Microwave Imaging for Diagnostic Application

Imaging of the human body makes a significant contribution to the diagnosis and succeeding treatment of diseases. Among the numerous medical imaging methods, microwave imaging (MWI) is an attractive approach for medical applications due to its high potential to produce images of the human body safely with cost-efficiency. A wide range of studies and research has been done with the aim of using the microwave approach for medical applications. The focus of this research is developing MWI algorithms, which is the Huygens Principle (HP) based and to validate the capability of the proposed MWI algorithm to detect skin cancer and bone lesion through phantom measurements. The probability of the HP procedure for skin cancer detection has been investigated through design, and fabrication of a heterogeneous phantom simulating the human forearm having an inclusion mimicking a skin cancer. Ultrawideband (UWB) MWI methods are then applied to the phantom. The S21 parameter measurements are collected in an anechoic chamber environment and processed via HP technique. The tumour is successfully detected after applying appropriate artefact removal procedure. The ability to successfully apply HP to detect and locate a skin cancer type inclusion in a multilayer cylindrical phantom has been verified. The feasibility study of HP-based MWI procedure for bone lesion detection has also been investigated using a dedicated phantom. Validation has been completed through measurements inside the anechoic chamber in the frequency range of 1–3 GHz using one receiving and one transmitting antennas in free space. The identification of the lesion’s presence in different bone layers has been performed on images. The quantification of the obtained images has been performed by introducing parameters such as the resolution and signal-to-clutter ratio (S/C). The impact of different frequencies and bandwidths (in the 1–3 GHz range) in lesion detection has been investigated. The findings showed that the frequency range of 1.5–2.5 GHz offered the best resolution (1.1 cm) and S/C (2.22 on a linear scale). Subtraction between S21 obtained using two slightly displaced transmitting positions has been employed to remove the artefacts; the best artefact removal has been obtained when the spatial displacement was approximately of the same magnitude as the dimension of the lesion. Subsequently, a phantom validation of a low complexity MWI device (based on HP) operating in free space in the 1-6.5 GHz frequency band using two antennas in free space has been applied. Detection has been achieved in both bone fracture lesion and bone marrow lesion scenarios using superimposition of five doublet transmitting positions after applying the rotation subtraction method to remove artefact. A resolution of 5 mm and the S/C (3.35 in linear scale) are achieved which is clearly confirming the advantage of employing multiple transmitting positions on increased detection capability. The finding of this research verifies the dedicated MWI device as a simple, safe and without any X-ray radiation, portable, and low complexity method, which is capable of been successfully used for bone lesion detection. The outcomes of this thesis may pave the way for the construction of a dedicated bone imaging system that in future could be used as a safe diagnostic device even in emergency sites

Microwave imaging for ultra-wideband antenna based cancer detection

Breast cancer is one of the most widespread types of cancer in the world. The key factor in treatment is to reliably diagnose the cancer in the early stages. Moreover, currently used clinical diagnostic methods, such as X-ray, ultra-sound and MRI, are limited by cost and reliability issues. These limitations have motivated researchers to develop a more effective, low-cost diagnostic method and involving lower ionization for cancer detection. In this thesis, radar based microwave imaging is proposed as a method for early breast cancer detection. This imaging system has advantages such as low cost, being non- invasive and easy to use, with high image resolution and its thus good potential for early cancer detection. In the first stage, an ultra-wideband Vivaldi antenna and a slot Vivaldi antenna are proposed, simulated and fabricated for breast cancer detection. The designed antennas exhibit an ultra-wideband working frequency. The radiation patterns also achieve the desired directional radiation patterns. The second stage of this study presents a planar breast phantom and a hemisphere breast phantom. These two breast phantoms are simulated and fabricated using CST microwave studio and tissue-mimicking materials respectively. Mono-static radar systems based on a single antenna configuration and an antenna pair configuration are then proposed. These two systems are used to measure the planar breast phantom and hemi- sphere breast phantom, with the scattering signals measured in the frequency and time domains. Based on the measurement results, it is concluded that the reflected energy increases when the antenna moves close to the tumour; otherwise, the reflected energy is reduced when the antenna moves away from the tumour. The received time domain scattering signals are processed first and then used to create microwave images to indicate tumour position. A clutter removal method is proposed to extract the tumour response from the received signals. The microwave images are then created using the tumour response based on the simulation and experimental results. The imaging results indicate that a 5 mm radius tumour can be detected. The tumour burial depth is also studied. A multi bio- layer phantom which contains deep and shallow buried tumours is simulated and measured using the Vivaldi antenna. A spectrum analysis method is proposed to distinguish between different tumour depths. The results indicate that a difference in depth of 15 mm results in a mean change of 0.3 dB in the magnitude of the spectrum. Discrimination between benign and malignant tumours is also considered in this study. The singularity expansion method (SEM) for breast cancer is proposed to discriminate between benign and malignant tumours based on their morphology. Two cancerous breast phantoms are developed in CST. The benign tumour is a 5mm radius sphere and the malignant tumour is a spiny sphere with an average radius of 5mm. The use of the SEM leads to the successful discrimination of these two tumours. This method provides a solution to discriminate between benign and malignant tumours similar size when the resulting images cannot provide sufficient resolution. A preliminary study of brain cancer detection is also concluded. Research in this area has never been implemented. A cancerous brain model is designed and simulated in CST. The antenna pair configuration is then used to measure the cancerous brain, with the scattering signals measured. Microwave images for brain cancer detection are then created based on the measurement results. The tumour is correctly indicated in the resulting images

Classifying Breast Tumors using Medical Microwave Radar Imaging

Medical Microwave Imaging (MMI) has been studied in the past years to develop techniques to detect breast cancer at the earliest stages of development. Particularly, ultra-wideband (UWB) micro-wave radar imaging systems can detect and classify tumors as benign or malignant since this technique yields information about the size and shape of tumors. In this study we used this technology to classify tumors. The primary goal of this dissertation is two-folded. First, producing breast tumor numerical mod-els and using them in 2D MMI simulations that recreate the conditions of a UWB microwave radar imaging system. The breast tumor numerical produced resemble real tumor morphologies since they are made from breast MRI exams segmentations. Second, the data of the backscattered UWB microwave signals produced by the MMI simulations was used to classify tumors according to their size and histol-ogy, which is relevant to assess potential of UWB microwave radar imaging systems as a reliable alter-native method for the classification of breast tumors in the field of Medical Microwave Imaging. The Classification Algorithms used in this work were Pseudo Linear Discriminant Analysis (Pseudo-LDA), Pseudo Quadratic Discriminant Analysis (pseudo-QDA), and k-Nearest Neighbors (KNN), alongside with a feature extraction algorithm – Principal Component Analysis (PCA).A Imagem Médica por Microondas (do inglês, MMI) tem sido estudada nos últimos anos de forma a desenvolver técnicas de deteção do cancro da mama nas primeiras fases de desenvolvimento. Em particular, os sistemas de imagem de radar por microondas em banda ultralarga (do inglês UWB) podem detetar e classificar os tumores como benignos ou malignos, uma vez que esta técnica produz informação sobre o tamanho e a forma dos tumores. Neste estudo, utilizámos esta tecnologia para classificar os tumores. A dissertação tem dois objetivos principais. Primeiro, produzir fantomas de tumores mamários e utilizá-los em simulações de MMI em 2D que recriam as condições de um sistema de imagem de radar por microondas UWB. Os fantomas numéricos de tumores mamários produzidos possuem morfologias semelhantes a tumores reais, uma vez que são feitos a partir de segmentações de exames de ressonância magnética da mama. Em segundo lugar, as reflexões dos sinais de microondas UWB produzidos pelas simulações de MMI foram utilizados para classificar tumores de acordo com o seu tamanho e histologia, o que é relevante para avaliar o potencial dos sistemas de imagem de radar por microondas UWB como um método alternativo e fiável para a classificação de tumores mamários no campo da MMI. Os Algo-ritmos de Classificação utilizados neste trabalho foram a Pseudo Linear Discriminant Analysis (Pseudo-LDA), Pseudo Quadratic Discriminant Analysis (pseudo-QDA), e a K-Nearest Neighbors (KNN), jun-tamente com um algoritmo de extração de features - Análise de Componentes Principais (do inglês PCA)

Classificação de tumores de cancro na mama através de radar de banda ultra-larga de microondas

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaA presente dissertação foi desenvolvida com a colaboração do Instituto de Biofísica e Engenharia Biomédica da Faculdade de Ciências da Universidade de Lisboa.A imagem por microondas é uma das técnicas mais promissoras de imagem médica para detecção e classificação do cancro da mama, explorando as diferenças das propriedades dieléctricas entre tecidos da mama e massas cancerígenas quando sujeitas a frequências microondas. O radar de banda ultra-larga de microondas baseia-se na iluminação da mama com um pulso de banda ultra-larga gravando o sinal resultante por retrodispersão. Esta técnica tem um grande potencial por ser relativamente barata, não invasiva, confortável para o paciente e utilizar radiação não-ionizante. A técnica de imagem por microondas tem sido estudada via simulação, sendo que os primeiros protótipos estão agora a ser construídos. Nesta dissertação é estudado o potencial do radar de banda ultra-larga de microondas para classificação de tumores baseada no tamanho e/ou na forma do tumor. Numa primeira parte foram estudados tumores numéricos, construídos com base em Gaussian Random Sphere, em simulações, e numa segunda parte foram estudados fantômas físicos de tumores com o uso de um protótipo. Três classificadores foram utilizados: Análise Discriminante Linear, Análise Discriminante Quadrática e Support Vector Machines. Várias arquitecturas foram estudadas, combinando a classificação por tamanho e depois por forma e vice-versa. Foram estudados cenários de fantômas da mama homogéneos e heterogéneos

Reconstruction of Microwave Imaging using Machine Learning

Tese de mestrado, Engenharia Biomédica e Biofísica, 2022, Universidade de Lisboa, Faculdade de CiênciasBreast cancer is the most diagnosed cancer in women. The gold standard technique for mass screening is X-ray mammography, which requires the use of ionising radiation. Mammography has a high false positive rate for women under 50, since the technique is highly sensitive to breast density. Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) and Ultrasound Imaging (US) have been suggested as complementary imaging tools to lessen the false positive results; however present some disadvantages. The potential of using microwave signals for breast cancer detection and monitoring has been studied for over 20 years. Microwave Breast Imaging (MBI) is a low-cost, non-invasive and non-ionising technique. The reflected microwave signals are transformed into an image via beamforming algorithms. These images have limited resolution, which may result in a considerable high rate of false positives and false negatives. In this dissertation, a complementary method of image reconstruction using Machine Learning (ML) models to predict the healthy or tumorous nature of breast is proposed. To study the potential of the proposed method, microwave signals were collected with a monostatic radar-based microwave system. The signal was acquired from three breast phantoms: one mimicking a homogeneous breast and two mimicking heterogeneous breasts. The phantoms had a cavity to introduce a plug, which included types of tumour models in terms of malignancies. From the signals, portions with and without tumour signature were extracted to train classification models. The most robust models were used to reconstruct a binary image of the breast with values of “hit” for tumorous focal points, and values of “miss” for healthy focal points. Eventually, the reconstructed images resulting from the proposed method were compared with the images obtained using the traditional beamforming method, DAS. Overall, the results obtained with the method ML-based were satisfactory, since for most phantoms the regions classified as tumour, indeed corresponded to the real position of the tumour

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

Simulation and Design of an UWB Imaging System for Breast Cancer Detection

Breast cancer is the most frequently diagnosed cancer among women. In recent years, the mortality rate due to this disease is greatly decreased thanks to both enormous progress in cancer research, and screening campaigns which have allowed the increase in the number of early diagnoses of the disease. In fact, if the tumor is identied in its early stage, e.g. when it has a diameter of less than one centimeter, the possibility of a cure can reach 93%. However, statistics show that more young aged women are suered breast cancer. The goal of screening exams for early breast cancer detection is to nd cancers before they start to cause symptoms. Regular mass screening of all women at risk is a good option to achieve that. Instead of meeting very high diagnostic standards, it is expected to yield an early warning, not a denitive diagnosis. In the last decades, X-ray mammography is the most ecient screening technique. However, it uses ionizing radiation and, therefore, should not be used for frequent check-ups. Besides, it requires signicant breast compression, which is often painful. In this scenario many alternative technologies were developed to overcome the limitations of mammography. Among these possibilities, Magnetic Resonance Imaging (MRI) is too expensive and time-consuming, Ultrasound is considered to be too operatordependent and low specicity, which are not suitable for mass screening. Microwave imaging techniques, especially Ultra WideBand (UWB) radar imaging, is the most interesting one. The reason of this interest relies on the fact that microwaves are non-ionizing thus permitting frequent examinations. Moreover, it is potentially lowcost and more ecient for young women. Since it has been demonstrated in the literatures that the dielectric constants between cancerous and healthy tissues are quite dierent, the technique consists in illuminating these biological tissues with microwave radiations by one or more antennas and analyzing the re ected signals. An UWB imaging system consists of transmitters, receivers and antennas for the RF part, the transmission channel and of a digital backend imaging unit for processing the received signals. When an UWB pulse strikes the breast, the pulse is re ected due to the dielectric discontinuity in tissues, the bigger the dierence, the bigger the backscatter. The re ected signals are acquired and processed to create the energy maps. This thesis aims to develop an UWB system at high resolution for the detection of carcinoma breast already in its initial phase. To favor the adoption of this method in screening campaigns, it is necessary to replace the expensive and bulky RF instrumentation used so far with ad-hoc designed circuits and systems. In order to realize that, at the very beginning, the overall system environment must be built and veried, which mainly consists of the transmission channel{the breast model and the imaging unit. The used transmission channel data come from MRI of the prone patient. In order to correctly use this numerical model, a simulator was built, which was implemented in Matlab, according to the Finite-Dierence-Time- Domain (FDTD) method. FDTD algorithm solves the electric and magnetic eld both in time and in space, thus, simulates the propagation of electromagnetic waves in the breast model. To better understand the eect of the system non-idealities, two 2D breast models are investigated, one is homogeneous, the other is heterogeneous. Moreover, the modeling takes into account all critical aspects, including stability and medium dispersion. Given the types of tissues under examination, the frequency dependence of tissue dielectric properties is incorporated into wideband FDTD simulations using Debye dispersion parameters. A performed further study is in the implementation of the boundary conditions. The Convolution Perfectly Matched Layer (CPML) is used to implement the absorbing boundaries. The objective of the imaging unit is to obtain an energy map representing the amount of energy re ected from each point of the breast, by recombining the sampled backscattered signals. For this purpose, the study has been carried out on various beamforming in the literature. The basic idea is called as "delay and sum", which is to align the received signals in such a way as to focus a given point in space and then add up all the contributions, so as to obtain a constructive interference at that point if this is a diseased tissue. In this work, Microwave Imaging via Space Time (MIST) Beamforming algorithm is applied, which is based on the above principle and add more elaborations of the signals in order to make the algorithm less sensitive to propagation phenomena in the medium and to the non-idealities of the system. It is divided into two distinct steps: the rst step, called SKin Artifact Removal (SKAR), takes care of removing the contributions from the signal caused by the direct path between the transmitter and receiver, the re ection of skin, as they are orders of magnitude higher compared to the re ections caused by cancers; the second step, which is BEAmForming (BEAF), performs the algorithm of reconstruction by forming a weighted combination of time delayed version of the calibrated re ected signals. As discussed above, more attention must be paid on the implementation of the ad-hoc integration circuits. In this scenario, due to the strict requirements on the RF receiver component, two dierent approaches of the implementation of the RF front-end, Direct Conversion (DC) receiver and Coherent Equivalent Time Sampling (CETS) receiver are compared. They are modeled behaviorally and the eects of various impairments, such as thermal, jitter, and phase noise, as well as phase inaccuracies, non-linearity, ADC quantization noise and distortion, on energy maps and on quantitative metrics such as SCR and SMR are evaluated. Dierential Gaussian pulse is chosen as the exciting source. Results show that DC receiver performs higher sensitivity to phase inaccuracies, which makes it less robust than the CETS receiver. Another advantage of the CETS receiver is that it can work in time domain with UWB pulses, other than in frequency domain with stepped frequency continuous waves like the DC one, which reduces the acquisition time without impacting the performance. Based on the results of the behavioral simulations, low noise amplier (LNA) and Track and Hold Amplier (THA) can be regarded as the most critical parts for the proposed CETS receiver, as well as the UWB antenna. This work therefore focuses on their hardware implementations. The LNA, which shows critical performance limitation at bandwidth and noise gure of receiver, has been developed based on common-gate conguration. And the THA based on Switched Source Follower (SSF) scheme has been presented and improved to obtain high input bandwidth, high sampling rate, high linearity and low power consumption. LNA and THA are implemented in CMOS 130nm technology and the circuit performance evaluation has been taken place separately and together. The small size UWB wide-slot antenna is designed and simulated in HFSS. Finally, in order to evaluate the eect of the implemented transistor level components on system performance, a multi-resolution top-down system methodology is applied. Therfore, the entire ow is analyzed for dierent levels of the RF frontend. Initially the system components are described behaviorally as ideal elements. The main activity consists in the analysis and development of the entire frontend system, observing and complementing each other blocks in a single ow simulation, clear and well-dened in its various interfaces. To achieve that the receiver is modeled and analyzed using VHDL-AMS language block by block, moreover, the impact of quantization, noise, jitter, and non-linearity is also evaluated. At last, the behavioral description of antenna, LNA and THA is replaced with a circuit-level one without changing the rest of the system, which permits a system-level assessment of low-level issues

Effect of nutritional factors on the growth and production of biosurfactant by Pseudomonas aeruginosa strain 181

The growth and production of biosurfactant by P. seudomonas aeruginosa (181) was dependant on nutritional factors. Among the eleven carbon sources tested, glucose supported the maximum growth (0.25 g/L) with the highest biosurfactant yield and this was followed by glycerol. Glucose reduced the surface tension to 35.3 dyne/ cm and gave an E24 reading of 62.7%. Butanol gave the lowest growth and had no biosurfactant production. For the nitrogen sources tested, casamino acid supported a growth of 0.21 g/L which reduced the surface tension to 41.1 dyne/cm and gave an E24 reading of 56%. Soytone was assimilated similarly, with good growth and high biosurfactant production. Corn steep liquor gave the lowest growth and did not show any biosurfactant activity

Microwave Imaging to Improve Breast Cancer Diagnosis

Breast cancer is the most prevalent type of cancer worldwide. The correct diagnosis of Axillary Lymph Nodes (ALNs) is important for an accurate staging of breast cancer. The performance of current imaging modalities for both breast cancer detection and staging is still unsatisfactory. Microwave Imaging (MWI) has been studied to aid breast cancer diagnosis. This thesis addresses several novel aspects of the development of air-operated MWI systems for both breast cancer detection and staging. Firstly, refraction effects in air-operated setups are evaluated to understand whether refraction calculation should be included in image reconstruction algorithms. Then, the research completed towards the development of a MWI system to detect the ALNs is presented. Anthropomorphic numerical phantoms of the axillary region are created, and the dielectric properties of ALNs are estimated from Magnetic Resonance Imaging exams. The first pre-clinical MWI setup tailored to detect ALNs is numerically and experimentally tested. To complement MWI results, the feasibility of using machine learning algorithms to classify healthy and metastasised ALNs using microwave signals is analysed. Finally, an additional study towards breast cancer detection is presented by proposing a prototype which uses a focal system to focus the energy into the breast and decrease the coupling between antennas. The results show refraction calculation may be neglected in low to moderate permittivity media. Moreover, MWI has the potential as an imaging technique to assess ALN diagnosis as estimation of dielectric properties indicate there is sufficient contrast between healthy and metastasised ALNs, and the imaging results obtained in this thesis are promising for ALN detection. The performance of classification models shows these models may potentially give complementary information to imaging results. The proposed breast imaging prototype also shows promising results for breast cancer detection