Gradient and Newton-Kantorovich Methods for Microwave Tomography
The development of reconstruction algorithms for Active Microwave Imaging, with applications in the medical domain or for non-destructive testing , and more generally for electromagnetic and acoustic imaging , has gained much interest during the last decade. The first generation of algorithms was based on Diffraction Tomography [2–5], which is a generalization of classical X-ray Computed Tomography, by taking into account diffraction effects. The scattered field data are filtered, mapped onto the Ewald sphere in k-space and inverse transformed. These algorithms provide quasi real-time approximate reconstructions of the polarization current density distribution (qualitative imaging). For a weak scatterer (Born or Rytov approximations), they also yield the complex permittivity distribution (quantitative imaging). They have been used and tested on experimental imaging systems such as a 2.45 GHz planar microwave camera , a 2.33 GHz circular microwave scanner [7, 8], and a broad frequency band microwave sensor , yielding valuable qualitative images, although artifacts are present for strong and/or inhomogeneous scatterers .
KeywordsConjugate Gradient Tikhonov Regularization Scattered Field Microwave Imaging Test Domain
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