Microwave image reconstruction of tissue property dispersion characteristics utilizing multiple-frequency information

A multiple-frequency-dispersion reconstruction algorithm utilizing a Gauss-Newton iterative strategy is presented for microwave imaging. This algorithm facilitates the simultaneous use of multiple-frequency measurement data in a single image reconstruction. Using the stabilizing effects of the low-frequency measurement data, higher frequency data can be included to reconstruct images with improved resolution. The parameters reconstructed in this implementation are now frequency-independent dispersion coefficients instead of the actual properties and may provide new diagnostic information. In this paper, large high-contrast objects are successfully constructed utilizing assumed simple dispersion models for both simulation and phantom cases for which the traditional single-frequency algorithm previously failed. Consistent improvement in image quality can be observed by involving more frequencies in the reconstruction; however, there appears to be a limit to how closely spaced the frequencies can be chosen while still providing independent new information. Possibilities for fine-tuning the image reconstruction performance in this context include: 1) variations of the assumed dispersion model and 2) Jacobian matrix column and row weighting schemes. Techniques for further reducing the forward solution computation time using time-domain solvers are also briefly discussed. The proposed dispersion reconstruction technique is quite general and can also be utilized in conjunction with other Gauss-Newton-based algorithms including the log-magnitude phase-form algorithm.