High Field

High-field magnetic resonance imaging (MRI), particularly at 7T and above, offers superior image quality but presents challenges like radiofrequency field inhomogeneities and increased scan times. Current research focuses on mitigating these limitations using deep learning techniques, including convolutional neural networks (like V-Net and U-Net variants) and transformers, to improve image quality, enable accurate segmentation of brain structures (e.g., subthalamic nucleus, white matter hyperintensities), and even synthesize high-field images from lower-field acquisitions. These advancements are significantly impacting neuroimaging research by improving diagnostic accuracy, facilitating more precise neurosurgical planning, and enabling studies of brain structure and function at unprecedented detail.