A multistage selective weighting method for improved microwave breast tomography

- This paper presents a novel Multistage Selective Weighting Method (MSWM) for microwave tomography of human breast. In the proposed two stage approach, the skin layer is approximated using scaled microwave measurements in the first pass of the inversion algorithm, which is then used as a priori information in the second stage of the algorithm that reconstructs the internal tissue structure of the breast using a set of temporal scaling functions.
- A time domain inverse scattering algorithm has been applied to estimate the parameters of Debye dielectric model of breast. The proposed method has been validated using fully heterogeneous MRI-derived numerical breast phantoms.
- The reconstruction quality of the proposed method has been benchmarked against the standard time domain inverse scattering algorithm. Based on the quality score assessed through Structural Similarity Index (SSIM) and the normalized reconstruction error, it is found that the proposed method produces 54% better results in terms of SSIM and 66% in terms of normalized reconstruction error. The total reconstruction time for a 2D slice of the breast phantom has been reduced to 1/3 of the time taken by standard method.

Microwave tomography has shown potential to successfully reconstruct the dielectric properties of the human breast, thereby providing an alternative to other imaging modalities used in breast imaging applications. Considering the costly forward solution and complex iterative algorithms, computational complexity becomes a major bottleneck in practical applications of microwave tomography. In addition, the natural tendency of microwave inversion algorithms to reward high contrast breast tissue boundaries, such as the skin-adipose interface, usually leads to a very slow reconstruction of the internal tissue structure of human breast. This paper presents a multistage selective weighting method to improve the reconstruction quality of breast dielectric properties and minimize the computational cost of microwave breast tomography. In the proposed two stage approach, the skin layer is approximated using scaled microwave measurements in the first pass of the inversion algorithm; a numerical skin model is then constructed based on the estimated skin layer and the assumed dielectric properties of the skin tissue. In the second stage of the algorithm, the skin model is used as a priori information to reconstruct the internal tissue structure of the breast using a set of temporal scaling functions. The proposed method is evaluated on anatomically accurate MRI-derived breast phantoms and a comparison with the standard single-stage technique is presented.