A General Framework for Compressed Sensing and Parallel MRI Using Annihilating Filter Based Low-Rank Hankel Matrix

Parallel MRI (pMRI) and compressed sensing MRI (CS-MRI) have been considered as two distinct reconstruction problems. Inspired by recent k-space interpolation methods, an annihilating filter-based low-rank Hankel matrix approach is proposed as a general framework for sparsity-driven k-space interpolation method which unifies pMRI and CS-MRI. Specifically, our framework is based on a novel observation that the transform domain sparsity in the primary space implies the low-rankness of weighted Hankel matrix in the reciprocal space. This converts pMRI and CS-MRI to a k-space interpolation problem using a structured matrix completion. Experimental results using in vivo data for single/multicoil imaging as well as dynamic imaging confirmed that the proposed method outperforms the state-of-the-art pMRI and CS-MRI.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2016-12
Language
English
Keywords

FINITE RATE; UNIFIED FORMULATION; DOMAIN THEORY; DYNAMIC MRI; K-SPACE; RECONSTRUCTION; COMPLETION; SIGNALS; INNOVATION; INFORMATION

Citation

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING, v.2, no.4, pp.480 - 495

ISSN
2333-9403
DOI
10.1109/TCI.2016.2601296
URI
http://hdl.handle.net/10203/214379
Appears in Collection
BiS-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
  • Hit : 99
  • Download : 0
  • Cited 0 times in thomson ci
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡClick to seewebofscience_button
⊙ Cited 1 items in WoSClick to see citing articles inrecords_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0