We propose a deep learning approach for MR fingerprinting that jointly learns a low-dimensional representation of the fingerprints and estimates biophysical parameters from this subspace. In contrast to SVD-based projections, which are agnostic to the estimation task, the learned subspace is optimized to maximize information content about the parameters of interest. Incorporating the learned basis functions in the forward imaging operator suppresses undersampling artifacts and increases computational efficiency.
This abstract and the presentation materials are available to members only; a login is required.