Improving high-frequency details in cerebellum for brain MRI super-resolution

Wang, Hanzhi ORCID: https://orcid.org/0000-0002-7714-4606, Marshall, Andrew ORCID: https://orcid.org/0000-0003-2789-1395, Jones, Derek ORCID: https://orcid.org/0000-0003-4409-8049 and Li, Yuhua ORCID: https://orcid.org/0000-0003-2913-4478 2024. Improving high-frequency details in cerebellum for brain MRI super-resolution. Presented at: Conference on ICT Solutions for eHealth (ICTS4eHealth 2024), Paris, France, 26 - 29 June 2024. 2024 IEEE Symposium on Computers and Communications (ISCC). IEEE, pp. 1-7. 10.1109/ISCC61673.2024.10733580

Preview

PDF - Accepted Post-Print Version Download (2MB) | Preview

Abstract

Deep-learning-based single-image super-resolution models are typically trained using image patches, rather than the whole images, due to hardware limits. Since different brain regions have disparate structures and their size varies, such as the cerebrum and cerebellum, models trained using image patches can be dominated by the structures of the larger brain regions and ignore the fine-grained details in smaller areas. In this paper, we first evaluate several previously proposed models using more blurry low-resolution images than previous studies, as input. Then, we propose an effective approach for the conventional patch-based strategy by balancing the proportion of patches containing high-frequency details. This makes the model focus more on high-frequency information in tiny regions, especially for the cerebellum. Compared with the conventional patch-based strategy, the resultant super-resolved image from our approach achieves comparable image quality in the whole brain. In contrast, it improves significantly on the high-frequency details in the cerebellum.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics Psychology
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	IEEE
ISBN:	979-8-3503-5423-2
Date of First Compliant Deposit:	30 July 2024
Date of Acceptance:	16 May 2024
Last Modified:	14 Nov 2024 09:45
URI:	https://orca.cardiff.ac.uk/id/eprint/170044

Actions (repository staff only)

Edit Item