Accelerated cardiac magnetic resonance imaging using deep learning for volumetric assessment in children.

ABSTRACT:

Background

Ventricular volumetry using a short-axis stack of two-dimensional (D) cine balanced steady-state free precession (bSSFP) sequences is crucial in any cardiac magnetic resonance imaging (MRI) examination. This task becomes particularly challenging in children due to multiple breath-holds.

Objective

To assess the diagnostic performance of accelerated 3-RR cine MRI sequences using deep learning reconstruction compared with standard 2-D cine bSSFP sequences.

Material and methods

Twenty-nine consecutive patients (mean age 11 ± 5, median 12, range 1-17 years) undergoing cardiac MRI were scanned with a conventional segmented 2-D cine and a deep learning accelerated cine (three heartbeats) acquisition on a 1.5-tesla scanner. Short-axis volumetrics were performed (se

SUBMITTER: Koechli M 

PROVIDER: S-EPMC11377620 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature