Improved PASL EPI Acquisitions with Parallel Imaging and UNFOLD

W. S. Hoge, Huan Tan, Robert A. Kraft
Proc of 42st Asilomar Conf on Signals, Systems, and Computers
Pages 1077-1080
Oct, 2008

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Abstract

Pulsed Arterial Spin Labeling (PASL) has been shown to be an effective method for quantitatively measuring cerebral blood flow. Quantification errors with PASL may result from patient motion, however, so Echo Planar Imaging (EPI) is commonly used to acquire PASL images due to its high temporal resolution. EPI achieves high temporal resolution through long echo trains, which can result in image distortions due to magnetic field inhomogeneities. Thus, perfusion weighted EPI could be further improved with shorter echo trains and faster image acquisitions. This paper investigates the use of UNFOLD and parallel MR imaging to reduce EPI image acquisition time. We demonstrate how the use of varied sampling patterns and UNFOLD can completely remove the 'Nyquist ghosts' that are common in EPI acquisitions. This allows one to greatly improve the effectiveness of parallel imaging in EPI, and to reduce field inhomogeneity artifacts by reducing the echo train length.

EPI images of a water phantom reconstructed using (a) self-referenced GRAPPA using standard Nyquist ghost correction, (b) GRAPPA with pre-scan data to generate the reconstruction parameter values and standard Nyquist ghost correction, and (c) self-referenced GRAPPA using UNFOLD to perform the Nyquist ghost correction.

Reference

Hoge WS, Tan H, Kraft RA. Improved PASL EPI acquisitions with parallel imaging and UNFOLD. In Proc of 42st Asilomar Conf on Signals, Systems, and Computers. 2008;1077-1080.

Bibtex entry

@InProceedings{hoge:asilomar08,
  author         = {Hoge, W. Scott and Tan, Huan and Kraft, Robert A.},        
  title          = {Improved {PASL} {EPI} Acquisitions with Parallel Imaging   
                   and {UNFOLD}},                                              
  booktitle      = {Proc of 42st Asilomar Conf on Signals, Systems, and        
                   Computers},                                                 
  pages          = {1077-1080},                                                
  year           = 2008,                                                       
  month          = {Oct}
}

Grants

NIH U41-RR019703

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