Breast cancer detection in highly dense numerical breast phantoms using time reversal

In this paper we investigate the detection of breast cancer using two-dimensional slices of realistic numerical phantoms employing time reversal microwave imaging. We used maximum-likelihood estimation coupled with time reversal technique to detect and estimate the location of tumor using FDTD based breast phantoms that contain dense fibroglandular tissue clutter. We show that time reversal maximum-likelihood estimation can detect and accurately localize tumors even in highly dense breasts where the dielectric contrast between healthy dense breast tissue and cancerous lesions is quite low without requiring any contrast enhancing agents. © 2013 IEEE

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

Time Reversal Compressive Sensing MIMO Radar Systems

Active radar systems transmit a probing signal and use the return backscatters received from the channel to determine properties of the channel. After detecting the presence of targets, the localization of targets is achieved by estimating relevant target parameters, including the range, Doppler's frequency, and azimuth associated with the targets. A major source of error in parameter estimation is the presence of clutter (undesired targets) that also reflects the probing signal back to the radar. To eliminate the fading effect introduced by backscatters originating from the clutter, the multiple input multiple output (MIMO) radar transmits a set of simultaneous uncorrelated probing signals from the transmit elements comprising the transmit array. A major problem with MIMO radars is the large amount of data generated when the recorded backscatters are discretized at the Nyquist sampling rate. This in turn necessitates the need of expensive, high speed analog-to-digital converter circuits. Compressive sensing (CS) has emerged as a new sampling paradigm for reconstructing sparse signals with relatively few observations and at a lower computational cost compared to other sparsity promoting approaching. Although compressive beamforming has the potential of high resolution estimates, the approach has several limitations arising mainly due to the difficulty in achieving complete incoherency and sparsity in the CS dictionary. This PhD thesis will apply the principle of time reversal (TR) to MIMO radars to improve the incoherency and sparsity of the compressive beamforming dictionary. The resulting CS TR MIMO radar is analytically studied and assessed for performance gains as compared to the conventional MIMO systems

Towards Microwave Detection of Thromboses

Stroke is estimated to be the second most common cause of death with hugeburdens and costs for the patient and society. Since the treatment given to a stroke patient depends on the type of stroke they have, a fast and reliable diagnosis of the stroke type is needed before any treatment can be started. In general, the treatment is more effective the sooner it is started. Thrombectomy is an interventional treatment for patients with an occlusion (thrombosis) in a large artery that is only performed in a limited number of hospitals, thus early detection can support the pre-hospital decision-making process and help decreasing the time to treatment start. The aim of this work is to investigate and develop a method for pre-hospital diagnosis of ischemic stroke by using a microwave diagnosis setup and Contrast-Enhancement Agent (CEA). We propose to exploit the asymmetry created in the brain as a result of partial or full blockage of the arteries due to thromboses. This asymmetry is enhanced with the use of CEA and can be captured by the EM waves transmitted and received by the antennas on the head.The microwave diagnosis setup consists of several antennas placed on thebody. The multipath interference caused by the waves traveling on the surfaceof the body is a factor that limits the detection accuracy of this system. In the present study, a Dielectric Rod Antenna (DRA) is designed to address this challenge with a Self Grounded Bow-Tie Antenna (SGBTA) as the wave exciter. It was shown that DRA can reduce the surface wave power up to 10 dB in comparison with that of SGBTA while increasing its bandwidth by 72%.Preliminary results obtained from measurements on sheep are promising

Tumour micro-environment elicits innate resistance to RAF inhibitors through HGF secretion.

Drug resistance presents a challenge to the treatment of cancer patients. Many studies have focused on cell-autonomous mechanisms of drug resistance. By contrast, we proposed that the tumour micro-environment confers innate resistance to therapy. Here we developed a co-culture system to systematically assay the ability of 23 stromal cell types to influence the innate resistance of 45 cancer cell lines to 35 anticancer drugs. We found that stroma-mediated resistance is common, particularly to targeted agents. We characterized further the stroma-mediated resistance of BRAF-mutant melanoma to RAF inhibitors because most patients with this type of cancer show some degree of innate resistance. Proteomic analysis showed that stromal cell secretion of hepatocyte growth factor (HGF) resulted in activation of the HGF receptor MET, reactivation of the mitogen-activated protein kinase (MAPK) and phosphatidylinositol-3-OH kinase (PI(3)K)-AKT signalling pathways, and immediate resistance to RAF inhibition. Immunohistochemistry experiments confirmed stromal cell expression of HGF in patients with BRAF-mutant melanoma and showed a significant correlation between HGF expression by stromal cells and innate resistance to RAF inhibitor treatment. Dual inhibition of RAF and either HGF or MET resulted in reversal of drug resistance, suggesting RAF plus HGF or MET inhibitory combination therapy as a potential therapeutic strategy for BRAF-mutant melanoma. A similar resistance mechanism was uncovered in a subset of BRAF-mutant colorectal and glioblastoma cell lines. More generally, this study indicates that the systematic dissection of interactions between tumours and their micro-environment can uncover important mechanisms underlying drug resistance

On the performance of the time reversal SM-MIMO-UWB system on correlated channels

The impact of spatial correlation on the multi-input multi-output ultrawide band (MIMO-UWB) system using the time reversal (TR) technique is investigated. Thanks to TR, several data streams can be transmitted by using only one antenna in a system named virtual MIMO-TRUWB. Since the virtual MIMO-TR-UWB system is not affected by the transmit correlation, under the condition of the high spatial correlation, it outperforms the true MIMO-UWB system with multiple transmit antennas. The channel measurements are performed in short-range indoor environment, both line-of-sight and non-line-of-sight to verify the adopted correlated channel model.Vietnamese National Foundation for Science and Technology Development (NAFOSTED)/102.02.07.0

Microwave Breast Cancer Imaging: Simulation, Experimental Data, Reconstruction and Classification

This work concerns the microwave imaging (MWI) for breast cancer. The full process to develop an experimental phantom is detailed. The models used in the simulation stage are presented in an increasing complexity. Starting from a simplified homogeneous breast where only the tumor is placed in a background medium, moving to an intermediate complexity model where a rugged fibroglandular structure other than tumor has been placed and reaching a realistic breast model derived from the nuclear magnetic resonance phantoms. The reconstruction is performed in 2D using the linear TR-MUSIC algorithm tested in the monostatic and multistatic approaches. The description of the developed phantom and the instruments involved are detailed along with the already planned improvements. The simulated and experimental results are compared. Finally a classification stage based on the leading technique known as “deep learning”, an improved branch of the machine learning, is adopted using mammographic images

Biomedical Sensing and Imaging

This book mainly deals with recent advances in biomedical sensing and imaging. More recently, wearable/smart biosensors and devices, which facilitate diagnostics in a non-clinical setting, have become a hot topic. Combined with machine learning and artificial intelligence, they could revolutionize the biomedical diagnostic field. The aim of this book is to provide a research forum in biomedical sensing and imaging and extend the scientific frontier of this very important and significant biomedical endeavor

Embedded systems and advanced signal processing for Acousto- Ultrasonic Inspections

Non Destructive Testing (NDT) and Structural Health Monitoring (SHM) are becoming essential in many application contexts, e.g. civil, industrial, aerospace etc., to reduce structures maintenance costs and improve safety. Conventional inspection methods typically exploit bulky and expensive instruments and rely on highly demanding signal processing techniques. The pressing need to overcome these limitations is the common thread that guided the work presented in this Thesis. In the first part, a scalable, low-cost and multi-sensors smart sensor network is introduced. The capability of this technology to carry out accurate modal analysis on structures undergoing flexural vibrations has been validated by means of two experimental campaigns. Then, the suitability of low-cost piezoelectric disks in modal analysis has been demonstrated. To enable the use of this kind of sensing technology in such non conventional applications, ad hoc data merging algorithms have been developed. In the second part, instead, imaging algorithms for Lamb waves inspection (namely DMAS and DS-DMAS) have been implemented and validated. Results show that DMAS outperforms the canonical Delay and Sum (DAS) approach in terms of image resolution and contrast. Similarly, DS-DMAS can achieve better results than both DMAS and DAS by suppressing artefacts and noise. To exploit the full potential of these procedures, accurate group velocity estimations are required. Thus, novel wavefield analysis tools that can address the estimation of the dispersion curves from SLDV acquisitions have been investigated. An image segmentation technique (called DRLSE) was exploited in the k-space to draw out the wavenumber profile. The DRLSE method was compared with compressive sensing methods to extract the group and phase velocity information. The validation, performed on three different carbon fibre plates, showed that the proposed solutions can accurately determine the wavenumber and velocities in polar coordinates at multiple excitation frequencies