Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging.

Journal: Magnetic resonance in medicine

Published Date: Sep 18, 2018

Abstract

PURPOSE: To develop an improved k-space reconstruction method using scan-specific deep learning that is trained on autocalibration signal (ACS) data.

Authors

Mehmet Akçakaya

Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota.
Steen Moeller

Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota.
Sebastian Weingärtner

Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Germany.
Kâmil Uğurbil

Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota.

Keywords

Adult Algorithms Brain Brain Mapping Databases, Factual Deep Learning Female Heart Humans Image Enhancement Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Magnetic Resonance Imaging Male Middle Aged Neural Networks, Computer Phantoms, Imaging Radionuclide Imaging Young Adult

External Resources

View on PubMed Access via DOI PubMed (30277269)

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