Optimal patient classification via statistical decomposition of a 3D left-ventricular atlas

Background Statistical quantification and classification of heart disease, using clinical indices such as ejection fraction (EF) or leftventricular mass (LVM), are used routinely in clinical practice for both diagnosis and prognosis. However, 3D CMR of the left ventricle (LV) provides a wealth of shape features which can maximise the classification power and accuracy of such indices. To exploit these, we propose a framework whereby shape parameters of a 3D LV finite element atlas are used to optimally classify patients according to linear statistical decompositions (Fig. 1).