Unsupervised deep learning with variational autoencoder for image quality improvement in ultrafast cardiac cine MRI: Initial results

Ultrafast imaging with high acceleration in cardiac MRI is of great clinical interest, but so far often results in inferior image quality that prevents its use in routine diagnosis. In this work, we aim to establish an unsupervised deep learning neural network based on vector quantized variational autoencoder for noise reduction and image quality improvement. Initial results on both public and clinical data promise a new approach to the existing methods. Further investigations with focus on its effectiveness of performance in real world applications are warranted.