Robust non-rigid motion compensation of free-breathing myocardial perfusion MRI data.

Kinetic parameter values, such as myocardial perfusion, can be quantified from dynamic contrast enhanced (DCE-) magnetic resonance imaging (MRI) data using tracer-kinetic modelling. However, respiratory motion affects the accuracy of this process. Motion compensation of the image series is difficult due to the rapid local signal enhancement caused by the passing of the gadolinium-based contrast agent. This contrast enhancement invalidates the assumptions of the (global) cost functions traditionally used in intensity-based registrations. The algorithms are unable to distinguish whether the differences in signal intensity between frames are caused by spatial motion artefacts or the local contrast enhancement. In order to address this problem, a fully-automated motion compensation scheme is proposed which consists of two stages. The first of which uses robust principal component analysis (RPCA) to separate the local signal enhancement from the baseline signal, before a refinement stage which uses traditional PCA to construct a synthetic reference series that is free from motion but preserves the signal enhancement. Validation is performed on 18 subjects acquired in free-breathing and 5 clinical subjects acquired with a breath-hold. The validation assesses visual quality, temporal smoothness of tissue curves and the clinically relevant quantitative perfusion values. The expert observers score of visual quality increased by a mean of 1.58/5 after motion compensation and improvement over previously published methods. The proposed motion compensation scheme also leads to the improved quantitative performance of motion compensated free-breathing image series (30% reduction in the coefficient of variation across quantitative perfusion maps, 53% reduction in temporal variations (p<0.001)).