Many methods for mapping ischemic myocardial regions by functional analysis have been suggested. However, the complicated relationship between myocardial function and perfusion, and the inherent limitations of the imaging techniques used, have led to a generally low mapping accuracy. We show herein, that highly accurate mapping can be obtained by combining tagged magnetic resonance imaging (MRI), three-dimensional (3-D) analysis, and artificial neural networks. Nine canine hearts with acute ischemia were studied using multiplanar tagged MRI. Twenty-four myocardial cuboids were tagged in each heart and reconstructed in 3-D at end diastole (ED) and end systole (ES). The cuboids were arranged in three slices approximately 1 cm thick and covered most of the left ventricle (LV). Transmural thickening and endocardial area strain were calculated for each cuboid. Applying a post- mortem (PM) analysis, the percent ischemia in each cuboid was estimated using monastral blue dye; the PM analysis served as a 'gold standard.' An artificial neural network (ANN), designed to estimate the percent ischemia in each cuboid from the functional indexes, was then created. The ANN 'learned' the function-ischemia relationship in 192 cuboids taken from eight of the hearts and was asked to estimate the percent ischemia in the 24 cuboids of the ninth heart. The process was repeated nine times, each time using a different heart as test case. The average accuracy of mapping, i.e., the accuracy with which the ANN has mapped the normal and ischemic cuboids using the functional parameters, was 87.5% ± 7.8 (s.d.). This accuracy was superior to the accuracy obtained by optimal thresholding of the same thickening (80.1%) and endocardial strain (76.9%) data.
ASJC Scopus subject areas
- Biomedical Engineering