UNLABELLED: In this study, three-dimensional maps of specific coronary artery territories were derived and combined with normal distribution maps as a reference for automated characterization of defects, including location and size. METHODS: One hundred sixty-eight 99mTc-sestamibi myocardial perfusion SPECT scans from normal patients and patients with single-vessel disease were selected according to angiographic data. Five separate groups were established for men and women: normal, proximal left anterior descending (PLAD), distal left anterior descending (DLAD), right coronary artery (RCA) and left circumflex (LCx). All myocardial perfusion studies were aligned and sized to the same three-dimensional orientation using a previously developed automated image registration technique. Mean and variation three-dimensional templates were constructed from stress images in each group. Normal templates were demarcated with hypoperfusion regions obtained from disease templates. The defects were detected in the individual patient's images by a region-growing algorithm which identified abnormal voxels by comparison to the corresponding voxels in the mean and variation templates. RESULTS: Defects were quantified with respect to volume, location relative to the expected hypoperfusion zones and severity index. Abnormal regions could be marked directly on tomographic slices and visualized in various orientations. Single defects greater than 2% of the myocardium positioned within demarcated perfusion territories were detected in 105/119 abnormal patients and in 3/49 normal patients. CONCLUSION: Maps of myocardial perfusion zones created from images of angiographically selected patients provide a reference for automated localization of myocardial perfusion defects. A template-based region-growing is a robust technique for volumetric quantification and localization of abnormal regions.
UNLABELLED: In this study, three-dimensional maps of specific coronary artery territories were derived and combined with normal distribution maps as a reference for automated characterization of defects, including location and size. METHODS: One hundred sixty-eight 99mTc-sestamibi myocardial perfusion SPECT scans from normal patients and patients with single-vessel disease were selected according to angiographic data. Five separate groups were established for men and women: normal, proximal left anterior descending (PLAD), distal left anterior descending (DLAD), right coronary artery (RCA) and left circumflex (LCx). All myocardial perfusion studies were aligned and sized to the same three-dimensional orientation using a previously developed automated image registration technique. Mean and variation three-dimensional templates were constructed from stress images in each group. Normal templates were demarcated with hypoperfusion regions obtained from disease templates. The defects were detected in the individual patient's images by a region-growing algorithm which identified abnormal voxels by comparison to the corresponding voxels in the mean and variation templates. RESULTS: Defects were quantified with respect to volume, location relative to the expected hypoperfusion zones and severity index. Abnormal regions could be marked directly on tomographic slices and visualized in various orientations. Single defects greater than 2% of the myocardium positioned within demarcated perfusion territories were detected in 105/119 abnormal patients and in 3/49 normal patients. CONCLUSION: Maps of myocardial perfusion zones created from images of angiographically selected patients provide a reference for automated localization of myocardial perfusion defects. A template-based region-growing is a robust technique for volumetric quantification and localization of abnormal regions.
Authors: Guido Germano; Paul B Kavanagh; Terrence D Ruddy; R Glenn Wells; Yuan Xu; Daniel S Berman; Piotr J Slomka Journal: J Nucl Cardiol Date: 2016-10-14 Impact factor: 5.952
Authors: Lene Rosendahl; Peter Blomstrand; Jan L Ohlsson; Per-Gunnar Björklund; Britt-Marie Ahlander; Sven-Ake Starck; Jan E Engvall Journal: BMC Med Imaging Date: 2008-12-12 Impact factor: 1.930