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Abstract #1757

Going beyond the image space: undersampled MRI reconstruction directly in the k-space using a complex valued residual neural network

Soumick Chatterjee1,2,3, Chompunuch Sarasaen1,4, Alessandro Sciarra1,5, Mario Breitkopf1, Steffen Oeltze-Jafra5,6,7, Andreas N├╝rnberger2,3,7, and Oliver Speck1,6,7,8
1Department of Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg, Germany, 2Data and Knowledge Engineering Group, Otto von Guericke University, Magdeburg, Germany, 3Faculty of Computer Science, Otto von Guericke University, Magdeburg, Germany, 4Institute of Medical Engineering, Otto von Guericke University, Magdeburg, Germany, 5MedDigit, Department of Neurology, Medical Faculty, University Hopspital, Magdeburg, Germany, 6German Centre for Neurodegenerative Diseases, Magdeburg, Germany, 7Center for Behavioral Brain Sciences, Magdeburg, Germany, 8Leibniz Institute for Neurobiology, Magdeburg, Germany

One of the common problems in MRI is the slow acquisition speed, which can be solved using undersampling. But this might result in image artefacts. Several deep learning based techniques have been proposed to mitigate this problem. Most of these methods work only in the image space. Fine anatomical structures obscured by artefacts in the image can be challenging to reconstruct for a model working in the image space, but not in k-space. In this research, a novel complex-valued ResNet has been proposed to work directly in the k-space to reconstruct undersampled MRI. The preliminary experiments have shown promising results.

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