Abstract #1757

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

Soumick Chatterjee^1,2,3, Chompunuch Sarasaen^1,4, Alessandro Sciarra^1,5, Mario Breitkopf¹, Steffen Oeltze-Jafra^5,6,7, Andreas Nürnberger^2,3,7, and Oliver Speck^1,6,7,8

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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