Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited Data.

ABSTRACT: Recent advances in MRI reconstruction have demonstrated remarkable success through deep learning-based models. However, most existing methods rely heavily on large-scale, task-specific datasets, making reconstruction in data-limited settings a critical yet underexplored challenge. While regularization by denoising (RED) leverages denoisers as priors for reconstruction, we propose Regularization by Neural Style Transfer (RNST), a novel framework that integrates a neural style transfer (NST) engine with a denoiser to enable magnetic field-transfer reconstruction. RNST generates high-field-quality images from low-field inputs without requiring paired training data, leveraging style priors to address limited-data settings. Our experiment results demonstrate RNST's ability to reconstruct high-q

SUBMITTER: Shen G 

PROVIDER: S-EPMC11875279 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature