The direct incorporation of perfusion defect information to define ischemia and infarction in a finite element model of the left ventricle.

This paper describes the process in which complex lesion geometries (specified by computer generated perfusion defects) are incorporated in the description of nonlinear finite element (FE) mechanical models used for specifying the motion of the left ventricle (LV) in the 4D extended cardiac torso (XCAT) phantom to simulate gated cardiac image data. An image interrogation process was developed to define the elements in the LV mesh as ischemic or infarcted based upon the values of sampled intensity levels of the perfusion maps. The intensity values were determined for each of the interior integration points of every element of the FE mesh. The average element intensity levels were then determined. The elements with average intensity values below a user-controlled threshold were defined as ischemic or infarcted depending upon the model being defined. For the infarction model cases, the thresholding and interrogation process were repeated in order to define a border zone (BZ) surrounding the infarction. This methodology was evaluated using perfusion maps created by the perfusion cardiac-torso (PCAT) phantom an extension of the 4D XCAT phantom. The PCAT was used to create 3D perfusion maps representing 90% occlusions at four locations (left anterior descending (LAD) segments 6 and 9, left circumflex (LCX) segment 11, right coronary artery (RCA) segment 1) in the coronary tree. The volumes and shapes of the defects defined in the FE mechanical models were compared with perfusion maps produced by the PCAT. The models were incorporated into the XCAT phantom. The ischemia models had reduced stroke volume (SV) by 18-59 ml. and ejection fraction (EF) values by 14-50% points compared to the normal models. The infarction models, had less reductions in SV and EF, 17-54 ml. and 14-45% points, respectively. The volumes of the ischemic/infarcted regions of the models were nearly identical to those volumes obtained from the perfusion images and were highly correlated (R² = 0.99).

Duke Scholars

Author William Paul Segars Radiology