Motion correction facilitates the automation of cardiac ASL perfusion imaging

Background Cardiac arterial spin labeling (ASL) perfusion imaging requires subtraction of signals in tagged and control CMR images. In recent clinical studies, perfusion reserve mapping of a single short-axis slice has required laborious manual segmentation of the LV muscle [1]. Here we demonstrate that using free open source software for automatic motion correction reduces the required manual segmentation to just 4 images (2 rest, 2 stress). Methods Framework Images for rest and stress acquisition are processed similarly using the procedure in Figure 1. The control and tagged image pair that has the highest correlation to other image pairs is chosen as a “reference” (Cref, Tref), and is manually segmented to generate masks (MCref, MTref). Remaining control and tagged images are then registered to their respective reference images. The resulting displa-