Deep Dive into MRI: Exploring Deep Learning Applications in 0.55T and 7T MRI
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Abstract (arXiv)
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.
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References · 5
- 202120-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstructioncited by 9
- 2024Deep Learning for Accelerated and Robust MRI Reconstruction: a Reviewcited by 9
- 2020Deep Cerebellar Nuclei Segmentation via Semi-Supervised Deep Context-Aware Learning from 7T Diffusion MRIcited by 2
- 2024Deep Learning-based Accelerated MR Cholangiopancreatography without Fully-sampled Datacited by 2
- 2023An automated pipeline for quantitative T2* fetal body MRI and segmentation at low fieldcited by 1
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