  Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard
for cardiac function evaluation. However, cine CMR acquisition is inherently
slow and in recent decades considerable effort has been put into accelerating
scan times without compromising image quality or the accuracy of derived
results. In this paper, we present a fully-automated, quality-controlled
integrated framework for reconstruction, segmentation and downstream analysis
of undersampled cine CMR data. The framework enables active acquisition of
radial k-space data, in which acquisition can be stopped as soon as acquired
data are sufficient to produce high quality reconstructions and segmentations.
This results in reduced scan times and automated analysis, enabling robust and
accurate estimation of functional biomarkers. To demonstrate the feasibility of
the proposed approach, we perform realistic simulations of radial k-space
acquisitions on a dataset of subjects from the UK Biobank and present results
on in-vivo cine CMR k-space data collected from healthy subjects. The results
demonstrate that our method can produce quality-controlled images in a mean
scan time reduced from 12 to 4 seconds per slice, and that image quality is
sufficient to allow clinically relevant parameters to be automatically
estimated to within 5% mean absolute difference.
