3-DIMENSIONAL DEMARCATION OF PERFUSION ZONES CORRESPONDING TO SPECIFIC CORONARY-ARTERIES - APPLICATION FOR AUTOMATED INTERPRETATION OF MYOCARDIAL SPECT

In this study, three-dimensional maps of specific coronary artery terr itories were derived and combined with normal distribution maps as a r eference for automated characterization of defects, including location and size. Methods: One hundred sixty-eight Tc-99m-sestamibi myocardia l perfusion SPECT scans from normal patients and patients with single- vessel disease were selected according to angiographic data. Five sepa rate groups were established for men and women: normal, proximal left anterior descending (PLAD), distal left anterior descending (DLAD), ri ght coronary artery (RCA) and left circumflex (LCx). All myocardial pe rfusion studies were aligned and sized to the same three-dimensional o rientation using a previously developed automated image registration t echnique. Mean and variation three-dimensional templates were construc ted from stress images in each group. Normal templates were demarcated with hypoperfusion regions obtained from disease templates. The defec ts were detected in the individual patient's images by a region-growin g algorithm which identified abnormal voxels by comparison to the corr esponding voxels in the mean and variation templates. Results: Defects were quantified with respect to volume, location relative to the expe cted hypoperfusion zones and severity index. Abnormal regions could be marked directly on tomographic slices and visualized in various orien tations. Single defects greater than 2% of the myocardium positioned w ithin demarcated perfusion territories were detected in 105/119 abnorm al patients and in 3/49 normal patients. Conclusion: Maps of myocardia l perfusion zones created from images of angiographically selected pat ients provide a reference for automated localization of myocardial per fusion defects. A template-based region-growing is a robust technique for volumetric quantification and localization of abnormal regions.