A miniature U-net for k-space-based parallel magnetic resonance imaging reconstruction with a mixed loss function.

ABSTRACT:

Background

Deep learning-based magnetic resonance imaging (MRI) methods require in most cases a separate dataset with thousands of images for each anatomical site to train the network model. This paper proposes a miniature U-net method for k-space-based parallel MRI where the network model is trained individually for each scan using scan-specific autocalibrating signal data.

Methods

The original U-net was tailored with fewer layers and channels, and the network was trained using the autocalibrating signal data with a mixing loss function involving magnitude loss and phase loss. The performance of the proposed method was measured using both phantom and in vivo datasets compared to scan-specific robust artificial-neural-networks for k-space interpolation (

SUBMITTER: Xu L 

PROVIDER: S-EPMC9403593 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature