Fig. 6

MR-IPT performance across different dataset sizes. To assess the impact of dataset size on restoration quality, MR-IPT was fine-tuned on the fastMRI brain dataset using an 8× Cartesian equispaced mask, with training subsets ranging from 10 to 2500 images. Each subset was randomly sampled, and the process was repeated ten times to evaluate both model performance and stability. The red line and green line represent zero-shot and fully fine-tuned performance. The results indicate that as the dataset size increases, MR-IPT exhibits significant improvements in both accuracy and consistency. Even with a limited number of training samples, the model maintains competitive performance, demonstrating its ability to leverage large-scale pretraining effectively and achieve stable, high-quality restorations in data-constrained scenarios.