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Abstract #3835

Iterative K-T PCA with Motion Corrected Training Regularization for 3D Myocardial Perfusion Imaging

Johannes F.M. Schmidt1, Lukas Wissmann1, Robert Manka1, 2, Sebastian Kozerke1, 3

1Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland; 2University Hospital Zurich, Zurich, Switzerland; 3Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom

In this work, an iterative k-t PCA algorithm is proposed where an additional spatial transformation is used to further sparsify the data. Training data based regularization is performed in a motion corrected x-pc domain where each time frame is warped to a reference respiratory position. Spatial transformations are derived from frame-by-frame composite images using atlas-based image registration. Using 3D perfusion data acquired in vivo it is demonstrated that this approach successfully corrects for incomplete unfolding due to respiratory bulk motion.