Regularization by neural style transfer for MRI field-transfer reconstruction with limited data
Year: 2025
Type: article
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-quality images across diverse anatomical planes (axial, coronal, sagittal) and noise levels, achieving superior clarity, contrast, and structural fidelity compared to lower-field references. Crucially, RNST maintains robustness even when style and content images lack exact alignment, broadening its applicability in clinical environments where precise reference matches are unavailable. By combining the strengths of NST and denoising, RNST offers a scalable, data-efficient solution for MRI field-transfer reconstruction, demonstrating significant potential for resource-limited settings.
Institution: Boston University
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,353
Awards: G1075639496, Alzheimer's Disease Research Center, 1Florida Alzheimer's Disease Research Center, Vanderbilt Alzheimer's Disease Research Center, G9000332211, Boston University Alzheimer's Disease Research Center, Yale Alzheimer Disease Research Center, UCSD Shiley-Marcos Alzheimer's Disease Research Center P30, Joint Estimate Diffusion Imaging (JEDI) for improved Tissue Characterization and Neural Connectivity in Aging and Alzheimer's Disease, Michigan Alzheimer's Disease Research Center, University of Kansas Alzheimer's Disease Research Center (KU ADRC), Alzheimer Disease Research Center, Alzheimer's Disease Research Center, Oregon Alzheimer's Disease Research Center, Alzheimer's Disease Research Center, UC Davis Alzheimer's Disease Research Center, Johns Hopkins Alzheimer's Disease Research Center, Indiana Alzheimer's Disease Research Center, Cleveland Alzheimers Disease Research Center, New Approaches to Dementia Heterogeneity, Stanford Alzheimer's Disease Research Center, Mount Sinai Alzheimer's Disease Research Center, Mayo Alzheimer's Disease Research Center, University of Washington Alzheimer's Disease Research Center, New Mexico Alzheimer's Disease Research Center, Duke/UNC Alzheimer's Disease Research Center, Wisconsin Alzheimer's Disease Research Center, Alzheimer's Disease Research Centers, UAB Alzheimer's Disease Research Center, National Alzheimer's Coordinating Center, University of Kentucky Alzheimer's Disease Research Center, NORTHWESTERN Alzheimer's Disease Research Center, Penn Alzheimer's Disease Research Center (ADRC), Alzheimer's Disease Research Center, Massachusetts Alzheimer's Disease Research Center (MADRC), South Texas Alzheimer's Disease Center, Goizueta Alzheimer's Disease Research Center, The Alzheimer's Disease Research Center at the University of California, Irvine, Rush Alzheimer's Disease Research Center
OpenAlex ID: W4408162187