Iterative k-t principal component analysis with nonrigid motion correction for dynamic three-dimensional cardiac perfusion imaging.

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.