Deep-learning-based super-resolution technique for cine cardiac magnetic resonance

Abstract

Cine cardiac magnetic resonance (CMR) imaging is an optimal cardiac volumetric analysis method because of its high contrast resolution. However, its spatial resolution is limited owing to prolonged scanning and breath-holding. Although compressed sensing–sensitivity encoding (Compressed SENSE; CS) and its deep-learning-based advancement (SmartSpeed AI; SSAI) can reduce the scan time, the spatial resolutions remain unchanged. Herein, we investigated the effect of a deep-learning-based super-resolution technique (SmartSpeed Precise Image; SSPI) on the cine CMR visual image quality in comparison with CS and SSAI with conventional zero-filling interpolation; resultantly, the SSPI significantly improved the visual image quality scores.