Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction.

ABSTRACT:

Purpose

To propose a novel combined parallel imaging and deep learning-based reconstruction framework for robust reconstruction of highly accelerated 2D cardiac cine MRI data.

Methods

We propose DL-ESPIRiT, an unrolled neural network architecture that utilizes an extended coil sensitivity model to address SENSE-related field-of-view (FOV) limitations in previously proposed deep learning-based reconstruction frameworks. Additionally, we propose a novel neural network design based on (2+1)D spatiotemporal convolutions to produce more accurate dynamic MRI reconstructions than conventional 3D convolutions. The network is trained on fully sampled 2D cardiac cine datasets collected from 11 healthy volunteers with IRB approval. DL-ESPIRiT is compared against a state-of-the-art para

SUBMITTER: Sandino CM 

PROVIDER: S-EPMC7722220 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature