K Space

K-space represents the raw data acquired by magnetic resonance imaging (MRI) scanners in the Fourier domain, and research focuses on efficiently processing this data to improve image quality and reduce scan times. Current efforts center on deep learning models, including transformers and convolutional neural networks, to reconstruct high-fidelity images from undersampled k-space data, often incorporating techniques like implicit neural representations and attention mechanisms to enhance performance. These advancements are significant because they promise faster, more accessible MRI scans, potentially improving diagnostic capabilities and expanding the clinical applications of this crucial imaging modality.