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Female breast at macroscopic scale is a nonmagnetic medium, which eliminates the possibility of realizing microwave imaging of the breast cancer based on magnetic permeability variations. However, by administering functionalized, superparamagnetic iron oxide nanoparticles (SPIONs) as a contrast material to modulate magnetic permeability of cancer cells, a small variation on the scattered electric field from the breast is achievable under an external, polarizing magnetic field. Purpose: We demonstrate an imaging technique that can locate cancerous tumors inside the breast due to electric field variations caused by SPION tracers under different magnetic field intensities. Furthermore, we assess the feasibility of SPION-enhanced microwave imaging for breast cancer with simulations performed on a multi-static imaging configuration. Methods: The imaging procedure is realized as the factorization method of qualitative inverse scattering theory, which is essentially a shape retrieval algorithm for inaccessible objects. The formulation is heuristically modified to accommodate the scattering parameters instead of the electric field to comply with the requirements of experimental microwave imaging systems. Results: With full-wave electromagnetic simulations performed on an anthropomorphically realistic breast phantom, which is excited with a cylindrical imaging prototype of 18 dipole antenna arranged as a single row, the technique is able to locate cancerous tumors for a experimentally achievable doses. Conclusions:The technique generates nonanatomic microwave images, which map the cancerous tumors depending on the concentration of SPION tracers, to aid the diagnosis of the breast cancer.

A new variant of Newton type methods has been developed for quantitative microwave imaging. To deal with the ill-posedness of the inverse problems, standard Newton type methods involve a linearization of the so called data equation using the Fréchet derivative with respect to the contrast function.Here, the formulation is expanded to include the object equation, therefore, the formulation seeks to reduce the errors in both the data and the object equations. While this modification does not remove the need to solve forward problem at each step, it nevertheless significantly improves convergence rate and the performance. To assess the efficiency of the proposed technique, numerical simulations with synthetic and experimental data have been carried out. The results demonstrate that the proposed variant outperforms the standard Newton method, and shows comparable performance to the contrast source inversion (CSI) algorithm with fewer iterations.

Özetçe Bu çalışmada, solumum fonksiyon testi ile elde edilen ölçümler kullanılarak cinsiyet tanıma işlemi gerçekleştirilmiştir. Önerilen yöntem üç ana aşamadan oluşmaktadır. İlk aşamada solumum fonksiyon testinden öznitelikler çıkartılır. İkinci aşamada belirlenen öznitelikler kullanılarak Gauss Karışım Modeline (GKM) dayalı istatistiksel modeller eğitilir ve son aşamada test edilen kişinin erkek veya kadın olduğuna olabilirlik oran testi ile karar verilir. AbstractIn this work, we proposed a new gender detection algorithm based on pulmonary function test. The proposed method has three main stages. In first the stage, some features are extracted from pulmonary function test. In the second stage, the probabilistic models based on Gaussian Mixture Model (GMM) are trained using these features, and in the final stage, the gender of test person is detected based on likelihood ratio test.