Deep prior for suppressing noise amplification and edge preservation in Phase-based EPT with Low-SNR image

Abstract

Phase-based EPT algorithm is extremely sensitive to noise. Although many studies have investigated such as linear(Gaussian filter) or non-linear filter(TV norm) to cope with amplification, textured noise and staircasing effect still remain in phase image, which lead to conductivity error such as broadening boundary artifact or high std value in reconstructed conductivity maps. In this study, we propose a deep prior based denoising method, which achieve to not only suppress instability brought by noise amplification but reduce boundary error.

Data‐driven electrical conductivity brain imaging using 3 T MRI

“…Although the proposed method is expected to be more robust to such factors compared to the conventional phase‐based EPT reconstruction methods (Lee et al, 2021), the experiment results show that noise still impairs the estimation accuracy (Figure 5), and changes in the phase distribution are reflected in the conductivity estimations for the in‐vivo dataset (Figures 6 and 8). Thus, it would be worthwhile to investigate the combination of various artifact reduction or denoising algorithms (Cui et al, 2022; Jung, Mandija, et al, 2021; Michel et al, 2014) with the proposed method to improve the estimation performance for datasets affected by artifacts or high noise.…”