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.