Cardiac MRI is clinically valuable but inherently slow, requiring many sequential measurements per frame to build a complete image. In dynamic MRI, where each time-frame must be acquired separately, this becomes especially limiting. This work presents a pipeline built on top of TEAM-PILOT model that learns to interpolate videos directly in the frequency domain, generating phase-consistent intermediate frames and effectively enlarging the training dataset without any new acquisitions. The approach addresses two challenges simultaneously: accelerating scan time and alleviating data scarcity, which is a general bottleneck in medical imaging deep learning. We demonstrate that combining 2-shot acquisition (proportional to the amount of signals) with 4 times temporal densification matches standard 8-shot reconstruction quality across multiple state-of-the-art architectures, achieving a 4 times reduction in scan time with no significant loss in image quality.