Including knowledge of the breast surface and skin layer leads to a substantial improvement in image quality compared to the blind case, but the images have limited diagnostic utility for applications such as tumor response tracking. The diagnostic utility of the reconstruction technique is improved considerably when patient-specific structural information is used. This qualitative observation is supported quantitatively with image metrics.
Abstract-Microwave tomography (MWT) and a radar-based region estimation technique are combined to create a novel algorithm for biomedical imaging with a focus on breast cancer detection and monitoring. The region estimation approach is used to generate a patient-specific spatial map of the breast anatomy that includes skin, adipose and fibroglandular regions, as well as their average dielectric properties. This map is incorporated as a numerical inhomogeneous background into an MWT algorithm based on the finite element contrast source inversion (FEM-CSI) method. The combined approach reconstructs finer structural details of the breast and better estimates the dielectric properties than either technique used separately. Numerical results obtained with the novel combined algorithmic approach, based on synthetically generated breast phantoms, show significant improvement in image quality.
Biomedical imaging and sensing applications in many scenarios demand accurate surface estimation from a sparse set of noisy measurements. These measurements may arise from a variety of sensing modalities, including laser or electromagnetic samples of an object’s surface. We describe a state-of-the-art microwave imaging prototype that has sensors to acquire both microwave and laser measurements. The approach developed to translate sparse samples of the breast surface into an accurate estimate of the region of interest is detailed. To evaluate the efficacy of the method, laser and electromagnetic samples are acquired by sensors from three realistic breast models with varying sizes and shapes. A set of metrics is developed to assist with the investigation and demonstrate that the algorithm is able to accurately estimate the shape of a realistic breast phantom when only a sparse set of data are available. Moreover, the algorithm is robust to the presence of measurement noise, and is effective when applied to measurement scans of patients acquired with the prototype.
We present a new inversion strategy that integrates radar-based methods with microwave tomography (MT) to efficiently provide low resolution information about an object's structure and average dielectric properties. For this preliminary investigation, we assume that the object may be characterized as having three regions: a thin outer layer and an interior with two inhomogeneous regions having dissimilar average dielectric properties. Our aim is not to reconstruct a detailed image of an object, but rather to provide information about its basic structure, including the geometric and mean dielectric properties of regions predominantly composed of a given material. The inversion technique is carried out in two steps. First, a reconstruction model indicating the locations and spatial features of the three regions of interest is constructed efficiently and quickly using ultrawideband (UWB) reflection data. The reconstruction model formed using radar-based techniques is then incorporated into the second step of the procedure which estimates the mean dielectric properties over each region using MT methods. Identifying the three homogeneous regions with radar-based techniques provides a priori information about an object's internal geometry and significantly simplifies the parameter space structure so that the inverse scattering problem solved with MT is not as ill-posed as those typically encountered. The performance of the proposed technique is first evaluated with both reflection and transmission data generated by progressively more complex 2D numerical models. Microwave breast imaging approaches would benefit from the internal structural information extracted by the algorithm, so a practical application is explored using 2D breast models formed from the magnetic resonance (MR) scans of a patient study. The algorithm's ability to infer the breast's basic internal structure is demonstrated with these examples.
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