Undersampling the k-space during MR acquisitions saves time, however results in an ill-
posed inversion problem, leading to an infinite set of images as possible solutions.
Traditionally, this is tackled as a reconstruction problem by searching for a single" best"
image out of this solution set according to some chosen regularization or prior. This
approach, however, misses the possibility of other solutions and hence ignores the
uncertainty in the inversion process. In this paper, we propose a method that instead returns …

Undersampling the k-space during MR acquisitions saves time, however results in an ill-
posed inversion problem, leading to an infinite set of images as possible solutions.
Traditionally, this is tackled as a reconstruction problem by searching for a single “best”
image out of this solution set according to some chosen regularization or prior. This
approach, however, misses the possibility of other solutions and hence ignores the
uncertainty in the inversion process. In this paper, we propose a method that instead returns …