  Dynamic imaging is a beneficial tool for interventions to assess
physiological changes. Nonetheless during dynamic MRI, while achieving a high
temporal resolution, the spatial resolution is compromised. To overcome this
spatio-temporal trade-off, this research presents a super-resolution (SR) MRI
reconstruction with prior knowledge based fine-tuning to maximise spatial
information while reducing the required scan-time for dynamic MRIs. An U-Net
based network with perceptual loss is trained on a benchmark dataset and
fine-tuned using one subject-specific static high resolution MRI as prior
knowledge to obtain high resolution dynamic images during the inference stage.
3D dynamic data for three subjects were acquired with different parameters to
test the generalisation capabilities of the network. The method was tested for
different levels of in-plane undersampling for dynamic MRI. The reconstructed
dynamic SR results after fine-tuning showed higher similarity with the high
resolution ground-truth, while quantitatively achieving statistically
significant improvement. The average SSIM of the lowest resolution experimented
during this research (6.25~\% of the k-space) before and after fine-tuning were
0.939 $\pm$ 0.008 and 0.957 $\pm$ 0.006 respectively. This could theoretically
result in an acceleration factor of 16, which can potentially be acquired in
less than half a second. The proposed approach shows that the super-resolution
MRI reconstruction with prior-information can alleviate the spatio-temporal
trade-off in dynamic MRI, even for high acceleration factors.
