Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network
This addresses motion artifacts in cardiac MRI for clinical diagnosis, but it is incremental as it applies a known deep learning approach to a specific medical imaging task.
The paper tackled motion artifacts in cine cardiac MRI by proposing a recurrent neural network that extracts spatial and temporal features from under-sampled images, resulting in substantially improved image quality with superior SSIM and PSNR compared to existing methods.
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).