Abstract #4816
Motion-robust dynamic abdominal MRI using k-t GRASP and dynamic dual-channel training of super-resolution U-Net (DDoS-UNet)
Chompunuch Sarasaen1,2,3, Soumick Chatterjee1,3,4,5, Georg Rose2,3, Andreas Nürnberger4,5,6, and Oliver Speck1,3,6,7,8
1Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg, Germany, 2Institute for Medical Engineering, Otto von Guericke University, Magdeburg, Germany, 3Research Campus STIMULATE, Otto von Guericke University, Magdeburg, Germany, 4Data and Knowledge Engineering Group, Otto von Guericke University, Magdeburg, Germany, 5Faculty of Computer Science, Otto von Guericke University, Magdeburg, Germany, 6German Center for Neurodegenerative Disease, Magdeburg, Germany, 7Center for Behavioral Brain Sciences, Magdeburg, Germany, 8Leibniz Institute for Neurobiology, Magdeburg, Germany
Synopsis
Cartesian sampling techniques are available to speed up the measurement of dynamic MRI, such as k-t GRAPPA. However, radial samplings, such as iGRASP, are more robust to motion and can be applied for abdominal dynamic MRI. In this work, k-t GRAPPA inspired iGRASP has been created (so-called k-t GRASP)–which acquires the subsequent time points by starting the initial spoke of the time point with an angle with the last spoke of the previous time point, and extended by super-resolution reconstruction of dynamic abdominal MRI using DDoS-UNet. The method was evaluated in 3D dynamic data of four subjects with retrospective undersampling.
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