Premium
T 2 mapping from highly undersampled data by reconstruction of principal component coefficient maps using compressed sensing
Author(s) -
Huang Chuan,
Graff Christian G.,
Clarkson Eric W.,
Bilgin Ali,
Altbach Maria I.
Publication year - 2012
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.23128
Subject(s) - compressed sensing , principal component analysis , computer science , component (thermodynamics) , data acquisition , pattern recognition (psychology) , function (biology) , algorithm , artificial intelligence , iterative reconstruction , data mining , physics , evolutionary biology , biology , thermodynamics , operating system
Recently, there has been an increased interest in quantitative MR parameters to improve diagnosis and treatment. Parameter mapping requires multiple images acquired with different timings usually resulting in long acquisition times. While acquisition time can be reduced by acquiring undersampled data, obtaining accurate estimates of parameters from undersampled data is a challenging problem, in particular for structures with high spatial frequency content. In this work, principal component analysis is combined with a model‐based algorithm to reconstruct maps of selected principal component coefficients from highly undersampled radial MRI data. This novel approach linearizes the cost function of the optimization problem yielding a more accurate and reliable estimation of MR parameter maps. The proposed algorithm—reconstruction of principal component coefficient maps using compressed sensing—is demonstrated in phantoms and in vivo and compared with two other algorithms previously developed for undersampled data. Magn Reson Med, 2012. © 2011 Wiley Periodicals, Inc.