Selection of K‐space samples in localized MR spectroscopy of arbitrary volumes of interest

A number of image modeling techniques have been proposed to represent magnetic resonance spectroscopic images. The goal of these techniques is to reduce the amount of data and thus the time required to reconstruct the image. However, little attention has been given to the choice of k-space samples. The combination of samples acquired has a great effect on the quality of the reconstructed image. A new method for optimizing the choice of k-space samples--in which samples from a candidate set are sequentially eliminated in a computationally efficient manner until the desired number remain--is presented and demonstrated by means of simulations.