We present a new inversion strategy for the early detection of breast cancer from microwave data which is based on a new multiphase level set technique. This novel structural inversion method uses a modification of the color level set technique adapted to the specific situation of structural breast imaging taking into account the high complexity of the breast tissue. We only use data of a few microwave frequencies for detecting the tumors hidden in this complex structure. Three level set functions are employed for describing four different types of breast tissue, where each of these four regions is allowed to have a complicated topology and to have an interior structure which needs to be estimated from the data simultaneously with the region interfaces. The algorithm consists of several stages of increasing complexity. In each stage more details about the anatomical structure of the breast interior is incorporated into the inversion model. The synthetic breast models which are used for creating simulated data are based on real MRI images of the breast and are therefore quite realistic. Our results demonstrate the potential and feasibility of the proposed level set technique for detecting, locating and characterizing a small tumor in its early stage of development embedded in such a realistic breast model. Both the data acquisition simulation and the inversion are carried out in 2D.
In this paper, we propose and analyze a novel shape reconstruction technique for the early detection of breast cancer from microwave data, which is based on a level-set technique. The shape-based approach offers several advantages compared to more traditional pixel-based approaches when targeting the reconstruction of key characteristics of a hidden tumor such as its correct size, shape, and static permittivity value. In addition to these key characteristics of hidden tumors, we aim at estimating the correct interfaces between fatty and fibroglandular tissue in the breast and their internal permittivity profiles. The level set strategy (which is an implicit representation of the shapes) frees us from topological restrictions when reconstructing an a priori arbitrary number of tumors and the often quite complicated interfaces between fatty and fibroglandular regions. The presented strategy is able to detect and, in most cases, characterize tumors whose sizes (diameters) are much smaller than the wavelengths of the electromagnetic waves that are used for illuminating the breast. We present numerical results for a 2-D model with two distinct tissue types (fatty and fibroglandular) in the interior of the breast (in addition to a possible tumor and the surrounding skin). Our results demonstrate the performance and potential of our scheme in various simulated but realistic situations.
We focus on the application of microwaves for the early detection of breast cancer. We investigate the potential of a novel strategy using shapes for modeling the tumor in the breast. An inversion using a shape-based model offers several advantages like well-defined boundaries and the incorporation of an intrinsic regularization that reduces the dimensionality of the inverse problem whereby at the same time stabilizing the reconstruction. We explore novel level-set techniques as a means to detect the tumor without any initialization of its position and size. We present some numerical resonstructions and we compare them with the conventional MUSIC algorithm, in particular with respect to the frequency which is used for the investigation. We show that for different frequencies these two methods show a different qualitative behaviour in the reconstructions.
We show that the source location problem can be solved in a scattering medium using the fluorescence lifetime and realistic a priori information. The intrinsic ill-posedness of the problem is reduced when the level of scattering increases. This work is a proof of principle demonstrating the high potential of quantitative lifetime imaging in complex media.
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