Challenges in the Design of Microwave Imaging Systems for Breast Cancer Detection

Among the various breast imaging modalities for breast cancer detection, microwave imaging is attractive due to the high contrast in dielectric properties between the cancerous and normal tissue. Due to this reason, this modality has received a significant interest and attention from the microwave community. This paper presents the survey of the ongoing research in the field of microwave imaging of biological tissues, with major focus on the breast tumor detection application. The existing microwave imaging systems are categorized on the basis of the employed measurement concepts. The advantages and disadvantages of the implemented imaging techniques are discussed. The fundamental tradeoffs between the various system requirements are indicated. Some strategies to overcome these limitations are outlined

Achieving Accuracy in Early Stage Tumor Identification Systems based on Image Segmentation and 3D Structure Analysis

Cancer is a disease which can be removed if early stage tumor identification systems efficiently and accurately work at cancer hospitals. As the accuracy in detection of tumor means to detect exact size of the tumor. Because the best way to beat cancer is early stage tumor diagnosis and quality treatment. In this research article an accuracy module is proposed for computer aided tumor diagnosis system. The ultimate proposed CAD gets image of tumor infected lung and breast images from different state of the art early stage tumor detection methodologies as micrographic and mammographic based imaging systems. For accuracy in detection of early stage tumor, image enhancement and segmentation techniques are applied according to the imaging problems at input image. Also for accurate estimation of tumor the 3D image construction and 3D structure analysis are tried to realized. The realization of the proposed CAD proves that the accuracy module can assist well the computer aided tumor diagnosis systems with almost near to 100% accuracy in early stage tumor detection and size estimation for breast and lung cancer. Keywords: Computer Aided Tumor Detection, Accurate identificatio

UWB Pulse Radar for Human Imaging and Doppler Detection Applications

We were motivated to develop new technologies capable of identifying human life through walls. Our goal is to pinpoint multiple people at a time, which could pay dividends during military operations, disaster rescue efforts, or assisted-living. Such system requires the combination of two features in one platform: seeing-through wall localization and vital signs Doppler detection. Ultra-wideband (UWB) radar technology has been used due to its distinct advantages, such as ultra-low power, fine imaging resolution, good penetrating through wall characteristics, and high performance in noisy environment. Not only being widely used in imaging systems and ground penetrating detection, UWB radar also targets Doppler sensing, precise positioning and tracking, communications and measurement, and etc. A robust UWB pulse radar prototype has been developed and is presented here. The UWB pulse radar prototype integrates seeing-through imaging and Doppler detection features in one platform. Many challenges existing in implementing such a radar have been addressed extensively in this dissertation. Two Vivaldi antenna arrays have been designed and fabricated to cover 1.5-4.5 GHz and 1.5-10 GHz, respectively. A carrier-based pulse radar transceiver has been implemented to achieve a high dynamic range of 65dB. A 100 GSPS data acquisition module is prototyped using the off-the-shelf field-programmable gate array (FPGA) and analog-to-digital converter (ADC) based on a low cost solution: equivalent time sampling scheme. Ptolemy and transient simulation tools are used to accurately emulate the linear and nonlinear components in the comprehensive simulation platform, incorporated with electromagnetic theory to account for through wall effect and radar scattering. Imaging and Doppler detection examples have been given to demonstrate that such a “Biometrics-at-a-glance” would have a great impact on the security, rescuing, and biomedical applications in the future

A review of recent innovations in remote health monitoring

The development of remote health monitoring systems has focused on enhancing healthcare services’ efficiency and quality, particularly in chronic disease management and elderly care. These systems employ a range of sensors and wearable devices to track patients’ health status and offer real-time feedback to healthcare providers. This facilitates prompt interventions and reduces hospitalization rates. The aim of this study is to explore the latest developments in the realm of remote health monitoring systems. In this paper, we explore a wide range of domains, spanning antenna designs, small implantable antennas, on-body wearable solutions, and adaptable detection and imaging systems. Our research also delves into the methodological approaches used in monitoring systems, including the analysis of channel characteristics, advancements in wireless capsule endoscopy, and insightful investigations into sensing and imaging techniques. These advancements hold the potential to improve the accuracy and efficiency of monitoring, ultimately contributing to enhanced health outcomes for patients.Publisher's VersionQ2WOS:001130630400001PMID:3813832

A FPGA/DSP based ultrasound system for tumor detection

This work presents a method of detection of size and location of tumor using ultrasound transmission. The system utilizes Quantitative Ultrasound (QUS) which means sending an ultrasound signal from a transmitter and receiving it at multiple receivers. This received signal is analyzed for echogenic as well as echolucent tumors to differentiate between the two along with non-tumorous sample and also for delay, signal distortion to determine the size/location of the tumor. This analysis is further implemented using Field Programmable Gate Array (FPGA) and Digital Signal Processor (DSP) technologies. The proposed detection system utilizes Low Transient Pulse (LTP) technique. In this co-design architecture, the DSP carries out analysis of received demodulated signal at a lower speed while the FPGA runs at 62.5MHz for the generation of LTP signal and to demodulate bandpass ultrasonic signal sampled at 1MHz which interrupts DSP at every 1µS. This work elaborates the implementation of Quadrature Amplitude Modulation (QAM) receiver on FPGA for received signal from ultrasound detector. LTP is applied to the tumor samples through the transmitter and the received signal at ultrasonic receiver is passed through QAM to get different maxima (peaks) which are then further used for calculation of the location and subsequently, the size of the tumor using DSP. This dual platform co-design demonstrates application of a FPGA/DSP platform for the generation of low transient pulse as well as processing of the received signal

1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

Beam scanning by liquid-crystal biasing in a modified SIW structure

A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

A Contrast Source Inversion Algorithm Formulated Using the Log-Phase Formulation

The contrast source inversion (CSI) algorithm was introduced for microwave imaging in 1997 and has since proven to be one of the most successful algorithms for nonlinear microwave tomography. In the CSI algorithm, the nonlinear integral equation, which must be solved to extract the constitutive electromagnetic parameters of the object under test from the microwave measurements, is represented by two linear equations, known as the data and the object equations. In this paper, the data equation in the CSI algorithm is reformulated using the so-called log-phase formulation. In this formulation, the measured data is represented by the change in the logarithm of the amplitude and the change in the unwrapped phase. This formulation has previously been applied for nonlinear tomography within the framework of a Gauss-Newton based algorithm for detection of breast cancer. Here, significant improvements have been observed compared to the more commonly used real-imaginary formulation. The modified CSI algorithm is tested on both simulated data and on a measurement of a breast. It is shown that for imaging setups with large differences in the measured signals, the new formulation of the data equation significantly improves the performance of the CSI algorithm

Investigating the Use of Traveltime and Reflection Tomography for Deep Learning-Based Sound-Speed Estimation in Ultrasound Computed Tomography

Ultrasound computed tomography (USCT) is actively being developed to quantify acoustic tissue properties such as the speed-of-sound (SOS). Although full-waveform inversion (FWI) is an effective method for accurate SOS reconstruction, it can be computationally challenging for large-scale problems. Deep learning-based image-to-image learned reconstruction (IILR) methods are being investigated as scalable and computationally efficient alternatives. This study investigates the impact of the chosen input modalities on IILR methods for high-resolution SOS reconstruction in USCT. The selected modalities are traveltime tomography (TT) and reflection tomography (RT), which produce a low-resolution SOS map and a reflectivity map, respectively. These modalities have been chosen for their lower computational cost relative to FWI and their capacity to provide complementary information: TT offers a direct -- while low resolution -- SOS measure, while RT reveals tissue boundary information. Systematic analyses were facilitated by employing a stylized USCT imaging system with anatomically realistic numerical breast phantoms. Within this testbed, a supervised convolutional neural network (CNN) was trained to map dual-channel (TT and RT images) to a high-resolution SOS map. Moreover, the CNN was fine-tuned using a weighted reconstruction loss that prioritized tumor regions to address tumor underrepresentation in the training dataset. To understand the benefits of employing dual-channel inputs, single-input CNNs were trained separately using inputs from each modality alone (TT or RT). The methods were assessed quantitatively using normalized root mean squared error and structural similarity index measure for reconstruction accuracy and receiver operating characteristic analysis to assess signal detection-based performance measures

First-order statistical speckle models improve robustness and reproducibility of contrast-enhanced ultrasound perfusion estimates

Contrast-enhanced ultrasound (CEUS) permits the quantification and monitoring of adaptive tumor responses in the face of anti-angiogenic treatment, with the goal of informing targeted therapy. However, conventional CEUS image analysis relies on mean signal intensity as an estimate of tracer concentration in indicator-dilution modeling. This discounts additional information that may be available from the first-order speckle statistics in a CEUS image. Heterogeneous vascular networks, typical of tumor-induced angiogenesis, lead to heterogeneous contrast enhancement of the imaged tumor cross-section. To address this, a linear (B-mode) processing approach was developed to quantify the change in the first-order speckle statistics of B-mode cine loops due to the incursion of microbubbles. The technique, named the EDoF (effective degrees of freedom) method, was developed on tumor bearing mice (MDA-MB-231LN mammary fat pad inoculation) and evaluated using nonlinear (two-pulse amplitude modulated) contrast microbubble-specific images. To improve the potential clinical applicability of the technique, a second-generation compound probability density function for the statistics of two-pulse amplitude modulated contrast-enhanced ultrasound images was developed. The compound technique was tested in an antiangiogenic drug trial (bevacizumab) on tumor bearing mice (MDA-MB-231LN), and evaluated with gold-standard histology and contrast-enhanced X-ray computed tomography. The compound statistical model could more accurately discriminate anti-VEGF treated tumors from untreated tumors than conventional CEUS image. The technique was then applied to a rapid patient-derived xenograft (PDX) model of renal cell carcinoma (RCC) in the chorioallantoic membrane (CAM) of chicken embryos. The ultimate goal of the PDX model is to screen RCC patients for de novo sunitinib resistance. The analysis of the first-order speckle statistics of contrast-enhanced ultrasound cine loops provides more robust and reproducible estimates of tumor blood perfusion than conventional image analysis. Theoretically this form of analysis could quantify perfusion heterogeneity and provide estimates of vascular fractal dimension, but further work is required to determine what physiological features influence these measures. Treatment sensitivity matrices, which combine vascular measures from CEUS and power Doppler, may be suitable for screening of de novo sunitinib resistance in patients diagnosed with renal cell carcinoma. Further studies are required to assess whether this protocol can be predictive of patient outcome