Clement David-Olawade, Aanuoluwapo, Olawade, David B., Vanderbloemen, Laura, Rotifa, Oluwayomi B., Fidelis, Sandra Chinaza, Egbon, Eghosasere, Akpan, Akwaowo Owoidighe, Adeleke, Sola, Ghose, Aruni, Boussios, Stergios and others. (2025) AI-Driven Advances in Low-Dose Imaging and Enhancement — A Review. Diagnostics, 15 (6). Article Number 689. ISSN 2075-4418. (doi:10.3390/diagnostics15060689) (KAR id:109583)
PDF
Publisher pdf
Language: English
This work is licensed under a Creative Commons Attribution 4.0 International License.
|
|
Download this file (PDF/2MB) |
Preview |
Request a format suitable for use with assistive technology e.g. a screenreader | |
Official URL: https://doi.org/10.3390/diagnostics15060689 |
Abstract
The widespread use of medical imaging techniques such as X-rays and computed tomography (CT) has raised significant concerns regarding ionizing radiation exposure, particularly among vulnerable populations requiring frequent imaging. Achieving a balance between high-quality diagnostic imaging and minimizing radiation exposure remains a fundamental challenge in radiology. Artificial intelligence (AI) has emerged as a transformative solution, enabling low-dose imaging protocols that enhance image quality while significantly reducing radiation doses. This review explores the role of AI-assisted low-dose imaging, particularly in CT, X-ray, and magnetic resonance imaging (MRI), highlighting advancements in deep learning models, convolutional neural networks (CNNs), and other AI-based approaches. These technologies have demonstrated substantial improvements in noise reduction, artifact removal, and real-time optimization of imaging parameters, thereby enhancing diagnostic accuracy while mitigating radiation risks. Additionally, AI has contributed to improved radiology workflow efficiency and cost reduction by minimizing the need for repeat scans. The review also discusses emerging directions in AI-driven medical imaging, including hybrid AI systems that integrate post-processing with real-time data acquisition, personalized imaging protocols tailored to patient characteristics, and the expansion of AI applications to fluoroscopy and positron emission tomography (PET). However, challenges such as model generalizability, regulatory constraints, ethical considerations, and computational requirements must be addressed to facilitate broader clinical adoption. AI-driven low-dose imaging has the potential to revolutionize radiology by enhancing patient safety, optimizing imaging quality, and improving healthcare efficiency, paving the way for a more advanced and sustainable future in medical imaging.
Item Type: | Article |
---|---|
DOI/Identification number: | 10.3390/diagnostics15060689 |
Uncontrolled keywords: | low-dose imaging, radiology, deep learning, artificial intelligence, radiation safety, CT scans |
Subjects: |
R Medicine T Technology > TJ Mechanical engineering and machinery |
Institutional Unit: | Schools > Kent and Medway Medical School |
Former Institutional Unit: |
Divisions > Division of Natural Sciences > Kent and Medway Medical School
|
Funders: | University of Kent (https://ror.org/00xkeyj56) |
SWORD Depositor: | JISC Publications Router |
Depositing User: | JISC Publications Router |
Date Deposited: | 29 Apr 2025 09:28 UTC |
Last Modified: | 20 May 2025 10:00 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/109583 (The current URI for this page, for reference purposes) |
- Link to SensusAccess
- Export to:
- RefWorks
- EPrints3 XML
- BibTeX
- CSV
- Depositors only (login required):