  In the clinical routine, short axis (SA) cine cardiac MR (CMR) image stacks
are acquired during multiple subsequent breath-holds. If the patient cannot
consistently hold the breath at the same position, the acquired image stack
will be affected by inter-slice respiratory motion and will not correctly
represent the cardiac volume, introducing potential errors in the following
analyses and visualisations. We propose an approach to automatically correct
inter-slice respiratory motion in SA CMR image stacks. Our approach makes use
of probabilistic segmentation maps (PSMs) of the left ventricular (LV) cavity
generated with decision forests. PSMs are generated for each slice of the SA
stack and rigidly registered in-plane to a target PSM. If long axis (LA) images
are available, PSMs are generated for them and combined to create the target
PSM; if not, the target PSM is produced from the same stack using a 3D model
trained from motion-free stacks. The proposed approach was tested on a dataset
of SA stacks acquired from 24 healthy subjects (for which anatomical 3D cardiac
images were also available as reference) and compared to two techniques which
use LA intensity images and LA segmentations as targets, respectively. The
results show the accuracy and robustness of the proposed approach in motion
compensation.
