Microwave radar imaging of inhomogeneous breast phantoms using circular holography

Circular holography is a novel reconstruction technique for Breast Microwave Radar (BMR) imaging. Compared to current state of the art BMR image formation methods, this reconstruction approach yields spatially accurate images with higher signal to noise ratios and no artifacts. Nevertheless, a preclinical study is required to assess the feasibility of this technique in realistic breast imaging scenarios. In this paper, a series of preliminary results showing the performance of circular holography on preclinical datasets are presented. These datasets were recorded from inhomogeneous breast phantoms that mimic the dielectric properties and the anatomy of the different breast tissues. These phantoms were fabricated using Magnetic Resonance (MR) images a base model to emulate the shape and volumes of dense tissue regions. The reconstructed BMR images show that tumor and fibroglandular tissue responses can be effectively distinguished, suggesting that circular holography can be used as BMR reconstruction approach in clinical scenarios

Incoherent Radar Imaging For Breast Cancer Detection and Experimental Validation Against 3d Multimodal Breast Phantoms

: In this paper we consider radar approaches for breast cancer detection. The aim is to give a brief review of the main features of incoherent methods, based on beam-forming and Multiple SIgnal Classification (MUSIC) algorithms, that we have recently developed, and to compare them with classical coherent beam-forming. Those methods have the remarkable advantage of not requiring antenna characterization/compensation, which can be problematic in view of the close (to the breast) proximity set-up usually employed in breast imaging. Moreover, we proceed to an experimental validation of one of the incoherent methods, i.e., the I-MUSIC, using the multimodal breast phantom we have previously developed. While in a previous paper we focused on the phantom manufacture and characterization, here we are mainly concerned with providing the detail of the reconstruction algorithm, in particular for a new multi-step clutter rejection method that was employed and only barely described. In this regard, this contribution can be considered as a completion of our previous study. The experiments against the phantom show promising results and highlight the crucial role played by the clutter rejection procedure

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

3D Huygens Principle based Microwave Imaging through MammoWave Device: Validation through Phantoms.

This work focuses on developing a 3D microwave imaging (MWI) algorithm based on the Huygens principle (HP). Specifically, a novel, fast MWI device (MammoWave) has been presented and exploited for its capabilities of extending image reconstruction from 2D to 3D. For this purpose, dedicated phantoms containing 3D structured inclusion have been prepared with mixtures having different dielectric properties. Phantom measurements have been performed at multiple planes along the z-axis by simultaneously changing the transmitter and receiver antenna height via the graphic user interface (GUI) integrated with MammoWave. We have recorded the complex S21 multi-quote data at multiple planes along the z-axis. The complex multidimensional raw data has been processed via an enhanced HP-based image algorithm for 3D image reconstruction. This paper demonstrates the successful detection and 3D visualization of the inclusion with varying dimensions at multiple planes/cross-sections along the z-axis with a dimensional error lower than 7.5%. Moreover, the paper shows successful detection and 3D visualization of the inclusion in a skull-mimicking phantom having a cylindrically shaped inclusion, with the location of the detected inclusion in agreement with the experimental setup. Additionally, the localization of a 3D structured spherical inclusion has been shown in a more complex scenario using a 3-layer cylindrically shaped phantom, along with the corresponding 3D image reconstruction and visualization

Fast Microwave Tomography Algorithm for Breast Cancer Imaging

Microwave tomography has shown promise for breast cancer imaging. The microwaves are harmless to body tissues, which makes microwave tomography a safe adjuvant screening to mammography. Although many clinical studies have shown the effectiveness of regular screening for the detection of breast cancer, the anatomy of the breast and its critical tissues challenge the identification and diagnosis of tumors in this region. Detection of tumors in the breast is more challenging in heterogeneously dense and extremely dense breasts, and microwave tomography has the potential to be effective in such cases. The sensitivity of microwaves to various breast tissues and the comfort and safety of the screening method have made microwave tomography an attractive imaging technique. Despite the need for an alternative screening technique, microwave tomography has not yet been introduced as a screening modality in regular health care, and is still subject to research. The main obstacles are imperfect hardware systems and inefficient imaging algorithms. The immense computational costs for the image reconstruction algorithm present a crucial challenge. 2D imaging algorithms are proposed to reduce the amount of hardware resources required and the imaging time. Although 2D microwave tomography algorithms are computationally less expensive, few imaging groups have been successful in integrating the acquired 3D data into the 2D tomography algorithms for clinical applications. The microwave tomography algorithms include two main computation problems: the forward problem and the inverse problem. The first part of this thesis focuses on a new fast forward solver, the 2D discrete dipole approximation (DDA), which is formulated and modeled. The effect of frequency, sampling number, target size, and contrast on the accuracy of the solver are studied. Additionally, the 2D DDA time efficiency and computation time as a single forward solver are investigated.\ua0 The second part of this thesis focuses on the inverse problem. This portion of the algorithm is based on a log-magnitude and phase transformation optimization problem and is formulated as the Gauss-Newton iterative algorithm. The synthetic data from a finite-element-based solver (COMSOL Multiphysics) and the experimental data acquired from the breast imaging system at Chalmers University of Technology are used to evaluate the DDA-based image reconstruction algorithm. The investigations of modeling and computational complexity show that the 2D DDA is a fast and accurate forward solver that can be embedded in tomography algorithms to produce images in seconds. The successful development and implementation in this thesis of 2D tomographic breast imaging with acceptable accuracy and high computational cost efficiency has provided significant savings in time and in-use memory and is a dramatic improvement over previous implementations

Evaluating a breast tumor monitoring vest with flexible UWB antennas and realistic phantoms:a proof-of-concept study

Abstract. The introduction provides an overview of the global significance of breast cancer as a health concern and the limitations of existing breast cancer screening methods. It introduces the concept of microwave-based breast cancer monitoring and highlights the promising findings from a previous research paper. The objective of the master thesis is presented, which is to develop and evaluate a self-monitoring vest equipped with UWB antennas and channel analysis to overcome the limitations of current screening methods and enable regular breast cancer monitoring from home. The "Background and Literature Review," provides a comprehensive overview of the relevant topics related to microwave techniques for breast cancer detection. It starts by discussing the anatomy of the female breast, highlighting the importance of understanding its structure for effective tumor detection. The section then delves into the microwave properties of the human breast, elucidating the interactions between microwaves and breast tissue. The basic principle of microwave channel analysis is explained, emphasizing its significance in detecting breast tumors. Furthermore, the advantages of microwave-based tumor detection methods are explored, showcasing their potential for improved breast cancer screening. Various microwave techniques used in breast cancer detection, including microwave tomography and radar-based UWB microwave imaging, are discussed, along with different self-monitoring vests integrated with UWB antennas. This section serves as a foundation for the subsequent chapters of the thesis, providing a comprehensive background and literature review to support the research and development of the practical self-monitoring vest for early detection of small-sized breast tumors. The "Preparation of Tissue Phantoms" section in the master’s thesis explores the comprehensive methodology for creating tissue phantoms that replicate the dielectric properties of various human tissues. While the section primarily focuses on fat tissue, it also acknowledges the existence of other phantom types. The outlined approach involves careful ingredient selection, formulation development, fabrication techniques, and stability evaluation for the creation of skin, muscle, fat, tumor, and gland tissue phantoms. By following these procedures, researchers can successfully produce tissue phantoms that closely mimic the properties of real human tissues. These phantoms serve as essential tools for investigating microwave-based applications in medical diagnostics and provide a reliable and versatile platform for further research in the field. The third section discusses the assembly of heterogeneous breast phantoms used for evaluating the performance of the tumor detection vest. The phantoms consisted of outer and inner molds, with the outer molds resembling the shape of a prone human breast. Two breast density types, representing very dense and less dense breasts, were used. For the dense breast phantoms, liquid fat material was solidified in the outer molds, and a glandular liquid was poured into the inner mold, with tumors inserted and covered with additional glandular liquid. For the less dense breast phantoms, fat liquid was solidified in the outer molds, and cylindrical glandular molds were inserted. A skin layer and muscle layer were added to complete the assembly, accurately simulating the composition and structure of a breast. This realistic breast phantom assembly allowed for accurate measurements and evaluation of the vest’s performance under different breast density conditions, contributing to breast imaging research and development. The "Monitoring Vest" section discusses the antennas used in the tumor detection vest and the design of two different vest versions. Antenna1 is a UWB monopole antenna with a flexible laminate substrate, while Antenna2 is a textile-based version of Antenna1. Antenna3 has a Kapton-based substrate and larger dimensions. The combination of these antennas ensures accurate tumor detection in various breast conditions. The section also highlights the measurement and comparison of the S11 parameter for the PCB antenna in free space and when placed on the skin, emphasizing the impact of the skin on antenna performance. The section concludes by describing the design of the vests, including the arrangement of pockets and the use of RF cables for connection. The careful design and implementation of the vests and antenna placement ensure accurate measurements and reliable performance evaluation. The results section of the study shows that the presence of tumors in breast tissue leads to a noticeable decrease in channel attenuation. The higher dielectric properties of tumors cause additional reflections and diffraction, affecting signal propagation within the breast. These changes in channel characteristics are influenced by factors such as tumor size, breast density, and antenna configuration. The study demonstrates the detectability of tumors and provides valuable insights for developing effective tumor detection systems in different breast tissue scenarios. In this master thesis, a prototype of a breast tumor monitoring vest utilizing UWB flexible antennas was developed and evaluated. The research demonstrated the effectiveness of the vest in detecting breast tumors, even as small as 1cm, by leveraging the distinct characteristics of radio channels among multiple on-body antennas embedded in the vest. Higher frequencies in the 7–8 GHz range showed improved resolution and contrast in relative permittivity, enhancing the accuracy of tumor detection. The development of tissue phantoms played a crucial role, enabling reliable experiments to mimic human tissues. Integration of advanced AI algorithms and 6G technology holds promise for enhancing diagnostic capabilities and revolutionizing healthcare. Overall, the breast tumor monitoring vest shows potential for widespread implementation in breast health checks, home monitoring, and wireless healthcare systems

A discrete dipole approximation solver based on the COCG-FFT algorithm and its application to microwave breast imaging

We introduce the discrete dipole approximation (DDA) for efficiently calculating the two-dimensional electric field distribution for our microwave tomographic breast imaging system. For iterative inverse problems such as microwave tomography, the forward field computation is the time limiting step. In this paper, the two-dimensional algorithm is derived and formulated such that the iterative conjugate orthogonal conjugate gradient (COCG) method can be used for efficiently solving the forward problem. We have also optimized the matrix-vector multiplication step by formulating the problem such that the nondiagonal portion of the matrix used to compute the dipole moments is block-Toeplitz. The computation costs for multiplying the block matrices times a vector can be dramatically accelerated by expanding each Toeplitz matrix to a circulant matrix for which the convolution theorem is applied for fast computation utilizing the fast Fourier transform (FFT). The results demonstrate that this formulation is accurate and efficient. In this work, the computation times for the direct solvers, the iterative solver (COCG), and the iterative solver using the fast Fourier transform (COCG-FFT) are compared with the best performance achieved using the iterative solver (COCG-FFT) in C++. Utilizing this formulation provides a computationally efficient building block for developing a low cost and fast breast imaging system to serve under-resourced populations

Expansion of the nodal-adjoint method for simple and efficient computation of the 2d tomographic imaging jacobian matrix

This paper focuses on the construction of the Jacobian matrix required in tomographic reconstruction algorithms. In microwave tomography, computing the forward solutions during the iterative reconstruction process impacts the accuracy and computational efficiency. Towards this end, we have applied the discrete dipole approximation for the forward solutions with significant time savings. However, while we have discovered that the imaging problem configuration can dramatically impact the computation time required for the forward solver, it can be equally beneficial in constructing the Jacobian matrix calculated in iterative image reconstruction algorithms. Key to this implementation, we propose to use the same simulation grid for both the forward and imaging domain discretizations for the discrete dipole approximation solutions and report in detail the theoretical aspects for this localization. In this way, the computational cost of the nodal adjoint method decreases by several orders of magnitude. Our investigations show that this expansion is a significant enhancement compared to previous implementations and results in a rapid calculation of the Jacobian matrix with a high level of accuracy. The discrete dipole approximation and the newly efficient Jacobian matrices are effectively implemented to produce quantitative images of the simplified breast phantom from the microwave imaging system

Breast cancer detection using interferometric MUSIC: Experimental and numerical assessment

Purpose: In microwave breast cancer detection, it is often beneficial to arrange sensors in close proximity to the breast. The resultant coupling generally changes the antenna response. As an a priori characterization of the radio frequency system becomes difficult, this can lead to severe degradation of the detection efficacy. The purpose of this paper is to demonstrate the advantages of adopting an interferometric multiple signal classification (I-MUSIC) approach due to its limited dependence from a priori information on the antenna. The performance of I-MUSIC detection was measured in terms of signal-to-clutter ratio (SCR), signal-to-mean ratio (SMR), and spatial displacement (SD) and compared to other common linear noncoherent imaging methods, such as migration and the standard wideband MUSIC (WB-MUSIC) which also works when the antenna is not accounted for. Methods: The data were acquired by scanning a synthetic oil-in-gelatin phantom that mimics the dielectric properties of breast tissues across the spectrum 1–3 GHz using a proprietary breast microwave multi-monostatic radar system. The phantom is a multilayer structure that includes skin, adipose, fibroconnective, fibroglandular, and tumor tissue with an adipose component accounting for 60% of the whole structure. The detected tumor has a diameter of 5 mm and is inserted inside a fibroglandular region with a permittivity contrast εr-tumor/εr-fibroglandular \u3c 1.5 over the operating band. Three datasets were recorded corresponding to three antennas with different coupling mechanisms. This was done to assess the independence of the I-MUSIC method from antenna characterizations. The datasets were processed by using I-MUSIC, noncoherent migration, and wideband MUSIC under equivalent conditions (i.e., operative bandwidth, frequency samples, and scanning positions). SCR, SMR, and SD figures were measured from all reconstructed images. In order to benchmark experimental results, numerical simulations of equivalent scenarios were carried out by using CST Microwave Studio. The three numerical datasets were then processed following the same procedure that was designed for the experimental case. Results: Detection results are presented for both experimental and numerical phantoms, and higher performance of the I-MUSIC method in comparison with the WB-MUSIC and noncoherent migration is achieved. This finding is confirmed for the three different antennas in this study. Although a delocalization effect occurs, experimental datasets show that the signal-to-clutter ratio and the signal-to-mean performance with the I-MUSIC are at least 5 and 2.3 times better than the other methods, respectively. The numerical datasets calculated on an equivalent phantom for cross-testing confirm the improved performance of the I-MUSIC in terms of SCR and SMR. In numerical simulations, the delocalization effect is dramatically reduced up to an SD value of 1.61 achieved with the I-MUSIC in combination with the antipodal Vivaldi antenna. This shows that mechanical uncertainties are the main reason for the delocalization effect in the measurements. Conclusions: Experimental results show that the I-MUSIC generates images with signal-to-clutter levels higher than 5.46 dB across all working conditions and it reaches 7.84 dB in combination with the antipodal Vivaldi antenna. Numerical simulations confirm this trend and due to ideal mechanical conditions return a signal-to-clutter level higher than 7.61 dB. The I-MUSIC largely outperforms the methods under comparison and is able to detect a 5-mm tumor with a permittivity contrast of 1.5