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
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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: | Professional Services > Advanced Research Computing @ Cardiff (ARCCA) Schools > Computer Science & Informatics Schools > 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: | 20 Jun 2025 14:00 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/170044 |
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