A review and experimental evaluation of deep learning methods for MRI reconstruction

Arghya PalHarvard Medical School, Yogesh RathiHarvard Medical School
March 2022 issue
Publication date: 2022/03/11
PDF · arXiv


Following the success of deep learning in a wide range of applications, neural network-based machine-learning techniques have received significant interest for accelerating magnetic resonance imaging (MRI) acquisition and reconstruction strategies. A number of ideas inspired by deep learning techniques for computer vision and image processing have been successfully applied to nonlinear image reconstruction in the spirit of compressed sensing for accelerated MRI. Given the rapidly growing nature of the field, it is imperative to consolidate and summarize the large number of deep learning methods that have been reported in the literature, to obtain a better understanding of the field in general. This article provides an overview of the recent developments in neural-network based approaches that have been proposed specifically for improving parallel imaging. A general background and introduction to parallel MRI is also given from a classical view of k-space based reconstruction methods. Image domain based techniques that introduce improved regularizers are covered along with k-space based methods which focus on better interpolation strategies using neural networks. While the field is rapidly evolving with plenty of papers published each year, in this review, we attempt to cover broad categories of methods that have shown good performance on publicly available data sets. Limitations and open problems are also discussed and recent efforts for producing open data sets and benchmarks for the community are examined.


MRI Reconstruction · Deep Leaning · machine learning · k-space reconstruction · parallel MRI

Bibtex @article{melba:2022:001:pal, title = "A review and experimental evaluation of deep learning methods for MRI reconstruction", authors = "Pal, Arghya and Rathi, Yogesh", journal = "Machine Learning for Biomedical Imaging", volume = "1", issue = "March 2022 issue", year = "2022" }

2022:001 cover