Abstract
Achieving sufficient spatial and temporal resolution for dynamic applications in cardiovascular magnetic resonance (CMR) imaging is a challenging task due to the inherently slow nature of CMR. In order to accelerate scans and allow improved resolution, much research over the past three decades has been aimed at developing innovative reconstruction methods that can yield high-quality images from reduced amounts of k-space data. In this review, we describe the evolution of these reconstruction techniques, with a particular focus on those advances that have shifted the dynamic reconstruction paradigm as it relates to CMR. This review discusses and explains the fundamental ideas behind the success of modern reconstruction algorithms, including parallel imaging, spatio-temporal redundancies, compressed sensing, low-rank methods and machine learning.