4.6k citations · 5k across the 26 of their papers we have counts for
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-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction
Kerstin Hammernik, Jo Schlemper, Chen Qin +3
Purpose: To systematically investigate the influence of various data consistency layers, (semi-)supervised learning and ensembling strategies, defined in a -net, for accelerated…
-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction
Jo Schlemper, Chen Qin, Jinming Duan +2
We explore an ensembled -net for fast parallel MR imaging, including parallel coil networks, which perform implicit coil weighting, and sensitivity networks, involving explicit…
Data consistency networks for (calibration-less) accelerated parallel MR image reconstruction
Jo Schlemper, Jinming Duan, Cheng Ouyang +4
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,…
dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance
Jo Schlemper, Ilkay Oksuz, James R. Clough +5
AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited. We present dAUTOMAP, a novel way for decomposing the…
Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image
Chen Qin, Wenjia Bai, Jo Schlemper +4
Accelerating the acquisition of magnetic resonance imaging (MRI) is a challenging problem, and many works have been proposed to reconstruct images from undersampled k-space data. H…
Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation
Chen Chen, Cheng Ouyang, Giacomo Tarroni +4
In this work, we present a fully automatic method to segment cardiac structures from late-gadolinium enhanced (LGE) images without using labelled LGE data for training, but instead…