Henrik Pedersen1, Henrik B.W. Larsson1, Rasmus Larsen2
1Functional Imaging Unit, Glostrup Hospital, Glostrup, Denmark; 2DTU Informatics, Technical University of Denmark, Lyngby, Denmark
Respiratory motion of the heart represents a major practical problem in myocardial perfusion MRI, because it hampers perfusion quantification and causes residual aliasing artifacts in modern k-space undersampling techniques. This work presents a computational framework that allows simultaneous modeling of perfusion and motion (SMPM) for arbitrary k-space sampling strategies. We demonstrate the SMPM concept for a representative free-breathing myocardial perfusion data set using 1) fully sampled k-space data, 2) undersampled Cartesian k-space data, and 3) undersampled radial k-space data. Results show that the SMPM approach fits the original data well, both with fully sampled k-space data and 8-fold radial undersampling.