Sparsity-based methods have a long history in the field of signal processing and have been successfully applied to various image reconstruction problems.
Iterative neural networks - which contain the physical model - can overcome these issues.
The network is based on a computationally light CNN-component and a subsequent conjugate gradient (CG) method which can be jointly trained end-to-end using an efficient training strategy.
We compare the proposed reconstruction scheme to two ground truth-free reconstruction methods, namely a well known Total Variation (TV) minimization and an unsupervised adaptive Dictionary Learning (DIC) method.
In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction.
Even when trained on only one single subject without data-augmentation, our approach yields results which are similar to the ones obtained on a large training dataset.
High-resolution three-dimensional (3D) cardiovascular magnetic resonance (CMR) is a valuable medical imaging technique, but its widespread application in clinical practice is hampered by long acquisition times.