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Iterative k‐t principal component analysis with nonrigid motion correction for dynamic three‐dimensional cardiac perfusion imaging
Author(s) -
Schmidt Johannes F. M.,
Wissmann Lukas,
Manka Robert,
Kozerke Sebastian
Publication year - 2014
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.24894
Subject(s) - undersampling , principal component analysis , artificial intelligence , computer science , iterative reconstruction , computer vision , nuclear medicine , pattern recognition (psychology) , mathematics , medicine
Purpose In this study, an iterative k‐t principal component analysis (PCA) algorithm with nonrigid frame‐to‐frame motion correction is proposed for dynamic contrast‐enhanced three‐dimensional perfusion imaging. Methods An iterative k‐t PCA algorithm was implemented with regularization using training data corrected for frame‐to‐frame motion in the x‐pc domain. Motion information was extracted using shape‐constrained nonrigid image registration of the composite of training and k‐t undersampled data. The approach was tested for 10‐fold k‐t undersampling using computer simulations and in vivo data sets corrupted by respiratory motion artifacts owing to free‐breathing or interrupted breath‐holds. Results were compared to breath‐held reference data. Results Motion‐corrected k‐t PCA image reconstruction resolved residual aliasing. Signal intensity curves extracted from the myocardium were close to those obtained from the breath‐held reference. Upslopes were found to be more homogeneous in space when using the k‐t PCA approach with motion correction. Conclusions Iterative k‐t PCA with nonrigid motion correction permits correction of respiratory motion artifacts in three‐dimensional first‐pass myocardial perfusion imaging. Magn Reson Med 72:68–79, 2014. © 2013 Wiley Periodicals, Inc.