorithm’s practicality. Furthermore, the research delves into the validation of Huygens Principle (HP)-based microwave imaging in detecting intricate lung lesions. Utilizing a meticulously designed multi-layered phantom with characteristics closely mirroring human anatomy, the study spans frequency bands from 0.5 GHz to 3 GHz within an anechoic chamber. The outcomes are compelling, demonstrating consistent lesion detection within reconstructed images. Impressively, the signal-to-clutter ratio post-artifact removal surges to 13.4 dB, affirming the algorithm’s potential in elevating medical imaging precision. To propel the capabilities of MWI further, this research unveils a novel device: 3D microwave imaging rooted in Huygens principle. Leveraging MammoWave device’s capabilities, the study ventures into 3D image reconstruction. Dedicated phantoms housing 3D structured inclusions, each embodying distinct dielectric properties, serve as the experimental bedrock. Through an intricate interplay of data acquisition and processing, the study attains a laudable feat: seamless 3D visualization of inclusions across various z-axis planes, accompanied by minimal dimensional error not exceeding 7.5%. In a parallel exploration, spiral-like measurement configurations enter the spotlight. These configurations, meticulously tailored along the z-axis, yield promising results. The research unveils an innovative approach to reducing measurement time while safeguarding imaging fidelity. Notably, spiral-like measurements achieve a notable 50% reduction in measurement time, albeit with slight trade-offs. Signal-to-clutter ratios experience a modest reduction, and there is a minor increase in dimensional analysis error, which remains within the confines of 3.5%. The research findings serve as a testament to MWI’s efficacy across diverse medical domains. The success in lung infection and lesion detection underscores its potential impact on medical diagnostics. Moreover, the foray into 3D imaging and the strategic exploration of measurement configurations lay the foundation for future advancements in microwave imaging technologies. As a result, the outcomes of this research promise to reshape the landscape of accurate and efficient medical imaging modalities

Design and Optimization of a Slotted Monopole Antenna for Ultra-Wide Band Body Centric Imaging Applications

This paper presents a cost-efficient design, optimization and physical implementation of a compact slotted ultra-wideband (UWB) monopole antenna for body-centric imaging applications. The proposed antenna is initially modelled and designed with the aid of commercial software (CST-Microwave Studio). To ensure that the proposed design is meeting the required specifications with reduced design time, the parallel surrogate model-assisted hybrid differential evolution for antenna optimization (PSADEA) is proposed to optimize the design. Based on the best set of geometry parameters for the optimum antenna performance, the antenna prototype is realized on an FR-4 substrate and analyzed in terms of bandwidth, gain, efficiency, and radiation pattern with and without the tissue models. All measured results are found to be in good agreement with the simulated results. The antenna provides a good reflection coefficient (S1

Design of Miniaturized Antipodal Vivaldi Antennas and a Microwave Head Imaging System for the Detection of Blood Clots in the Brain

Traditional brain imaging modalities, for example, MRI, CT scan, X-ray, etc. can provide precise and high-resolution images of the brain for diagnosing lesions, tumors or clots inside the brain. However, these modalities require bulky and expensive test setups accessible only at specialized diagnostic centers, and hence may not be suitable or affordable to many patients. Furthermore, the inherent health risks limit the usability of these modalities for frequent monitoring. Microwave imaging is deemed a promising alternative due to its being cost-effective, portable, non-ionizing, non-intrusive. Therefore, this work aims to design an effective microwave head imaging system for the detection of blood clots inside the brain. Two miniaturized antipodal Vivaldi antenna designs are proposed which can provide wideband operation covering the low microwave frequency range (within 1 - 6 GHz) while having electrically small dimensions, directional radiation pattern with reasonable gain, and without requiring immersion in any matching/ coupling liquid. A head imaging system is presented which utilizes a quarter-head scanning approach, to reconstruct four images of the brain by scanning four quarters of the head, using the designed antipodal wideband Vivaldi antenna. A numerical brain model, with and without the presence of blood clot, is simulated using the proposed head-imaging system. At each quarter, the antenna is placed at nine different positions for scanning. The reflected signal at each position is processed and using confocal microwave imaging technique four images of the brain are reconstructed. A comparison is made among the four images in terms of their intensities, for the detection and approximate location of the blood clot inside the brain. The presence of higher intensity regions in any specific quarter of the head demonstrates the presence of a clot and its location and validates the feasibility of the proposed head imaging system using the low frequency wideband Vivaldi antenna

Application-Specific Broadband Antennas for Microwave Medical Imaging

The goal of this work is the introduction of efficient antenna structures on the basis of the requirement of different microwave imaging methods; i.e. quantitative and qualitative microwave imaging techniques. Several criteria are proposed for the evaluation of single element antenna structures for application in microwave imaging systems. The performance of the proposed antennas are evaluated in simulation and measurement scenarios

Low Frequency Bio-Electrical Impedance Mammography and Dielectric Measurement

Assessment of electrical impedance of biological tissues at low frequencies offers a great potential for a safe, simple, and low-cost medical breast imaging techniques such as mammography. As such, in this dissertation a mammography method which uses tissue electrical impedance to detect breast malignancies was developed. The dissertation also introduces a new technique for measuring the dielectric properties of biological tissues at low frequencies. The impedance mammography technique introduced in this study is founded on the assumption that dielectric values of breast malignancies are significantly higher than the dielectric values of normal breast tissues. While previous studies have shown that this assumption is valid at high frequencies (50MHz-20GHz), less research efforts have been dedicated to ascertain the validity of such assumption at low frequencies (in silico and tissue mimicking phantom studies. Results of this investigation suggest that imaging the electrical impedance properties of biological tissues through the proposed electrical impedance mammography can be potentially employed for breast cancer detection in a reliable and safe manner

Detection of brain stroke in simulation and realistic 3-D human head phantom using microwave imaging

Brain stroke is globally one of the most widespread sorts of brain abnormalities. There are common symptoms between the transient ischemic attack (TIA), strokes and generic medical conditions like fainting, migraine, heart problems and seizures. Therefore, the other health conditions should not be misdiagnosed with stroke. It is well known that providing immediate medical attention for a patient with a brain injury is of vital importance. Every second, from the moment of brain injury, millions of brain cells die, leading to irreparable and permanent damage or even death. Thus, if medical staff diagnose stroke, and perform an appropriate drug treatment within a few hours of the symptoms onset, they play a crucial role in saving a patient’s life. The key factor in treatment is to reliably diagnose the stroke immediately. Hence, a portable diagnosic system is pivotal on the spot for rapid diagnosis of brain injuries. Initially, a clinical examination using a neurological assessment is performed by a general practitioner (GP). Compared to CT and MRI scanners, microwave imaging (MWI) can provide a portable detection system, and allow initial diagnosis of various emergency, life-threatening circumstances such as strokes due to brain injury, whilst patients are still being taken by ambulance to hospital, and saving critical time. In recent years, MWI has emerged as a promising non-ionising and non-invasive technology for a range of applications, particularly medical applications. In the current thesis, radar-based MWI is proposed as a procedure for brain haemorrhagic stroke detection. This imaging procedure has also more advantages such as low cost, being portable, fast, and easy to use with a good potential for brain haemorrhage detection. In MWI, the imaging of different human head tissues relies on their different response (i.e., electric contrast) to an applied microwave radiation. MWI is a screening technology for detection and monitoring of haemorrhagic stroke, tumours and cancerous cells, based on the significant contrast in the dielectric properties at microwave frequencies of normal and abnormal tissues. This thesis deals with the use and validation of an innovative low complexity MWI procedure for brain imaging, where antennas operate in free space. In particular, we employ only two microstrip antennas, operating between 1 and 2 GHz for successful detection of the haemorrhagic stroke. Detection is achieved using both simulation and experimental measurements. I. In the first stage, a wideband (WB) microstrip antenna with fractal ground plane is proposed, simulated and fabricated for brain haemorrhage detection. The designed antennas exhibit a WB working frequency between 1-2 GHz. This band has demonstrated to be ideal and optimal to do brain imaging; in addition, it is obviously emphasised that WB can enhance performance in lesion detection. The simulations have been performed applying an anthropomorphic human head model where a haemorrhagic stroke has been inserted (using CST Microwave studio). The simulation results concluded that the emulated brain haemorrhagic stroke can be distinguished at four different positions of 0◦, 5◦, 40◦, and 45◦. II. The second stage of this study presents a hemi-ellipsoidal human head phantom with a millimetric cylindrically-shaped inclusion to emulate brain haemorrhage (suitable to be used inside the anechoic chamber) and a human head phantom (suitable to be applied in MWI device). The process has been performed based on the following procedures: - In the second, stage, first, multi-biostatic frequency-domain measurements have been performed to collect the transfer function (S21) between two proposed mono-static radar system based antennas inside an anechoic chamber using a multi-layered phantom mimicking a human head. This procedure is used to measure the received signal (S21). A Vector Network Analyser (VNA) is linked to the mentioned antennas, and the measured (S21) are recorded when they changed the position to every new observation position. Subsequently, the measured (S21) are post-processed in order to generate microwave images with emphasising the object (e.g. the tumour or the stroke). In this stage, on the basis of the measurement results, it is concluded that the object (brain haemorrhagic stroke phantom) can be successfully detected at four different positions of 0◦, 90◦, 180◦ and 270◦. - Secondly, since the results coming from measurements inside the anechoic chamber are not as realistic as clinical trials reports and also there is a medical requirement for a brain stroke portable imaging device, we have come to a decision on applying different signal pre-processing methods to the imaging results collected from a portable MWI device for brain haemorrhage imaging. A portable MWI device, which operates in free space with two azimuthally-rotating antennas, has been used for brain haemorrhage detection. Measurements are performed by recording the complex (S21) in a multi-bistatic fashion, i.e. for each transmitting position the receiving antenna is moved to measure the received signal every 4.5◦, leading to a total of 80 receiving points. In conclusion, based on the results of the MWI device, the inclusion emulating the brain haemorrhage may be detected at four different positions of 0◦, 90◦, 180◦ and 270◦. In this thesis, all images have been obtained through Huygens Principle (HP). To reconstruct the image, signal pre-processing techniques are used to reduce artefacts (which may be due to the direct fields and the fields reflected by the first layer). Subtraction artefact removal method between the data of a healthy head and the data of a head with stroke has been initially employed in simulation and measurements. Accordingly, an "Ideal" image would be generated using this artefact removal method to prove the concept of the technology. This would mean that the "Ideal" image performed as a reference for the comparison with the resulting image from using other artefact removal methods. It is important to point out that, for the purposes of real scenario, there is no possibility of applying this artefact removal method to medical imaging, where the ideal response is not calculated or known. Hence, in clinical trials this artefact removal method cannot be helpful. In addition to the subtraction artefact removal method, in this research, four more methods have been introduced and investigated. These methods consist of rotation subtraction, average subtraction, differential symmetric receiver type, and summed symmetric differential. The subtraction and rotation subtraction artefact removal methods have been used both in simulations and measurements. It has been verified that all artefact removal procedures allow detection. Subsequently, 6 dedicated image quantification procedures have been implemented in order to assess the detection capability. These procedures comprise area difference, centroid difference, signal-to-noise ratio, structural similarity index metric, image quality index, and signal-to-clutter ratio. Validation of the techniques through both simulation and experimental measurements have been performed and presented, illustrating the effectiveness of the methods