  Cine cardiac magnetic resonance imaging (MRI) is widely used for diagnosis of
cardiac diseases thanks to its ability to present cardiovascular features in
excellent contrast. As compared to computed tomography (CT), MRI, however,
requires a long scan time, which inevitably induces motion artifacts and causes
patients' discomfort. Thus, there has been a strong clinical motivation to
develop techniques to reduce both the scan time and motion artifacts. Given its
successful applications in other medical imaging tasks such as MRI
super-resolution and CT metal artifact reduction, deep learning is a promising
approach for cardiac MRI motion artifact reduction. In this paper, we propose a
recurrent neural network to simultaneously extract both spatial and temporal
features from under-sampled, motion-blurred cine cardiac images for improved
image quality. The experimental results demonstrate substantially improved
image quality on two clinical test datasets. Also, our method enables
data-driven frame interpolation at an enhanced temporal resolution. Compared
with existing methods, our deep learning approach gives a superior performance
in terms of structural similarity (SSIM) and peak signal-to-noise ratio (PSNR).
