Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease.

ABSTRACT:

Purpose

Real-time assessment of ventricular volumes requires high acceleration factors. Residual convolutional neural networks (CNN) have shown potential for removing artifacts caused by data undersampling. In this study, we investigated the ability of CNNs to reconstruct highly accelerated radial real-time data in patients with congenital heart disease (CHD).

Methods

A 3D (2D plus time) CNN architecture was developed and trained using synthetic training data created from previously acquired breath hold cine images from 250 CHD patients. The trained CNN was then used to reconstruct actual real-time, tiny golden angle (tGA) radial SSFP data (13 × undersampled) acquired in 10 new patients with CHD. The same real-time data was also reconstructed with compressed sensing (CS) to

SUBMITTER: Hauptmann A 

PROVIDER: S-EPMC6492123 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature