  Cine cardiac MRI is routinely acquired for the assessment of cardiac health,
but the imaging process is slow and typically requires several breath-holds to
acquire sufficient k-space profiles to ensure good image quality. Several
undersampling-based reconstruction techniques have been proposed during the
last decades to speed up cine cardiac MRI acquisition. However, the
undersampling factor is commonly fixed to conservative values before
acquisition to ensure diagnostic image quality, potentially leading to
unnecessarily long scan times. In this paper, we propose an end-to-end
quality-aware cine short-axis cardiac MRI framework that combines image
acquisition and reconstruction with downstream tasks such as segmentation,
volume curve analysis and estimation of cardiac functional parameters. The goal
is to reduce scan time by acquiring only a fraction of k-space data to enable
the reconstruction of images that can pass quality control checks and produce
reliable estimates of cardiac functional parameters. The framework consists of
a deep learning model for the reconstruction of 2D+t cardiac cine MRI images
from undersampled data, an image quality-control step to detect good quality
reconstructions, followed by a deep learning model for bi-ventricular
segmentation, a quality-control step to detect good quality segmentations and
automated calculation of cardiac functional parameters. To demonstrate the
feasibility of the proposed approach, we perform simulations using a cohort of
selected participants from the UK Biobank (n=270), 200 healthy subjects and 70
patients with cardiomyopathies. Our results show that we can produce
quality-controlled images in a scan time reduced from 12 to 4 seconds per
slice, enabling reliable estimates of cardiac functional parameters such as
ejection fraction within 5% mean absolute error.
