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Updated: Jan 9, 2026
Assessing Cortical Cerebral Microinfarcts on High Resolution MR Images
Published on: November 20, 2015
The role of AI in optimizing CMR image quality: A scoping review
Daniele Silipo1, Julien Greggio2, Christina Malamateniou3
1Alliance Medical- City St George's, University of London, Northampton Square, London EC1V 0HB, United Kingdom.
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.
