Super-Resolving and Denoising 4D flow MRI of Neurofluids Using Physics-Guided Neural Networks.

ABSTRACT:

Purpose

To obtain high-resolution velocity fields of cerebrospinal fluid (CSF) and cerebral blood flow by applying a physics-guided neural network (div-mDCSRN-Flow) to 4D flow MRI.

Methods

The div-mDCSRN-Flow network was developed to improve spatial resolution and denoise 4D flow MRI. The network was trained with patches of paired high-resolution and low-resolution synthetic 4D flow MRI data derived from computational fluid dynamic simulations of CSF flow within the cerebral ventricles of five healthy cases and five Alzheimer's disease cases. The loss function combined mean squared error with a binary cross-entropy term for segmentation and a divergence-based regularization term for the conservation of mass. Performance was assessed using synthetic 4D flow MRI in one healthy

SUBMITTER: Patel NM 

PROVIDER: S-EPMC12476268 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature