Time-dependent deep image prior for dynamic MRI
Jaejun Yoo

Meeting • 08 September 2020

Abstract
In this seminar, I would like to share our recent work on reconstructing dynamic MRI images. I will also share my experience and some (among numerous others) of my failed attempts that might be useful in your project. We introduce a novel unsupervised deep-learning-based image reconstruction algorithm for dynamic magnetic resonance imaging (MRI). To accelerate the magnetic resonance imaging (MRI), every existing method relies on a partial sampling of the k-space to reduce the acquisition time. To compensate the information loss due to this partial sampling, they either exploit a hand-crafted prior such as sparsity in certain transform domains (compressed sensing) or use a neural network to learn a mapping from a partially sampled data to fully sampled data (supervised learning), which are expensive to acquire and generally unavailable. Unlike the previous approaches, our method learns to encode the temporal redundancy of the measurements and decode the corresponding image sequences using a strong structural prior of convolutional neural networks (CNNs). By carefully designing a latent space and optimizing CNNs, our method improves the reconstructed image quality by 3 dB from the previous state of the art in a fully unsupervised manner.