Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging—State-of-the-Art and Challenges.
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 an...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 1; pp. 204 - 231 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | diagnostic images review tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2023
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=162233269&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162233269 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2023 vid: 36 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 162233269 159997445 162233269 162233269 10.1007/s10278-022-00721-9 162233269 ppf: 204 ppct: 27 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging—State-of-the-Art and Challenges. aug: au: Chen, Zhaolin Pawar, Kamlesh Ekanayake, Mevan Pain, Cameron Zhong, Shenjun Egan, Gary F. affil: Monash Biomedical Imaging, Monash University, 3168, Melbourne, VIC, Australia sug: subj: Deep Learning Image Enhancement Methods Magnetic Resonance Imaging Methods Artifacts Algorithms Image Processing, Computer Assisted Radiographic Image Interpretation, Computer-Assisted Workflow ab: 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. pubtype: Academic Journal doctype: diagnostic images review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|