[2604.00205] Unsupervised 4D Flow MRI Velocity Enhancement and Unwrapping Using Divergence-Free Neural Networks

Computer Science > Machine Learning arXiv:2604.00205 (cs) [Submitted on 31 Mar 2026] Title:Unsupervised 4D Flow MRI Velocity Enhancement and Unwrapping Using Divergence-Free Neural Networks Authors:Javier Bisbal, Julio Sotelo, Hernán Mella, Oliver Welin Odeback, Joaquín Mura, David Marlevi, Junya Matsuda, Kotomi Iwata, Tetsuro Sekine, Cristian Tejos, Sergio Uribe View a PDF of the paper titled Unsupervised 4D Flow MRI Velocity Enhancement and Unwrapping Using Divergence-Free Neural Networks, by Javier Bisbal and 10 other authors View PDF HTML (experimental) Abstract:This work introduces an unsupervised Divergence and Aliasing-Free neural network (DAF-FlowNet) for 4D Flow Magnetic Resonance Imaging (4D Flow MRI) that jointly enhances noisy velocity fields and corrects phase wrapping artifacts. DAF-FlowNet parameterizes velocities as the curl of a vector potential, enforcing mass conservation by construction and avoiding explicit divergence-penalty tuning. A cosine data-consistency loss enables simultaneous denoising and unwrapping from wrapped phase images. On synthetic aortic 4D Flow MRI generated from computational fluid dynamics, DAF-FlowNet achieved lower errors than existing techniques (up to 11% lower velocity normalized root mean square error, 11% lower directional error, and 44% lower divergence relative to the best-performing alternative across noise levels), with robustness to moderate segmentation perturbations. For unwrapping, at peak velocity/velocity-encoding ...