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Abstract #0953

Hybrid image and k-space deep learning reconstruction exploiting spatio-temporal redundancies for 2D cardiac CINE

Siying Xu1, Patrick Krumm2, Andreas Lingg2, Haikun Qi3, Kerstin Hammernik4,5, and Thomas Küstner1
1Medical Image And Data Analysis (MIDAS.lab), Department of Interventional and Diagnostic Radiology, University Hospital of Tuebingen, Tuebingen, Germany, 2Department of Radiology, University Hospital of Tuebingen, Tuebingen, Germany, 3School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, 4Lab for AI in Medicine, Technical University of Munich, Munich, Germany, 5Department of Computing, Imperial College London, London, United Kingdom


Cardiac CINE MR imaging allows for accurate and reproducible measurement of cardiac function but requires long scanning times. Parallel imaging and compressed sensing (CS) have reduced the acquisition time, but the possible acceleration remained limited. Deep learning-based MR image reconstructions can further increase the acceleration rates with improved image quality. In this work, we propose a novel network for retrospectively undersampled 2D cardiac CINE that a) operates in image and k-space domain with interleaved architectures and b) utilizes spatio-temporal filters for multi-coil dynamic data. The proposed network outperforms CS and some other neural networks in both qualitative and quantitative evaluations.

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