Data · dataset · 2027
Information Filter-based Diffusion Transformer for medical image super-resolution
Listed in ZivaHub
Medical image super-resolution (SR) aims to recover fine anatomical details from low-resolution (LR) scans, yet obtaining high-resolution images is often challenging due to clinical limitations, such as acquisition time and radiation exposure.
Description
To address the limitation of conventional diffusion models with uniform noise perturbation in preserving sparse but clinically important structures, we propose an Information Filter-based medical image SR Network (IFNet), which incorporates region-aware guidance into the diffusion process.
Specifically, we design a sparsity extractor to identify information-rich regions and generate a sparse structural mask, which is subsequently utilized to guide uncertainty-aware noise modulation and the denoising process. Furthermore, we develop an Information Filter-based Diffusion Transformer (IF-DiT) to enhance feature representation by adaptively emphasizing diagnostically relevant regions, thereby improving both reconstruction accuracy and computational efficiency.
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Extensive experiments are conducted on three public medical datasets, including IXI, BraTS2017, and Coltea-Lung-CT-100 W, under both (Formula presented) and (Formula presented) SR settings. The experimental results demonstrate that IFNet consistently achieves superior performance compared with recent state-of-the-art methods. Specifically, IFNet obtains PSNR/SSIM values of 50.27/0.9960 on IXI at (Formula presented) SR, 39.59/0.9860 on BraTS2017 at (Formula presented) SR, and 58.64/0.9992 on Coltea-Lung-CT-100 W at (Formula presented) SR.
Compared with the strongest competing approaches, IFNet improves PSNR by up to 6.88 dB on IXI at (Formula presented) SR and 2.44 dB on BraTS2017 at (Formula presented) SR, while achieving consistently competitive SSIM performance across different datasets and scaling factors. These results demonstrate that IFNet effectively preserves fine anatomical structures and enhances reconstruction fidelity for medical image SR.<p></p>
Links
Where it is published
- DOI doi.org/10.26187/deakin.34020477 ↗
DOI / persistent id · from zivahub uct ac za
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from zivahub uct ac za
Topics
- From keywords
- Biomedical imaging · Computer Science & AI · Computer vision and multimedia computation · Data management and data science · Earth & Environmental Science · Library and information studies · Medicine & Health
- Inferred from text
- Image 75%
Provenance · 1 source records, 13 field assertions
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| title | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/title |