Numerical Breast Models Research Articles

A progressive processing method for breast cancer detection via UWB based on an MRI-derived model

Ultra-wideband (UWB) microwave imaging is a promising method for breast cancer detection based on the large contrast of electric parameters between the malignant tumor and its surrounded normal breast organisms. In the case of multiple tumors being present, the conventional imaging approaches may be ineffective to detect all the tumors clearly. In this paper, a progressive processing method is proposed for detecting more than one tumor. The method is divided into three stages: primary detection, refocusing and image optimization. To test the feasibility of the approach, a numerical breast model is developed based on the realistic magnetic resonance image (MRI). Two tumors are assumed embedded in different positions. Successful detection of a 3.6 mm-diameter tumor at a depth of 42 mm is achieved. The correct information of both tumors is shown in the reconstructed image, suggesting that the progressive processing method is promising for multi-tumor detection.

A PREPROCESSING FILTER FOR MULTISTATIC MICROWAVE BREAST IMAGING FOR ENHANCED TUMOUR DETECTION

Ultra Wideband Radar imaging has shown promising results in the detection of small tumours within low to medium density human breasts. A wide range of beamforming algorithms has been presented in several recent studies with good tumour localization capabilities, but most of these sufier a deterioration in performance with an increase in breast tissue density. In this paper, a preprocessing fllter is used to compensate for path-dependent attenuation and phase efiects, in conjunction with a range of existing data-dependent and data-independent confocal microwave imaging algorithms. Results indicate that this data preprocessing improves the performance of all beamformers, enabling detection and accurate localization of multiple tumours in mild to moderately dense human breasts. The proposed framework is tested on 3D anatomically accurate numerical breast models and the performance is evaluated across a range of appropriate metrics.

Towards Enhancing Skin Reflection Removal and Image Focusing Using a 3-D Breast Surface Reconstruction Algorithm

Suppressing skin reflection is vital for successful tumor detection in radar breast imaging systems. In this communication, a novel skin reflection removal (SRR) algorithm is presented based on a previously-proposed breast surface reconstruction algorithm. This skin reflection removal algorithm is validated using numerical MRI-derived breast models. This communication also investigates how the same skin location information can be used to enhance the delay-and-sum algorithm.

Holographic microwave imaging for medical applications

This paper presents a new 2D holographic microwave imaging array (HMIA) technique for medical imaging applications. The HMIA technique has been applied to early stage breast cancer detection and brain stroke detection. Computer models are developed to demonstrate the feasibility of detecting and localizing small brain strokes within a 2D numerical head model and breast tumours within a 3D numerical breast model using the HMIA technique. Experimental validation of the HMIA simulation model using a breast phantom has been undertaken and demonstrated a good agreement between experimental and simulated images. Simulation and experimental results showed that the proposed HMIA technique has the potential to become a powerful medical screening and diagnostic tool.

Microwave Radar and Microwave-Induced Thermoacoustics: Dual-Modality Approach for Breast Cancer Detection

Microwave radar and microwave-induced thermoacoustics, recently proposed as promising breast cancer detection techniques, each have shortcomings that reduce detection performance. Making the assumption that the measurement noises experienced when applying these two techniques are independent, we propose a methodology to process the input signals jointly based on a hypothesis testing framework. We present two test statistics and derive their distributions to set the thresholds. The methodology is evaluated on numerically simulated signals acquired from 2-D numerical breast models using finite-difference time-domain method. Our results show that the proposed dual-modality approach can give a significant improvement in detection performance.

NEIGHBORHOOD-BASED ALGORITHM TO FACILITATE THE REDUCTION OF SKIN REFLECTIONS IN RADAR-BASED MICROWAVE IMAGING

Radar-based microwave imaging is being investigated as a complementary diagnostic tool for breast cancer detection. One of the major challenges associated with radar-based breast imaging is the removal of the overwhelming re∞ection caused by the skin. This paper presents an algorithm that has been designed for realistic 3D scenarios. The algorithm is tested on a variety of realistic 3D numerical breast models, as well as measured data from a phantom and patient. In all cases, the re∞ections from the skin are signiflcantly reduced, facilitating detection of known tumors.

Magnetoacoustic tomography with magnetic induction (MAT-MI) for breast tumor imaging: numerical modeling and simulation

Magnetoacoustic tomography with magnetic induction (MAT-MI) was recently introduced as a noninvasive electrical conductivity imaging approach with high spatial resolution close to ultrasound imaging. In this study, we test the feasibility of the MAT-MI method for breast tumor imaging using numerical modeling and computer simulation. Using the finite element method, we have built three-dimensional numerical breast models with varieties of embedded tumors for this simulation study. In order to obtain an accurate and stable forward solution that does not have numerical errors caused by singular MAT-MI acoustic sources at conductivity boundaries, we first derive an integral forward method for calculating MAT-MI acoustic sources over the entire imaging volume. An inverse algorithm for reconstructing the MAT-MI acoustic source is also derived with spherical measurement aperture, which simulates a practical setup for breast imaging. With the numerical breast models, we have conducted computer simulations under different imaging parameter setups and all the results suggest that breast tumors that have large conductivity in contrast to the surrounding tissue as reported in the literature may be readily detected in the reconstructed MAT-MI images. In addition, our simulations also suggest that the sensitivity of imaging breast tumors using the presented MAT-MI setup depends more on the tumor location and the conductivity contrast between the tumor and its surrounding tissue than on the tumor size.

EVOLVING SPIKING NEURAL NETWORK TOPOLOGIES FOR BREAST CANCER CLASSIFICATION IN A DIELECTRICALLY HETEROGENEOUS BREAST

Several studies have investigated the possibility of using the Radar Target Signature (RTS) of a tumour to classify the tumour as either benign or malignant, since the RTS has been shown to be in∞uenced by the size, shape and surface texture of tumours. The Evolved-Topology Spiking Neural Neural (SNN) presented here extends the use of evolutionary algorithms to determine an optimal number of neurons and interneuron connections, forming a robust and accurate Ultra Wideband Radar (UWB) breast cancer classifler. The classifler is examined using dielectrically realistic numerical breast models, and the performance of the classifler is compared to an existing Fixed-Topology SNN cancer classifler.

Support Vector Machine-Based Ultrawideband Breast Cancer Detection System

Ultrawideband (UWB) microwave radar is a promising alternative breast screening method. Previous research has focused on the imaging and classification of early-stage breast cancer from backscattered microwave signals. The heterogeneous composition of breast tissue, prevalent among younger females, inhibits UWB scanning technologies to effectively localize cancerous regions within the breast. Rather than using UWB radar to image the breast or classify between types of cancer, the method proposed in this paper is to simply detect the presence of a tumor using a Support Vector Machine (SVM) based UWB cancer detection system. The SVM cancer detection system is evaluated using dielectrically realistic numerical breast models, and the performance of the detection system is compared to a Linear Discriminant Analysis (LDA) classification method.

Breast Lesion Classification Using Ultrawideband Early Time Breast Lesion Response

Breast lesion characterization for discriminating between localized malignant and benign lesions is important as the current breast screening techniques do not have the required specificity to be clinically acceptable. A method using ultrawideband (UWB) microwave imaging system for such classification is proposed in this paper. The early time portion of the backscatter breast response is processed for lesion discrimination. This method provides a high resolution since the early time lesion response has the largest signal strength. A correlator is used at the receiver to extract the early response and to quantify the degree of ruggedness of a lesion through several key parameters associated with the correlation operation. Subsequently, a large scale simulation study using a two-dimensional (2D) numerical breast model with an antenna array is used for the development of a lesion classification technique. It is shown that the lesion classification method is capable of discriminating between lesions with different morphologies.

Feasibility Study of Lesion Classification via Contrast-Agent-Aided UWB Breast Imaging

This letter investigates the feasibility of applying contrast agents for lesion classification in ultra wideband (UWB) breast imaging. Previous study has focused on distinguishing benign from malignant masses by exploiting their morphology-dependent backscatter signature via the complex natural resonances of the late-time target response. The tissue differentiation capability, however, deteriorates severely if the intrinsic contrast between the dielectric properties of dysplastic and normal tissues are small. A possible solution to this problem is proposed in this letter via the use of microwave contrast agents, where the damping factors of the differential backscatter responses before and after the infusion of contrast agents to a dysplastic inclusion are used to correlate with the anomaly shapes. The feasibility of this approach for lesion classification is demonstrated through comprehensive simulation studies using realistic numerical breast models.

An Improved Technique to Predict the Time-of-Arrival of a Tumor Response in Radar-Based Breast Imaging

Radar-based microwave imaging has been proposed as a complementary modality for early-stage breast cancer screening. This paper presents an algorithm that may be used to accurately predict the time-of-arrival (TOA) of a tumor response contained in sample data acquired from a small number of antennas in a realistic scenario. The TOA information may be used by many of the existing radar-based methods to detect and localize a tumor response. The robustness of the algorithm is demonstrated with data generated from realistic numerical breast models.

Computational feasibility of deformable mirror microwave hyperthermia technique for localized breast tumors

Purpose: Computational feasibility of a new non-invasive microwave hyperthermia technique that employs dual deformable mirror is investigated using simplified computational tools and anatomically realistic breast models.Materials and methods: The proposed technique employs two pairs of electromagnetic sources and continuously deformable mirrors to focus the electromagnetic radiation at the target site for hyperthermia. The mirror functions like a continuum of radiating elements that offer effective scan coverage inside the breast with efficient field focusing at the target location. The electric field focusing and temperature mapping in the two-dimensional numerical simulations are investigated using wave propagation and bio-heat transfer models respectively. The method of moments, a popular numerical simulation tool, is used to model the electric field maintained by the deformable mirrors for continuous wave excitation. The electromagnetic (EM) energy deposited by the mirrors is used in the steady state bio-heat transfer equation to quantify the temperature distribution inside two-dimensional anatomically realistic breast models.Results: Feasibility of the proposed technique is evaluated using numerical breast models derived from magnetic resonance images of patients with variation in breast density, age and pathology.Conclusions: The computational study indicates preferential EM energy deposition and temperature elevation inside tumor tissue with minimum collateral damage to the neighboring normal tissues. Simulation results obtained for the magnetic resonance (MR) breast data appear promising and indicate the merit in pursuing the investigation using 3D computational models.

FDTD Investigations into UWB Radar Technique of Breast Tumor Detection and Location

In this paper, a finite difference time domain (FDTD) method is applied to investigate capabilities of an ultra-wide band (UWB) radar system to detect and locate a breast tumor. The investigations are divided into three parts. The first part concerns an EM field analysis of a phantom formed by a plastic container with liquid and a small highly reflecting target. In the second part, a three-dimensional numerical breast model is used to perform more advanced studies. In the carried out 3D FDTD simulations, a quasi-plane wave is used as an incident wave. Various time snap shots of the electromagnetic field are recorded to learn about the physical phenomenon of reflection and scattering in different layers of the phantoms. The third part of the investigations concerns a two dimensional (cylindrical) image reconstruction, which is performed by means of 2D FDTD. The obtained results should form the ground for working out suitable guidelines for designing an optimal microwave breast imaging apparatus based on the UWB radar technique.

Numerical uncertainty and perfusion induced instability in bioheat equation: its importance in thermographic interpretation

The use of engineering in biomedical sciences has opened new facets in research. The present paper deals with problems arising from numerical simulation, in order to develop an expert system for the diagnosis of breast cancer using thermography. A female breast is modelled in three dimensions and the surface temperature pattern is obtained by solving the bioheat equation. This solution will be used in conjunction with a database of thermograms to develop an intelligent diagnostic tool. The focus of the present paper is to build and check the confidence level of the numerical scheme before proceeding to model the actual problem. The parametric study is done along with a check for mesh insensitivity and wiggle free isotherm contours. This process yielded a benchmark nodal distance, with which the 3D model is generated and isotherm pattern analysed. It can be seen that this enhances the accuracy of the surface temperature distribution. The use of this is tested in a close-to-actual numerical breast model and the results compared with the thermographic results. The outcome is very encouraging. Finally, a typical clinical protocol in conjunction with the use of numerical prediction for breast thermographic interpretation is outlined.