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20172020
most citeddAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance

22 citations · 75 across the 9 of their papers we have counts for

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9 papers · 1 filter

eess.IV2020

Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction

Chen Qin, Jinming Duan, Kerstin Hammernik +7

Purpose: To introduce a novel deep learning based approach for fast and high-quality dynamic multi-coil MR reconstruction by learning a complementary time-frequency domain network…

eess.IV20202 cited

Deep Network Interpolation for Accelerated Parallel MR Image Reconstruction

Chen Qin, Jo Schlemper, Kerstin Hammernik +3

We present a deep network interpolation strategy for accelerated parallel MR image reconstruction. In particular, we examine the network interpolation in parameter space between a…

eess.IV201918 cited

-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…

eess.IV20199 cited

-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…

eess.IV20193 cited

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,…

eess.IV2019

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…