  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.
