CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 10806 > Article
Paper
9 August 2018 Compressive sensing magnetic resonance imaging reconstruction based on nonlocal autoregressive modeling
Xi Wu, Jin Wang, Wei Xu, Qing Zhu
Author Affiliations +
Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108063F (2018) https://doi.org/10.1117/12.2503050
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China
Abstract
Magnetic resonance imaging (MRI) is a revolutionary tool in medical imaging, which plays an important role in clinical diagnosis. Compressive sensing (CS) has shown great potential in significantly reducing the acquisition time of MRI scanning. However, how to improve the reconstruction quality with limited k-space data is still a challenge. MRI images are featured with large area of smooth regions, sharp edges and rich textures. Motivated by these facts, we propose a nonlocal autoregressive model (NAM) for CS MRI reconstruction. Nonlocal similarity between image patches is exploited as a regularization term to constrain the nonlocal feature in MRI images, which is very helpful in preserving edge sharpness. While an autoregressive regularization term is employed to describe the linear correlation between neighboring pixels, which preserves more spatial details. Different from previous work, we reconstruct an MRI image patch utilizing correlations both among patches and among neighboring pixels. Extensive experimental results demonstrate that our method outperforms mainstream methods in MRI reconstruction in terms of both subjective quality and objective quality.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Xi Wu, Jin Wang, Wei Xu, and Qing Zhu "Compressive sensing magnetic resonance imaging reconstruction based on nonlocal autoregressive modeling", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108063F (9 August 2018); https://doi.org/10.1117/12.2503050
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 8 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Magnetic resonance imaging
Autoregressive models
Compressed sensing
Neuroimaging
Reconstruction algorithms
Detection theory
Medical imaging
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top