Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges

Zhaolin Chen, Kamlesh Pawar, Mevan Ekanayake, Cameron Pain, Shenjun Zhong, Gary F. Egan

Research output: Contribution to journalReview ArticleResearchpeer-review

29 Citations (Scopus)

Abstract

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical diagnoses and research which underpin many recent breakthroughs in medicine and biology. The post-processing of reconstructed MR images is often automated for incorporation into MRI scanners by the manufacturers and increasingly plays a critical role in the final image quality for clinical reporting and interpretation. For image enhancement and correction, the post-processing steps include noise reduction, image artefact correction, and image resolution improvements. With the recent success of deep learning in many research fields, there is great potential to apply deep learning for MR image enhancement, and recent publications have demonstrated promising results. Motivated by the rapidly growing literature in this area, in this review paper, we provide a comprehensive overview of deep learning-based methods for post-processing MR images to enhance image quality and correct image artefacts. We aim to provide researchers in MRI or other research fields, including computer vision and image processing, a literature survey of deep learning approaches for MR image enhancement. We discuss the current limitations of the application of artificial intelligence in MRI and highlight possible directions for future developments. In the era of deep learning, we highlight the importance of a critical appraisal of the explanatory information provided and the generalizability of deep learning algorithms in medical imaging.

Original languageEnglish
Pages (from-to)204-230
Number of pages27
JournalJournal of Digital Imaging
Volume36
DOIs
Publication statusPublished - 2023

Keywords

  • Artefact correction
  • Image enhancement
  • Magnetic resonance imaging
  • Noise
  • Post-processing
  • Super-resolution

Cite this