Patient-Specific Background Model Estimation for Effective Brain Stroke Microwave Imaging

One of the main challenges in medical microwave imaging is to provide meaningful images when only minimal a priori information is available. Concerning the case of brain stroke imaging, this paper proposes a novel method for generating a low-resolution patient-specific approximation of the head by processing the same data used for the diagnosis. The method assumes knowledge of the head shape and a rough approximation of the fat and skin tissue above the skull. The approach consists of two steps. First, the measured data are processed using the first-order Born approximation scattering model to generate a qualitative head image. The underlying linear inverse problem is regularized by representing the unknown complex permittivity in terms of Chebyshev polynomials to improve its stability and effectiveness. Then, the voxels belonging to the skull are identified in the obtained image, and the skull boundaries are approximated from the discrete data using spherical harmonics. The proposed method is tested against an anthropomorphic phantom, demonstrating its capability to derive a patient-specific approximation. Moreover, the obtained background model enables stroke detection using real-time qualitative imaging.