Erich Kobler

TL;DR: In this paper, a variational network approach is proposed to reconstruct the clinical knee imaging protocol for different acceleration factors and sampling patterns using retrospectively and prospectively undersampled data.

TL;DR: To allow fast and high‐quality reconstruction of clinical accelerated multi‐coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning.

TL;DR: In this paper, the authors evaluated the generalization ability of learned image reconstruction with respect to deviations in the acquisition settings between training and testing, and provided an outlook for the potential of transfer learning to fine-tune trainings to a particular target application using only a small number of training cases.

TL;DR: Surprisingly, in numerical experiments on image reconstruction problems it turns out that giving up exact minimization leads to a consistent performance increase, in particular in the case of convex models.

TL;DR: This paper proposes a novel learnable general-purpose regularizer exploiting recent architectural design patterns from deep learning and casts the learning problem as a discrete sampled optimal control problem, for which the adjoint state equations and an optimality condition are derived.