Abstract #3376

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

Florian Knoll^1,2, Kerstin Hammernik^1,2,3, Thomas Pock³, Daniel K Sodickson^1,2, and Michael P Recht^1,2

¹Center for Biomedical Imaging, New York University School of Medicine, New York, NY, United States, ²Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, United States, ³Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria

The goal of this study is to assess the influence of image contrast, SNR and image content on the generalization of machine learning in MR image reconstruction. Experiments are performed with patient data from clinical knee MR exams as well as synthetic data created from a public image database. It shows that while SNR is a critical parameter, trainings can be generalized towards a range of SNR values. It also demonstrates that transfer learning can be used successfully to fine-tune trainings from synthetic data to a particular target application using only a very small number of training cases.

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