  In real-time (RT) cardiac cine imaging, a stack of 2D slices is collected
sequentially under free-breathing conditions. A complete heartbeat from each
slice is then used for cardiac function quantification. The inter-slice
respiratory mismatch can compromise accurate quantification of cardiac
function. Methods based on principal components analysis (PCA) have been
proposed to extract the respiratory signal from RT cardiac cine, but these
methods cannot resolve the inter-slice sign ambiguity of the respiratory
signal. In this work, we propose a fully automatic sign correction procedure
based on the similarity of neighboring slices and correlation to the
center-of-mass curve. The proposed method is evaluated in eleven volunteers,
with ten slices per volunteer. The motion in a manually selected
region-of-interest (ROI) is used as a reference. The results show that the
extracted respiratory signal has a high, positive correlation with the
reference in all cases. The qualitative assessment of images also shows that
the proposed approach can accurately identify heartbeats, one from each slice,
belonging to the same respiratory phase. This approach can improve cardiac
function quantification for RT cine without manual intervention.
