Improving phase-based conductivity reconstruction by means of deep learning-based denoising of phase data for 3T MRI

To denoise $$ phase using a deep learning method for phase-based in vivo electrical conductivity reconstruction in a 3T MR system.

For $$ phase deep-learning denoising, a convolutional neural network (U-net) was chosen. Training was performed on data sets from 10 healthy volunteers. Input data were the real and imaginary components of single averaged spin-echo data (SNR = 45), which was used to approximate the $$ phase. For label data, multiple signal-averaged spin-echo data (SNR = 128) were used. Testing was performed on in silico and in vivo data. Reconstructed conductivity maps were derived using phase-based conductivity reconstructions. Additionally, we investigated the usability of the network to various SNR levels, imaging contrasts, and anatomical sites (ie, T₁ , T₂ , and proton density-weighted brain images and proton density-weighted breast images. In addition, conductivity reconstructions from deep learning-based denoised data were compared with conventional image filters, which were used for data denoising in electrical properties tomography (ie, the Gaussian filtering and the Savitzky-Golay filtering).

The proposed deep learning-based denoising approach showed improvement for $$ phase for both in silico and in vivo experiments with reduced quantitative error measures compared with other methods. Subsequently, this resulted in an improvement of reconstructed conductivity maps from the denoised $$ phase with deep learning.

The results suggest that the proposed approach can be used as an alternative preprocessing method to denoise $$ maps for phase-based conductivity reconstruction without relying on image filters or signal averaging.