ICA Based Automatic Segmentation of Dynamic ${\bf H}_{\bf 2}^{\bf 15}{\bf O}$ Cardiac PET Images

In this study, we applied an iterative independent component analysis (ICA) method for the separation of cardiac tissue components (myocardium, right, and left ventricle) from dynamic positron emission tomography (PET) images. Previous phantom and animal studies have shown that ICA separation extracts the cardiac structures accurately. Our goal in this study was to investigate the methodology with human studies. The ICA separated cardiac structures were used to calculate the myocardial perfusion in two different cases: 1) the regions of interest were drawn manually on the ICA separated component images and 2) the volumes of interest (VOI) were automatically segmented from the component images. For the whole myocardium, the perfusion values of 25 rest and six drug-induced stress studies obtained with these methods were compared to the values from the manually drawn regions of interest on differential images. The separation of the rest and stress studies using ICA-based methods was successful in all cases. The visualization of the cardiac structures from ${\bf H}_2^{15}{\bf O}$ PET studies was improved with the ICA separation. Also, the automatic segmentation of the VOI seemed to be feasible.