A preliminary study for early breast cancer detection with microwaves

Breast cancer is the most common type of cancer in female all over the world. Early detection and treatment gives a chance to overcome this cancer. In breast cancer detection, there are many methods such as X-ray mammography, Magnetic Resonance Imaging, and Ultrasound Imaging. However, existing these methods have limitations such as X-rays, disturbing pressure on the breast, and high cost of devices, etc. Because of all the reasons mentioned, microwave breast imaging has the potential to overcome from some of the limitations of conventional breast cancer screening systems. The physical basis breast cancer detection by microwaves depends on the difference between the dielectric properties of normal and malignant breast tissue. Microwave breast cancer imaging is also a noninvasive method and it has low cost. Therefore, microwave imaging technology for breast cancer detection has attracted much attention by many researchers in these days. By using Computer Simulation Technology Microwave Studio and Antenna Magus Software, breast model with tumor and antennas were generated in this study. The presence of the tumor was investigated using a receiver and a transmitting antenna. While the transmitting antenna was stationary, the receiving antenna was moved to different positions. S11 (return loss) results were evaluated. This study is a preliminary study to determine the location and characteristic features of the tumor. Furthermore, this study will show that scanning methods will determine the location and size of the tumor at higher accuracy and the reconstruction of the reflected waves will allow to clearly determining the location of the tumor

Microwave Imaging for Early Breast Cancer Detection

We overview the research trend on microwave imaging for early breast cancer detection. The technologies have two categories: ultra-wide band (UWB) radar that reconstructs the scattering power distribution in the breast and inverse scattering problem that reconstructs the dielectric properties distribution. We have developed a clinical equipment using UWB radar and carried out clinical test 4 years ago. Through the experiments, we concluded that the UWB radar was insufficient for the clinical equipment, because the UWB radar cannot discriminate cancerous tumor and other lesions. Therefore, we have been studying inverse scattering. It is a challenging task to develop an equipment using inverse scattering technologies. We have proposed a microwave mammography that has four features: (1) sensor with breast fixing by absorption, (2) small sensor with multipolarization, (3) image reconstruction program linking the commercial EM simulator, and (4) hybrid imaging method using UWB radar and inverse scattering

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

Feasibility study of focal lens for multistatic microwave breast imaging

Microwave Imaging is an emerging technique to aid breast cancer diagnosis. Current multistatic setups involve complex and heavy signal processing techniques, such as to remove the energy coupling between adjacent sensors, which masks the response from inner tissues. We investigate a novel approach using a dielectric lens in order to reduce the coupling effects between antennas, thus reducing the signal processing burden, while preserving all the advantages of multistatic setups. In this paper, we show that we can successfully detect simulated breast targets on reconstructed images using a setup with a dielectric lens.info:eu-repo/semantics/publishedVersio

Microwave power imaging for ultra-wide band early breast cancer detection

Due to the critical need for complementary or/and alternative modalities to current X-ray mammography for early-stage breast cancer detection, a 3D active microwave imaging system has been developed. This thesis presents a detailed method for rapid, high contrast microwave imaging for the purpose of breast survey. In the proposed imaging system, several transmitters polarized in different directions take turns sending out a low-power UWB pulse into the breast; backscattered signals are recorded by a synthetic aperture antenna array. These backscattered signals are passed through a beamformer, which spatially focuses the waveforms to image backscattered energy as a function of location in the breast. A simple Delay-and-Sum algorithm is applied to test the proposed multistatic multi-polarized detection scheme. The obtained 2-D and 3-D numerical results have demonstrated the feasibility and superiority of detecting small malignant breast tumors using our antenna strategy. An improved algorithm of microwave power imaging for detecting small breast tumors within an MRI-derived phantom is also introduced. Our imaging results demonstrate that a high-quality image can be reached without solving the inverse problem. To set up an experimental system for future clinical investigation, we developed two Vivaldi antennas, which have a notable broad band property, good radiation pattern, and a suitable size for breast cancer detection. Finally, an antenna array which consists of eight proposed Vivaldi antennas is introduced. By conveniently moving up/down and rotating this antenna array, it can be used for the multistatic breast cancer imaging and qualified for our multi-polarized scan mode

Parallel delay multiply and sum algorithm for microwave medical imaging using spark big data framework

Microwave imaging systems are currently being investigated for breast cancer, brain stroke and neurodegenerative disease detection due to their low cost, portable and wearable nature. At present, commonly used radar-based algorithms for microwave imaging are based on the delay and sum algorithm. These algorithms use ultra-wideband signals to reconstruct a 2D image of the targeted object or region. Delay multiply and sum is an extended version of the delay and sum algorithm. However, it is computationally expensive and time-consuming. In this paper, the delay multiply and sum algorithm is parallelised using a big data framework. The algorithm uses the Spark MapReduce programming model to improve its efficiency. The most computational part of the algorithm is pixel value calculation, where signals need to be multiplied in pairs and summed. The proposed algorithm broadcasts the input data and executes it in parallel in a distributed manner. The Spark-based parallel algorithm is compared with sequential and Python multiprocessing library implementation. The experimental results on both a standalone machine and a high-performance cluster show that Spark significantly accelerates the image reconstruction process without affecting its accuracy