ArticleNeuroImage2025
Unsupervised single-image super-resolution for infant brain MRI.
Article in NeuroImage, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
4 authors.
Funding
Abstract
Acquiring high-resolution (HR) MR images of infant brains is challenging due to lengthy scan times and limited subject compliance. Image super-resolution (SR) techniques can generate HR images from low-resolution (LR) inputs, reducing the need for extended acquisitions. However, most existing SR methods require HR images for training, limiting their practical use in real-world scenarios. To overcome this limitation, we propose an unsupervised single-image SR approach that requires only a single LR image for training. By integrating image space regularity with k-space consistency, our method enhances training stability and mitigates overfitting. Additionally, we introduce joint self-supervised learning to improve the fidelity of low-frequency content in the generated images. Our approach demonstrates both quantitative and qualitative improvements in MRI resolution for infants aged 1 week to 1 year, offering robust performance without manual hyperparameter tuning across diverse inputs. This innovation enables fully automated, high-throughput MRI resolution enhancement, addressing a critical need in pediatric neuroimaging.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.