Fast Regularized Reconstruction of Non-uniformly Subsampled Parallel MRI data

W. S. Hoge, Misha E. Kilmer, Stephen J. Haker, Dana H. Brooks, Walid E. Kyriakos
Proceedings of 2006 IEEE International Symposium on Biomedical Imaging (ISBI06)
Pages 714-717
April 6-9, 2006

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Abstract

Parallel MR imaging is an effective approach to reduce MR image acquisition time. Non-uniform subsampling allows one to tailor the subsampling scheme for improved image quality at high acceleration factors. However, non-uniform subsampling precludes fast reconstruction schemes such as SENSE, and is more likely to require a regularized solution than reconstruction of uniformly subsampled data demands. This means that one needs to choose a good regularization parameter, typically requiring multiple expensive system solves. Here, we present an efficient LSQR-Hybrid algorithm which simultaneously addresses the need for rapid regularization parameter selection and fast reconstruction. This algorithm can reconstruct non-uniformly subsampled parallel MRI data, with automatic regularization and good image quality, in a time competitive with Cartesian SENSE.

Reference

Hoge WS, Kilmer ME, Haker SJ, Brooks DH, Kyriakos WE. Fast regularized reconstruction of non-uniformly subsampled parallel MRI data. In Proceedings of 2006 IEEE International Symposium on Biomedical Imaging (ISBI06). Arlington, VA, USA, 2006;714-717.

Bibtex entry

@InProceedings{hogeISBI06,
  author         = {Hoge, W. Scott and Kilmer, Misha E. and Haker, Stephen J.  
                   and Brooks, Dana H. and Kyriakos, Walid E.},                
  title          = {Fast Regularized Reconstruction of Non-uniformly Subsampled
                   Parallel {MRI} data},                                       
  booktitle      = {Proceedings of 2006 IEEE International Symposium on        
                   Biomedical Imaging (ISBI06)},                               
  pages          = {714-717},                                                  
  month          = {April 6--9},                                               
  year           = 2006,                                                       
  address        = {Arlington, VA, USA}
}

Grants

NIH U41-RR019703

Copyright Information

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