A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction

@inproceedings{Schlemper2017ADC,
  title={A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction},
  author={Jo Schlemper and Jose Caballero and Joseph V. Hajnal and Anthony N. Price and Daniel Rueckert},
  booktitle={Information Processing in Medical Imaging},
  year={2017}
}
The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow. Inspired by recent advances in deep learning, we propose a framework for reconstructing MR images from undersampled data using a deep cascade of convolutional neural networks to accelerate the data acquisition process. We show that for Cartesian undersampling of 2D cardiac MR images, the proposed method outperforms the state-of-the-art compressed sensing approaches, such as dictionary learning-based MRI (DLMRI… 

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