activity
20182021
most citedLAPNet: Non-rigid Registration derived in k-space for Magnetic Resonance Imaging

46 citations · 46 across the 1 of their papers we have counts for

collaborators

5 papers

eess.IV202146 cited

LAPNet: Non-rigid Registration derived in k-space for Magnetic Resonance Imaging

Thomas Küstner, Jiazhen Pan, Haikun Qi +7

Physiological motion, such as cardiac and respiratory motion, during Magnetic Resonance (MR) image acquisition can cause image artifacts. Motion correction techniques have been pro…

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…

cs.CV2018

Retrospective correction of Rigid and Non-Rigid MR motion artifacts using GANs

Karim Armanious, Sergios Gatidis, Konstantin Nikolaou +2

Motion artifacts are a primary source of magnetic resonance (MR) image quality deterioration with strong repercussions on diagnostic performance. Currently, MR motion correction is…

cs.CV2018

A Machine-learning framework for automatic reference-free quality assessment in MRI

Thomas Küstner, Sergios Gatidis, Annika Liebgott +9

Magnetic resonance (MR) imaging offers a wide variety of imaging techniques. A large amount of data is created per examination which needs to be checked for sufficient quality in o…

cs.CV2018

MedGAN: Medical Image Translation using GANs

Karim Armanious, Chenming Jiang, Marc Fischer +4

Image-to-image translation is considered a new frontier in the field of medical image analysis, with numerous potential applications. However, a large portion of recent approaches…