FlowMRI-Net: A generalizable self-supervised 4D flow MRI reconstruction network.

ABSTRACT:

Background

Image reconstruction from highly undersampled four-dimensional (4D) flow magnetic resonance imaging (MRI) data can be very time-consuming and may result in significant underestimation of velocities depending on regularization, thereby limiting the applicability of the method. The objective of the present work was to develop a generalizable self-supervised deep learning-based framework for fast and accurate reconstruction of highly undersampled 4D flow MRI and to demonstrate the utility of the framework for aortic and cerebrovascular applications.

Methods

The proposed deep-learning-based framework, called FlowMRI-Net, employs physics-driven unrolled optimization using a complex-valued convolutional recurrent neural network and is trained in a self-supervised manner

SUBMITTER: Jacobs L 

PROVIDER: S-EPMC12271072 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature