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Abstract #1057

Brain MR image super resolution using simulated data to perform in real-world MRI

Aymen Ayaz1, Kirsten Lukassen1, Cristian Lorenz2, Juergen Weese2, and Marcel Breeuwer1,3
1Biomedical Engineering Department, Eindhoven University of Technology, Eindhoven, Netherlands, 2Philips Research Laboratories, Hamburg, Germany, 3MR R&D – Clinical Science, Philips Healthcare, Best, Netherlands


We propose to simulate a large set of anatomically variable voxel-aligned and artifact-free brain MRI data at different resolutions to be used for training deep-learning based Super Resolution (SR) networks. To the best of our knowledge, no such efforts have been made in past regarding use of simulated data to train a SR network. We trained a SR network using such simulated data and tested the performance on real-world MRI data. The trained network could significantly sharpen low-resolution input MR images and clearly outperformed classic image interpolation methods.

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