Network Accelerated Motion Estimation and Reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model.

ABSTRACT:

Purpose

We introduce and validate a scalable retrospective motion correction technique for brain imaging that incorporates a machine learning component into a model-based motion minimization.

Methods

A convolutional neural network (CNN) trained to remove motion artifacts from 2D T₂ -weighted rapid acquisition with refocused echoes (RARE) images is introduced into a model-based data-consistency optimization to jointly search for 2D motion parameters and the uncorrupted image. Our separable motion model allows for efficient intrashot (line-by-line) motion correction of highly corrupted shots, as opposed to previous methods which do not scale well with this refinement of the motion model. Final image generation incorporates the motion parameters within a model-based

SUBMITTER: Haskell MW 

PROVIDER: S-EPMC6626557 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature