Dynamic MRI using model-based deep learning and SToRM priors: MoDL-SToRM.

ABSTRACT:

Purpose

To introduce a novel framework to combine deep-learned priors along with complementary image regularization penalties to reconstruct free breathing & ungated cardiac MRI data from highly undersampled multi-channel measurements.

Methods

Image recovery is formulated as an optimization problem, where the cost function is the sum of data consistency term, convolutional neural network (CNN) denoising prior, and SmooThness regularization on manifolds (SToRM) prior that exploits the manifold structure of images in the dataset. An iterative algorithm, which alternates between denoizing of the image data using CNN and SToRM, and conjugate gradients (CG) step that minimizes the data consistency cost is introduced. Unrolling the iterative algorithm yields a deep network, which

SUBMITTER: Biswas S 

PROVIDER: S-EPMC7895318 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature