Abstract #4663

Data Consistency Networks for (Calibration-less) Accelerated Parallel MR Image Reconstruction

Jo Schlemper¹, Jinming Duan², Cheng Ouyang¹, Chen Qin¹, Jose Caballero¹, Joseph V. Hajnal³, and Daniel Rueckert¹

¹Department of Computing, Imperial College London, London, United Kingdom, ²Faculty of Medicine, Institute of Clinical Sciences, Imperial College London, London, United Kingdom, ³Biomedical Engineering, King's College London, London, United Kingdom

We present simple reconstruction networks for multi-coil data by extending deep cascade of CNN’s and exploiting the data consistency layer. In particular, we propose two variants, where one is inspired by POCSENSE and the other is calibration-less. We show that the proposed approaches are competitive relative to the state of the art both quantitatively and qualitatively.

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