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20182024
most cited-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction

18 citations · 84 across the 14 of their papers we have counts for

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14 papers · 1 filter

eess.IV202213 cited

The Extreme Cardiac MRI Analysis Challenge under Respiratory Motion (CMRxMotion)

Shuo Wang, Chen Qin, Chengyan Wang +12

The quality of cardiac magnetic resonance (CMR) imaging is susceptible to respiratory motion artifacts. The model robustness of automated segmentation techniques in face of real-wo…

eess.IV20224 cited

Artificial Intelligence-Based Image Reconstruction in Cardiac Magnetic Resonance

Chen Qin, Daniel Rueckert

Artificial intelligence (AI) and Machine Learning (ML) have shown great potential in improving the medical imaging workflow, from image acquisition and reconstruction to disease di…

eess.IV2021

Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation

Shuo Wang, Chen Qin, Nicolo Savioli +6

In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures. However, due to the…

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.IV20201 cited

Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI

Chen Qin, Shuo Wang, Chen Chen +3

Image registration is an ill-posed inverse problem which often requires regularisation on the solution space. In contrast to most of the current approaches which impose explicit re…

eess.IV2020

Deep Generative Model-based Quality Control for Cardiac MRI Segmentation

Shuo Wang, Giacomo Tarroni, Chen Qin +7

In recent years, convolutional neural networks have demonstrated promising performance in a variety of medical image segmentation tasks. However, when a trained segmentation model…