Purpose: To denoise (Formula presented.) phase using a deep learning method for phase-based in vivo electrical conductivity reconstruction in a 3T MR system. Methods: For (Formula presented.) 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 (Formula presented.) 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). Results: The proposed deep learning–based denoising approach showed improvement for (Formula presented.) 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 (Formula presented.) phase with deep learning. Conclusion: The results suggest that the proposed approach can be used as an alternative preprocessing method to denoise (Formula presented.) maps for phase-based conductivity reconstruction without relying on image filters or signal averaging.
Bibliographical noteFunding Information:
The National Research Foundation of Korea, funded by the Korea government (NRF-2019R1A2C1090635) and the Ministry of Science and ICT, Korea, under the Information Technology Research Center support program (IITP-2020-2020-0-01461), which is supervised by the Institute for Information & Communications Technology Planning & Evaluation, Korea
© 2021 International Society for Magnetic Resonance in Medicine
All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging