Real-time deep artifact suppression using recurrent U-Nets for low-latency cardiac MRI.

ABSTRACT:

Purpose

Real-time low latency MRI is performed to guide various cardiac interventions. Real-time acquisitions often require iterative image reconstruction strategies, which lead to long reconstruction times. In this study, we aim to reconstruct highly undersampled radial real-time data with low latency using deep learning.

Methods

A 2D U-Net with convolutional long short-term memory layers is proposed to exploit spatial and preceding temporal information to reconstruct highly accelerated tiny golden radial data with low latency. The network was trained using a dataset of breath-hold CINE data (including 770 time series from 7 different orientations). Synthetic paired data were created by retrospectively undersampling the magnitude images, and the network was trained to recov

SUBMITTER: Jaubert O 

PROVIDER: S-EPMC8613539 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature