Dynamic imaging is required during interventions to assess the physiological changes. Unfortunately, while achieving a high temporal resolution the spatial resolution is compromised. To overcome the spatiotemporal trade-off, in this work deep learning based super-resolution approach has been utilized and fine-tuned using prior-knowledge. 3D dynamic data for three subjects was acquired with different parameters to test the generalization capabilities of the network. Experiments were performed for different in-plane undersampling levels. A U-net based model with perceptual loss was used for training. Then, the trained network was fine-tuned using prior scan to obtain high resolution dynamic images during the inference stage.