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Abstract #3363

Densely Connected Iterative Network for Sparse MRI Reconstruction

Itzik Malkiel1, Sangtae Ahn2, Zac Slavens3, Valentina Taviani4, and Christopher J Hardy2

1GE Global Research, Herzliya, Israel, 2GE Global Research, Niskayuna, NY, United States, 3GE Healthcare, Waukesha, WI, United States, 4GE Healthcare, Menlo Park, CA, United States

We propose a densely connected deep convolutional network for reconstruction of highly undersampled MR images. Eight-channel 2D brain data with fourfold undersampling were used as inputs, and the corresponding fully-sampled reconstructed images as references for training. The algorithm produced notably higher-quality images than state-of-the-art parallel imaging and compressed sensing methods, both in terms of reconstruction error and perceptual quality. The dense architecture was found to significantly outperform a similar network without dense connections.

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