DocumentCode :
1817777
Title :
Highly accelerated parallel imaging methods for localized massive array coils: comparison using 64-channel phased-array data
Author :
Ji, Jim X. ; Son, Jong Bum ; McDougall, Mary P. ; Wright, Steve M.
Author_Institution :
Dept. of Electr. & Comput. Eng., Texas A&M Univ., TX
fYear :
2006
fDate :
6-9 April 2006
Firstpage :
734
Lastpage :
737
Abstract :
Massive parallel phased-array systems with 32, 64, or more receive channels have potential to achieve high scan time saving for parallel MRI. In the past decade, a number of parallel MRI methods have been proposed including SENSE, SMASH, PILS, GRAPPA, SPACE-RIP, SEA, and other methods. In this work, we investigate the optimality of four reconstruction methods for parallel imaging with massive localized linear phased-array coils. In particular, the artifact power, SNR, and scan efficiency of different methods are systematically compared and analyzed. The studies are based on real MR data collected using a 64-channel system for 2-D MR imaging. The results show that the auto-PILS method and the improved GRAPPA method provide the best SNR and minimal artifact. In addition, the auto-PILS method is most efficient in scan time reduction. Interestingly, SMASH is not optimal for use with the highly localized coils with coil width on the same order as the voxel size
Keywords :
biomedical MRI; image reconstruction; medical image processing; 64-channel phased-array data; GRAPPA; PILS; SEA; SENSE; SMASH; SNR; SPACE-RIP; artifact power; auto-PILS; highly accelerated parallel imaging; image reconstruction; localized massive array coils; parallel MRI; scan efficiency; Acceleration; Coils; High definition video; Image reconstruction; Image sampling; Magnetic resonance imaging; Neoplasms; Phased arrays; Reconstruction algorithms; Torso;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Imaging: Nano to Macro, 2006. 3rd IEEE International Symposium on
Conference_Location :
Arlington, VA
Print_ISBN :
0-7803-9576-X
Type :
conf
DOI :
10.1109/ISBI.2006.1625021
Filename :
1625021
Link To Document :
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