A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction

Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate the data acquisition process. In particular, we address the case where data is acquired using aggressive Cartesian undersampling... (read more)

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METHOD TYPE
Convolution
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