Assessment of the generalization of learned image reconstruction and the potential for transfer learning. Issue 1 (17th May 2018)

• Record Type: Journal Article
• Authors: Knoll, Florian; Hammernik, Kerstin; Kobler, Erich; Pock, Thomas; Recht, Michael P; Sodickson, Daniel K
• Is Part Of: Magnetic resonance in medicine. Volume 81:Issue 1(2019)
• Page Start: 116
• Page End: 128
• Publication Date: 2018-05-17
• Subjects: accelerated imaging -- deep learning -- iterative image reconstruction -- machine learning -- transfer learning -- variational network; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.27355
• Languages: English
• ISSNs: 0740-3194

Abstract

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 was evaluated by fine‐tuning baseline trainings from synthetic data with a small subset of in vivo MR training data. Results: Deviations in SNR between training and testing led to substantial decreases in reconstruction image quality, whereas image contrast was less relevant. Trainings from heterogeneous training data generalized well toward the test data with a range of acquisition parameters. Trainings from synthetic, non‐MR image data showed residual aliasing artifacts, which could be removed by transfer learning–inspired fine‐tuning. Conclusion: This study presents insights into the generalization ability of learned image reconstruction with respect to deviations in the acquisition settings between training and testing. It also provides an outlook for the potential of transfer learning to fine‐tune trainings to a particular target application using only a small number of training cases.