Segmenting and tracking diaphragm and heart regions in Gated-CT datasets as an aid to developing a predictive model for respiratory motion-correction

Diaphragm motion during respiration induces motion of the heart, which is known to cause artifacts in cardiac PET-CT imaging. A possible method of correcting for this is to build a predictive model, which requires knowledge of diaphragm and heart motion interactions. The purpose of this work was to segment and track the diaphragm in 6 respiratory- gated CT datasets for comparison with the heart motion, previously determined from affine registration. Diaphragm segmentation was performed using an algorithm developed for this purpose, based on the extraction of features from an edge- enhanced image. Three regions on the diaphragm surface were then selected for tracking, corresponding to the maxima in the left and right domes and the trough between them. The average diaphragm height in each region was calculated and tracked over the respiratory cycle, as were ratios in the height, reflecting shape. The curves generated from tracking were compared with 6 affine registration parameters (3 translations and 3 rotations) and correlations between them investigated. Correlations were derived for pairs of motion-curves for each patient and for parameter amplitudes across all patients. A number of significant correlations were found (p<0.05). The strongest relationship, as expected, was that between the diaphragm motion and the z translation of the heart, correlated in both the motion-curves and across patients. Other correlations include the amplitude of the diaphragm (right-hand dome) with the amplitudes of the x and y rotations and the x translation of the heart, indicating that these parameters may have useful predictive value. Additionally, diaphragm ratios showed correlations with certain heart motions, confirming that the changing shape of the diaphragm is linked with the type of heart motion. It can be concluded that strong links exist between certain aspects of diaphragm motion and shape change that should have predictive value in building a suitable motion-correcti- on model.