Abstract
Abstract
Background
Magnetic resonance imaging (MRI) is an effective auxiliary diagnostic method in clinical medicine, but it has always suffered from the problem of long acquisition time. Compressed sensing and parallel imaging are two common techniques to accelerate MRI reconstruction. Recently, deep learning provides a new direction for MRI, while most of them require a large number of data pairs for training. However, there are many scenarios where fully sampled k-space data cannot be obtained, which will seriously hinder the application of supervised learning. Therefore, deep learning without fully sampled data is indispensable.
Main text
In this review, we first introduce the forward model of MRI as a classic inverse problem, and briefly discuss the connection of traditional iterative methods to deep learning. Next, we will explain how to train reconstruction network without fully sampled data from the perspective of obtaining prior information.
Conclusion
Although the reviewed methods are used for MRI reconstruction, they can also be extended to other areas where ground-truth is not available. Furthermore, we may anticipate that the combination of traditional methods and deep learning will produce better reconstruction results.
Funder
National Natural Science Foundation of China
the Natural Science Foundation of Fujian Province of China
Fundamental Research Funds for the Central Universities
Health-Education Joint Research Project of Fujian Province
Natural Science Foundation of Fujian Province
the National Natural Science Foundation of China
the science and technology fund of Fujian education department
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging
Cited by
35 articles.
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