Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks
This is an incremental review article that discusses signal processing issues to guide further development of deep learning methods for fast MRI reconstruction.
The paper reviews deep learning-based image reconstruction methods for MRI, categorizing them into data-driven, model-driven, and integrated approaches to speed up reconstruction with reduced measurements.
Image reconstruction from undersampled k-space data has been playing an important role for fast MRI. Recently, deep learning has demonstrated tremendous success in various fields and also shown potential to significantly speed up MR reconstruction with reduced measurements. This article gives an overview of deep learning-based image reconstruction methods for MRI. Three types of deep learning-based approaches are reviewed, the data-driven, model-driven and integrated approaches. The main structure of each network in three approaches is explained and the analysis of common parts of reviewed networks and differences in-between are highlighted. Based on the review, a number of signal processing issues are discussed for maximizing the potential of deep reconstruction for fast MRI. the discussion may facilitate further development of "optimal" network and performance analysis from a theoretical point of view.