City Research Online

Abstract

Background
Cardiovascular magnetic resonance (CMR) imaging is a powerful tool for assessing cardiac anatomy and function but remains limited by average image quality due to artefacts and long acquisition times, and complex and often too long breath-holds. Deep learning methods have recently been applied and show potential to shorten scan times by 70–80 % while improving image quality, enhancing clinical efficiency. The aim of this study is to summarise the different AI-enabled methods for improving CMR image quality, including scanning time, as a key determinant for artefact reduction.

Methods
A scoping review was conducted according to PRISMA guidelines. The articles were screened and reviewed by two researchers. A qualitative thematic synthesis was conducted and a CASP-mediated risk of bias assessment was performed.

Results
The eligible articles were thirty-one. These articles were thematically categorised in four subgroups, based on emerging themes: scan acceleration, artefact detection, artefact reduction, image reconstruction. A table with significant results for each theme has been presented and results were discussed qualitatively.

Discussion
AI demonstrated consistent improvements across the four subgroups. For scan acceleration, deep learning achieved approximately a 70–80 % reduction in scan duration maintaining or even improving image quality. For artefact detection, convolutional neural networks achieved on average a 90 % accuracy in detecting artefacts, across multiple metrics, indicating reliable artefact identification and strong agreement with human experts. AI models effectively reduce artefacts and enhance image quality, achieving consistently better reconstruction accuracy, sharper edges, and faster processing compared to conventional methods. Finally, for image reconstruction, generative adversarial networks enhanced structural similarity by approximately 56 % (SSIM 0.591 → 0.925). Together, these results illustrate the potential of AI to optimise CMR image quality.

Conclusion
AI can be an effective tool in addressing many of the CMR imaging challenges and thus improving image quality.

Publication Type:	Article
Additional Information:	This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords:	Artificial intelligence, Magnetic resonance imaging, Cardiovascular magnetic resonance imaging, Image quality optimisation, Scoping Review
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine
Departments:	School of Health & Medical Sciences School of Health & Medical Sciences > Department of Allied Health
SWORD Depositor:	Symplectic Administrator

Export

Downloads