MIQANet: A novel dual-branch deep learning framework for MRI image quality assessment

Ma, Yueran, Wang, Huasheng, Wu, Yingying, Tanguy, Jean-Yves, White, Richard, Wardle, Phillip, Krupinski, Elizabeth, Corcoran, Padraig ORCID: https://orcid.org/0000-0001-9731-3385 and Liu, Hantao ORCID: https://orcid.org/0000-0003-4544-3481 2026. MIQANet: A novel dual-branch deep learning framework for MRI image quality assessment. IEEE Transactions on Circuits and Systems for Video Technology , p. 1. 10.1109/tcsvt.2026.3656671

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Abstract

Image quality assessment (IQA) algorithms have significantly advanced over the past two decades, primarily focusing on natural images. However, applying these methods directly to medical imaging often yields suboptimal performance due to inherent differences such as the structural complexity of medical images and the limited availability of annotated databases. In this study, we conduct a comprehensive evaluation of state-of-the-art IQA methods, including 29 traditional full-reference (FR), 4 traditional no-reference (NR), and 9 deep learning-based approaches, to assess their effectiveness in the context of medical imaging. Our evaluation is performed on a recently developed MRI image quality assessment benchmark, revealing critical performance gaps in existing methods. Building on these findings, we propose a novel dual-branch deep learning framework specifically designed for medical IQA (MIQANet). The proposed approach effectively combines global contextual information with local structural details, enhancing the model’s ability to capture subtle degradations and structural inconsistencies in MRI scans. Experiential results demonstrate the superiority of our approach over existing methods, providing valuable theoretical and practical insights for enhancing quality assessment of medical images.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: URL: https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html, Start Date: 2026-01-01
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1051-8215
Date of First Compliant Deposit:	6 February 2026
Date of Acceptance:	19 January 2026
Last Modified:	06 Feb 2026 11:45
URI:	https://orca.cardiff.ac.uk/id/eprint/184478

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