Low-Rank and Sparse Decomposition Model for Accelerating Dynamic MRI Reconstruction
Joint Authors
Chen, Junbo
Liu, Shouyin
Huang, Min
Source
Journal of Healthcare Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-08-08
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
The reconstruction of dynamic magnetic resonance imaging (dMRI) from partially sampled k-space data has to deal with a trade-off between the spatial resolution and temporal resolution.
In this paper, a low-rank and sparse decomposition model is introduced to resolve this issue, which is formulated as an inverse problem regularized by robust principal component analysis (RPCA).
The inverse problem can be solved by convex optimization method.
We propose a scalable and fast algorithm based on the inexact augmented Lagrange multipliers (IALM) to carry out the convex optimization.
The experimental results demonstrate that our proposed algorithm can achieve superior reconstruction quality and faster reconstruction speed in cardiac cine image compared to existing state-of-art reconstruction methods.
American Psychological Association (APA)
Chen, Junbo& Liu, Shouyin& Huang, Min. 2017. Low-Rank and Sparse Decomposition Model for Accelerating Dynamic MRI Reconstruction. Journal of Healthcare Engineering،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1181465
Modern Language Association (MLA)
Chen, Junbo…[et al.]. Low-Rank and Sparse Decomposition Model for Accelerating Dynamic MRI Reconstruction. Journal of Healthcare Engineering No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1181465
American Medical Association (AMA)
Chen, Junbo& Liu, Shouyin& Huang, Min. Low-Rank and Sparse Decomposition Model for Accelerating Dynamic MRI Reconstruction. Journal of Healthcare Engineering. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1181465
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1181465