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Abstract #1778

Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge

Chompunuch Sarasaen1,2, Soumick Chatterjee1,3,4, Fatima Saad2, Mario Breitkopf1, Andreas N├╝rnberger4,5,6, and Oliver Speck1,5,6,7
1Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg, Germany, 2Institute for Medical Engineering, Otto von Guericke University, Magdeburg, Germany, 3Data and Knowledge Engineering Group, Otto von Guericke University, Magdeburg, Germany, 4Faculty of Computer Science, Otto von Guericke University, Magdeburg, Germany, 5Center for Behavioral Brain Sciences, Magdeburg, Germany, 6German Center for Neurodegenerative Disease, Magdeburg, Germany, 7Leibniz Institute for Neurobiology, Magdeburg, Germany

Dynamic imaging is required during interventions to assess the physiological changes. Unfortunately, while achieving a high temporal resolution the spatial resolution is compromised. To overcome the spatiotemporal trade-off, in this work deep learning based super-resolution approach has been utilized and fine-tuned using prior-knowledge. 3D dynamic data for three subjects was acquired with different parameters to test the generalization capabilities of the network. Experiments were performed for different in-plane undersampling levels. A U-net based model[1] with perceptual loss[2] was used for training. Then, the trained network was fine-tuned using prior scan to obtain high resolution dynamic images during the inference stage.

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