Scan-specific artifact reduction in k-space (SPARK) neural networks synergize with physics-based reconstruction to accelerate MRI.

ABSTRACT:

Purpose

To develop a scan-specific model that estimates and corrects k-space errors made when reconstructing accelerated MRI data.

Methods

Scan-specific artifact reduction in k-space (SPARK) trains a convolutional-neural-network to estimate and correct k-space errors made by an input reconstruction technique by back-propagating from the mean-squared-error loss between an auto-calibration signal (ACS) and the input technique's reconstructed ACS. First, SPARK is applied to generalized autocalibrating partially parallel acquisitions (GRAPPA) and demonstrates improved robustness over other scan-specific models, such as robust artificial-neural-networks for k-space interpolation (RAKI) and residual-RAKI. Subsequent experiments demonstrate that SPARK synergizes with residual-RAKI

SUBMITTER: Arefeen Y 

PROVIDER: S-EPMC8627503 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature