Document Detail


Variable-density parallel imaging with partially localized coil sensitivities.
MedLine Citation:
PMID:  20236876     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Partially parallel imaging with localized sensitivities is a fast parallel image reconstruction method for both Cartesian and non-Cartesian trajectories, but suffers from aliasing artifacts when there are deviations from the assumption of perfect localization. Such reconstructions would normally crop the individual coil images to remove the artifacts prior to combination. However, the sampling densities in variable-density k-space trajectories support different field-of-views for separate regions in k -space. In fact, the higher sampling density of low frequencies can be used to reconstruct a bigger field-of-view without introducing aliasing artifacts and the resulting image signal-to-noise ratio (SNR) can be improved. A novel, fast variable-density parallel imaging method is presented, which reconstructs different field-of-views from separate frequencies according to the local sampling density in k-space. Aliasing-suppressed images can be produced with high SNR-efficiency without the need for accurate estimation of coil sensitivities and complex or iterative computations.
Authors:
Tolga Cukur; Juan M Santos; John M Pauly; Dwight G Nishimura
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't     Date:  2010-03-15
Journal Detail:
Title:  IEEE transactions on medical imaging     Volume:  29     ISSN:  1558-254X     ISO Abbreviation:  IEEE Trans Med Imaging     Publication Date:  2010 May 
Date Detail:
Created Date:  2010-07-27     Completed Date:  2011-02-04     Revised Date:  2012-04-24    
Medline Journal Info:
Nlm Unique ID:  8310780     Medline TA:  IEEE Trans Med Imaging     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1173-81     Citation Subset:  IM    
Affiliation:
Information Systems Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA. cukur@stanford.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Artifacts
Image Enhancement / methods
Image Interpretation, Computer-Assisted / methods*
Magnetic Resonance Imaging / methods*
Grant Support
ID/Acronym/Agency:
R01 HL039297-24/HL/NHLBI NIH HHS; R01 HL075803-08/HL/NHLBI NIH HHS; R01-HL075803/HL/NHLBI NIH HHS; R01-HL39297/HL/NHLBI NIH HHS
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