An unsupervised method for MRI recovery: deep image prior with structured sparsity.

ABSTRACT:

Objective

To propose and validate an unsupervised MRI reconstruction method that does not require fully sampled k-space data.

Materials and methods

The proposed method, deep image prior with structured sparsity (DISCUS), extends the deep image prior (DIP) by introducing group sparsity to frame-specific code vectors, enabling the discovery of a low-dimensional manifold for capturing temporal variations. DISCUS was validated using four studies: (I) simulation of a dynamic Shepp-Logan phantom to demonstrate its manifold discovery capabilities, (II) comparison with compressed sensing and DIP-based methods using simulated single-shot late gadolinium enhancement (LGE) image series from six distinct digital cardiac phantoms in terms of normalized mean square error (NMSE) and struct

SUBMITTER: Sultan MA 

PROVIDER: S-EPMC12354159 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature