Low Field

Low-field MRI (LF-MRI), using magnets significantly weaker than those in standard MRI machines, offers increased portability and affordability, but suffers from lower image quality. Current research focuses on improving LF-MRI image quality through various computational methods, including deep learning architectures like U-Nets and diffusion models, and signal processing techniques to mitigate electromagnetic interference. These advancements aim to enhance the diagnostic capabilities of LF-MRI, particularly in resource-limited settings and for applications where high-field MRI is impractical, ultimately expanding access to neuroimaging and other medical applications. The success of these methods hinges on improving image resolution, contrast, and accuracy of automated segmentation and quantification of brain structures.