Abstract: Accelerated MRI reconstruction is important for making MRI faster and thus applicable in a broader range of problem domains. Computational tools allow for high-resolution imaging without the need to perform time-consuming measurements. Most recently, deep learning approaches have been applied to this problem. However, none of these methods have been shown to transfer well across different measurement settings. We propose to use Recurrent Inference Machines as a framework for accelerated MRI, which allows us to leverage the power of deep learning without explicit domain knowledge. We show in experiments that the model can generalize well across different setups, while at the same time it outperforms another deep learning method and a compressed sensing approach.
Keywords: RNNs, MRI, Inverse Problem, Image Reconstruction, Iterative Methods
Author Affiliation: Informatics Institute UvA, AMLAB UvA, Academic Medical Center, Spinoza Centre for Neuroimaging, Canadian Institute for Advanced Research