Learning a variational network for reconstruction of accelerated MRI data

20171,660 citationsJournal Article

Authors

Kerstin Hammernik · Graz University of Technology

Teresa Klatzer · Graz University of Technology

Erich Kobler · Graz University of Technology

Michael P. Recht · New York University

Daniel K. Sodickson · New York University

Thomas Pock · Austrian Institute of Technology

Florian Knöll ·

Abstract

Variational network reconstructions preserve the natural appearance of MR images as well as pathologies that were not included in the training data set. Due to its high computational performance, that is, reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow. Magn Reson Med 79:3055-3071, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Topics & Keywords

Advanced MRI Techniques and Applications Medical Imaging Techniques and Applications Cardiac Imaging and Diagnostics

Publication Details

Published in: Magnetic Resonance in Medicine

Volume 79, Issue 6, pp. 3055-3071

DOI: 10.1002/mrm.26977

Field-Weighted Citation Impact: 96.94

Command Palette

Learning a variational network for reconstruction of accelerated MRI data

Authors

Abstract

Topics & Keywords

Publication Details