Abstract:
GRAPPA reconstructions from parallel receivers rely on local mutual information between k-space neighbors acquired across multiple channels. Typically, the criteria for inclusion in the reconstruction kernel are based on ad hoc or empirical considerations. This work shows that the GRAPPA kernel selection can be framed as a model selection problem and that the well known Akaike Information Criterion (AIC) provides an automated and practical choice of reconstruction kernel.
Reference:
abstract K. Heberlein, R. Nana, S. LaConte, X. Hu. Automated GRAPPA Kernel Selection using Akaike Information Criterion. In Proceedings 16th Scientific Meeting, International Society for Magnetic Resonance in Medicine, Toronto, page 1292, 2008. [bibtex]
Bibtex Entry:
@inproceedings{1292,
Author = {Heberlein, K. and Nana, R. and LaConte, S. and Hu, X.},
Title ={Automated {G}{R}{A}{P}{P}{A} Kernel Selection using {A}kaike Information Criterion},
BookTitle = {Proceedings 16th Scientific Meeting, International Society for Magnetic Resonance in Medicine, Toronto},
Pages = {1292},
Abstract = {GRAPPA reconstructions from parallel receivers rely on local mutual information between k-space neighbors acquired across multiple channels. Typically, the criteria for inclusion in the reconstruction kernel are based on ad hoc or empirical considerations. This work shows that the GRAPPA kernel selection can be framed as a model selection problem and that the well known Akaike Information Criterion (AIC) provides an automated and practical choice of reconstruction kernel.},
Year = {2008} }