Deep Learning has received much attention in the past few decades and is recognized as being one of the most important methodologies for potential breakthroughs in the medical field. This work is a preliminary attempt to introduce a multifaceted view on static and dynamic learned MRI reconstruction. First, we investigate the problem of domain shift in the context of state-of-the-art Magnetic Resonance Imaging (MRI) reconstruction networks with respect to variations in training data. We provide visualization tools and support our findings with statistical analysis for the networks evaluated    on the 1.5T/3T fastMRI knee and neuro data. We observe that the signal-to-noise ratio of the examined sequences plays an essential role, and we statistically prove the hypothesis that both the type and amount of training data are less important for low acceleration factors. Finally, a visualization tool facilitating the examination of the networks’ performance on each individual subject of the fastMRI data is provided. In the second part of this research paper, we adapt existing methodologies and findings in MRI reconstruction to novel 7T MRI reconstruction of dynamic cardiac processes. Despite the less severe degradation of image quality at high acceleration factors for higher magnetic field strengths, 7T MRIs, especially those with an additional temporal dimension, have rarely been studied directly in the field of learning-based undersampled MRI reconstruction. In this context, we identify the substantial impact certain neural network architectures and configurations have on reconstruction quality. In summary, we conclude that findings on domain shift, reconstruction quality and factors impacting network performance in 1.5T and 3T MRIs can also be translated to other magnetic field strengths, facilitating the transition to ultra-high-field (7T) and low-field MRI.