Space Interpolation

Space interpolation, particularly in the context of magnetic resonance imaging (MRI), aims to reconstruct high-resolution images from undersampled k-space data, accelerating scan times and improving temporal resolution. Current research heavily focuses on deep learning approaches, employing convolutional neural networks (CNNs) and transformers, often incorporating the undersampling mask directly into the model architecture to improve robustness and generalization. These advancements offer significant improvements in reconstruction accuracy, especially at high acceleration rates, leading to faster and more efficient MRI scans with broader clinical applications.