A Deep Learning Approach Utilizing Covariance Matrix Analysis for the
  ISBI Edited MRS Reconstruction Challenge
Julian P. Merkofer Dennis M. J. van de Sande Sina Amirrajab Gerhard S. Drenthen Mitko Veta Jacobus F. A. Jansen Marcel Breeuwer Ruud J. G. van Sloun
This work proposes a method to accelerate the acquisition of high-quality
edited magnetic resonance spectroscopy (MRS) scans using machine learning
models taking the sample covariance matrix as input. The method is invariant to
the number of transients and robust to noisy input data for both synthetic as
well as in-vivo scenarios.