  The optimization of MRI data sampling and image reconstruction methods has
been a priority for the MRI community since the very early days of the field.
Designing an "optimal" method requires the definition of an optimality metric
(i.e., a quantitative evaluation of the "goodness" of different competing
approaches that allows an objective comparison between them). However, a key
challenge is that there are many different possible ways of quantitatively
evaluating the "goodness" of a data sampling scheme or a reconstruction result,
and there are no acquisition or reconstruction methods that are known to be
universally optimal with respect to all of these possible metrics
simultaneously. Thus, optimization of MRI methods requires a subjective choice
about what aspects of quality matter most in the context of a given MRI
experiment, and subsequently the subjective choice of an optimality metric that
hopefully does a reasonable job of quantifying those aspects of quality. Once
these choices are made, the optimization problem becomes well-defined, and it
remains to choose an algorithm that can identify data sampling or image
reconstruction methods that are optimal with respect to the chosen metric. All
of these choices are generally nontrivial.
  In this presentation, we will discuss optimal sampling and reconstruction
designs from multiple different perspectives, including ideas from information
and estimation theory and various practical perspectives.
