  The development of magnetic resonance imaging (MRI) for medical imaging has
provided a leap forward in diagnosis, providing a safe, non-invasive
alternative to techniques involving ionising radiation exposure for diagnostic
purposes. It was described by Block and Purcel in 1946, and it was not until
1980 that the first clinical application of MRI became available. Since that
time the MRI has gone through many advances and has altered the way diagnosing
procedures are performed. Due to its ability to improve constantly, MRI has
become a commonly used practice among several specialisations in medicine.
Particularly starting 0.55T and 7T MRI technologies have pointed out enhanced
preservation of image detail and advanced tissue characterisation. This review
examines the integration of deep learning (DL) techniques into these MRI
modalities, disseminating and exploring the study applications. It highlights
how DL contributes to 0.55T and 7T MRI data, showcasing the potential of DL in
improving and refining these technologies. The review ends with a brief
overview of how MRI technology will evolve in the coming years.
