Deep Dive into MRI: Exploring Deep Learning Applications in 0.55T and 7T MRI

arXiv (Cornell University)

Abstract

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.

Authors 4

1. André Filipe Ferreira
2. Behrus Puladi Aachen Department of Oral and Maxillofacial Surgery Institute of Medical Informatics

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Oral and Maxillofacial Surgery , University Hospital RWTH Aachen , Aachen , Germany

   Institute of Medical Informatics , University Hospital RWTH Aachen , Aachen , Germany

3. Jan Egger
4. Victor Alves

   University of Minho

   Affiliation as printed

   Center Algoritmi / LASI , University of Minho , Braga , Portugal