MRI Reconstruction

MRI reconstruction aims to create high-quality images from incomplete or undersampled data, significantly reducing scan times and improving patient experience. Current research heavily utilizes deep learning, employing various architectures like diffusion models, transformers, and convolutional neural networks, often incorporating techniques such as multi-modal fusion and uncertainty quantification. These advancements are crucial for improving the efficiency and diagnostic capabilities of MRI, impacting both clinical practice and research by enabling faster scans, higher resolution images, and more robust quantitative analysis.