Magnetic resonance parameter mapping using model-guided self-supervised deep learning.

ABSTRACT:

Purpose

To develop a model-guided self-supervised deep learning MRI reconstruction framework called reference-free latent map extraction (RELAX) for rapid quantitative MR parameter mapping.

Methods

Two physical models are incorporated for network training in RELAX, including the inherent MR imaging model and a quantitative model that is used to fit parameters in quantitative MRI. By enforcing these physical model constraints, RELAX eliminates the need for full sampled reference data sets that are required in standard supervised learning. Meanwhile, RELAX also enables direct reconstruction of corresponding MR parameter maps from undersampled k-space. Generic sparsity constraints used in conventional iterative reconstruction, such as the total variation constraint, can be addi

SUBMITTER: Liu F 

PROVIDER: S-EPMC9185837 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature