Next Article in Journal
The First Application of a Gd3Al2Ga3O12:Ce Single-Crystal Scintillator to Neutron Radiography
Next Article in Special Issue
Recovering the Magnetic Image of Mars from Satellite Observations
Previous Article in Journal
Quantitative Evaluation of Soil Structure and Strain in Three Dimensions under Shear Using X-ray Computed Tomography Image Analysis
Previous Article in Special Issue
On a Variational and Convex Model of the Blake–Zisserman Type for Segmentation of Low-Contrast and Piecewise Smooth Images
Article

An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset

1
Department of Mathematics, University of Florida, Gainesville, FL 32611, USA
2
Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303, USA
*
Author to whom correspondence should be addressed.
Author list in alphabetical order.
Academic Editors: Fabiana Zama and Elena Loli Piccolomini
J. Imaging 2021, 7(11), 231; https://doi.org/10.3390/jimaging7110231
Received: 23 September 2021 / Revised: 26 October 2021 / Accepted: 28 October 2021 / Published: 31 October 2021
(This article belongs to the Special Issue Inverse Problems and Imaging)
This work aims at developing a generalizable Magnetic Resonance Imaging (MRI) reconstruction method in the meta-learning framework. Specifically, we develop a deep reconstruction network induced by a learnable optimization algorithm (LOA) to solve the nonconvex nonsmooth variational model of MRI image reconstruction. In this model, the nonconvex nonsmooth regularization term is parameterized as a structured deep network where the network parameters can be learned from data. We partition these network parameters into two parts: a task-invariant part for the common feature encoder component of the regularization, and a task-specific part to account for the variations in the heterogeneous training and testing data. We train the regularization parameters in a bilevel optimization framework which significantly improves the robustness of the training process and the generalization ability of the network. We conduct a series of numerical experiments using heterogeneous MRI data sets with various undersampling patterns, ratios, and acquisition settings. The experimental results show that our network yields greatly improved reconstruction quality over existing methods and can generalize well to new reconstruction problems whose undersampling patterns/trajectories are not present during training. View Full-Text
Keywords: MRI reconstruction; meta-learning; domain generalization MRI reconstruction; meta-learning; domain generalization
Show Figures

Figure 1

MDPI and ACS Style

Bian, W.; Chen, Y.; Ye, X.; Zhang, Q. An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset. J. Imaging 2021, 7, 231. https://doi.org/10.3390/jimaging7110231

AMA Style

Bian W, Chen Y, Ye X, Zhang Q. An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset. Journal of Imaging. 2021; 7(11):231. https://doi.org/10.3390/jimaging7110231

Chicago/Turabian Style

Bian, Wanyu, Yunmei Chen, Xiaojing Ye, and Qingchao Zhang. 2021. "An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset" Journal of Imaging 7, no. 11: 231. https://doi.org/10.3390/jimaging7110231

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop