Assessment of the generalization of learned image reconstruction and the potential for transfer learning.

ABSTRACT: PURPOSE:Although deep learning has shown great promise for MR image reconstruction, an open question regarding the success of this approach is the robustness in the case of deviations between training and test data. The goal of this study is to assess the influence of image contrast, SNR, and image content on the generalization of learned image reconstruction, and to demonstrate the potential for transfer learning. METHODS:Reconstructions were trained from undersampled data using data sets with varying SNR, sampling pattern, image contrast, and synthetic data generated from a public image database. The performance of the trained reconstructions was evaluated on 10 in vivo patient knee MRI acquisitions from 2 different pulse sequences that were not used during training. Transfer learning wa

SUBMITTER: Knoll F 

PROVIDER: S-EPMC6240410 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature