Denoising of B₁⁺ field maps for noise-robust image reconstruction in electrical properties tomography.

Purpose: To validate the use of adaptive nonlinear filters in reconstructing conductivity and permittivity images from the noisy B₁(+) maps in electrical properties tomography (EPT).

Methods: In EPT, electrical property images are computed by taking Laplacian of the B₁(+) maps. To mitigate the noise amplification in computing the Laplacian, the authors applied adaptive nonlinear denoising filters to the measured complex B₁(+) maps. After the denoising process, they computed the Laplacian by central differences. They performed EPT experiments on phantoms and a human brain at 3 T along with corresponding EPT simulations on finite-difference time-domain models. They evaluated the EPT images comparing them with the ones obtained by previous EPT reconstruction methods.

Results: In both the EPT simulations and experiments, the nonlinear filtering greatly improved the EPT image quality when evaluated in terms of the mean and standard deviation of the electrical property values at the regions of interest. The proposed method also improved the overall similarity between the reconstructed conductivity images and the true shapes of the conductivity distribution.

Conclusions: The nonlinear denoising enabled us to obtain better-quality EPT images of the phantoms and the human brain at 3 T.