Abstract
MRI acquisition and reconstruction research has transformed into a computation-driven field. As methods become more sophisticated, compute-heavy, and data-hungry, efforts to reproduce them become more difficult. While the computational MRI research community has made great leaps toward reproducible computational science, there are few tailored guidelines or standards for users to follow. In this review article, we develop a cookbook to facilitate reproducible research for MRI acquisition and reconstruction. Like any good cookbook, we list several recipes, each providing a basic standard on how to make computational MRI research reproducible. And like cooking, we show example flavours where reproducibility may fail due to under-specification. We structure the article, so that the cookbook itself serves as an example of reproducible research by providing sequence and reconstruction definitions as well as data to reproduce the experimental results in the figures. We also propose a community-driven effort to compile an evolving list of best practices for making computational MRI research reproducible.