Low Field Magnetic Resonance Imaging

Low-field magnetic resonance imaging (MRI) offers a cost-effective and accessible alternative to high-field MRI, but suffers from lower resolution and signal-to-noise ratio. Current research focuses on improving low-field image quality using deep learning techniques, such as generative adversarial networks (GANs), denoising autoencoders (DAEs), and score-based generative models, often incorporating meta-learning or teacher-student approaches to overcome the challenges of limited training data and the ill-posed nature of image reconstruction. These advancements aim to enhance the diagnostic value of low-field MRI, particularly in resource-limited settings and for patient populations sensitive to high-field scanners, by improving image clarity and enabling more accurate segmentation and analysis.