Sparsity adaptive reconstruction for highly accelerated cardiac MRI.

ABSTRACT:

Purpose

To enable parameter-free, accelerated cardiovascular magnetic resonance (CMR).

Methods

Regularized reconstruction methods, such as compressed sensing (CS), can significantly accelerate MRI data acquisition but require tuning of regularization weights. In this work, a technique, called Sparsity adaptive Composite Recovery (SCoRe) that exploits sparsity in multiple, disparate sparsifying transforms is presented. A data-driven adjustment of the relative contributions of different transforms yields a parameter-free CS recovery process. SCoRe is validated in a dynamic digital phantom as well as in retrospectively and prospectively undersampled cine CMR data.

Results

The results from simulation and 6 retrospectively undersampled datasets indicate that SCoRe with aut

SUBMITTER: Chen C 

PROVIDER: S-EPMC6435424 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature