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Model-based Deep MR Imaging: the roadmap of generalizing compressed sensing model using deep learning

Research paper by Jing Cheng, Haifeng Wang, Yanjie Zhu, Qiegen Liu, Leslie Ying, Dong Liang

Indexed on: 11 Mar '20Published on: 19 Jun '19Published in: arXiv - Computer Science - Computer Vision and Pattern Recognition



Abstract

Accelerating magnetic resonance imaging (MRI) has been an ongoing research topic since its invention in the 1970s. Among a variety of acceleration techniques, compressed sensing (CS) has become an important strategy during the past decades. Although CS-based methods can achieve high performance with many theoretical guarantees, it is challenging to determine the numerical uncertainties in the reconstruction model such as the optimal sparse transformations, sparse regularizer in the transform do-main, regularization parameters and the parameters of the optimization algorithm. Recently, deep learning has been introduced in MR reconstruction to address these issues and shown potential to significantly improve image quality.In this paper, we propose a general framework combining the CS-MR model with deep learning to maximize the potential of deep learning and model-based reconstruction for fast MR imaging and attempt to provide a guideline on how to improve the image quality with deep learning based on the traditional reconstruction algorithms.