| Title: |
SRNR: Training neural networks for Super-Resolution MRI using Noisy high-resolution Reference data |
| Authors: |
Xiao, Jiaxin, Li, Zihan, Bilgic, Berkin, Polimeni, Jonathan R., Huang, Susie, Tian, Qiyuan |
| Publication Year: |
2022 |
| Subject Terms: |
Electrical Engineering and Systems Science - Image and Video Processing |
| More Details: |
Neural network (NN) based approaches for super-resolution MRI typically require high-SNR high-resolution reference data acquired in many subjects, which is time consuming and a barrier to feasible and accessible implementation. We propose to train NNs for Super-Resolution using Noisy Reference data (SRNR), leveraging the mechanism of the classic NN-based denoising method Noise2Noise. We systematically demonstrate that results from NNs trained using noisy and high-SNR references are similar for both simulated and empirical data. SRNR suggests a smaller number of repetitions of high-resolution reference data can be used to simplify the training data preparation for super-resolution MRI.
Comment: 2 pages, 5 figures, submitted to ISMRM |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2211.05360 |
| Accession Number: |
edsarx.2211.05360 |
| Database: |
arXiv |
