Stroke is a dangerous disease with a high recurrence rate. Therefore, postoperative patients need timely monitoring of stroke conditions in their rehabilitation stage for early treatment. Recent studies in biomedical imaging have shown that strokes produce variations in the electric permittivity of brain tissues, which can be detected by microwave imaging techniques. Assuming that we have obtained the image of electromagnetic parameters in previous treatment, we can use differential imaging to detect the bleeding points when stroke recurs. However, the computational cost of traditional methods could be prohibitively large, as the bleeding points are small in the early stages of recurrence and the data of scatterers are sparse. Compressive sensing is a potential candidate for efficient microwave imaging algorithms. In this paper, we propose a new method for microwave differential imaging. The difference of the scattering field is related to sparse unknown scatterers by the Green's function of the inhomogeneous medium. An improved simultaneous stagewise weak orthogonal matching pursuit algorithm is then proposed to solve the problem in the framework of compressive sensing. The numerical results validate the accuracy and efficiency of the proposed method.

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Yahui Ding, Yifan Chen, Wending Mai, and Jun Hu