IDENTIFICATION OF A HYPOPERFUSED SEGMENT IN BULLS-EYE MYOCARDIAL PERFUSION IMAGES USING A FEED FORWARD NEURAL-NETWORK

Artificial neural networks are computer systems which can be trained to recognize similarities in patterns and which learn by example; one of the more straightforward types being the feed forward neural network (FFNN). We previously reported the use of FFNNs for classification of hypoperfusion patterns in bull's-eye representation of Tl-201 Single photon emission tomography myocardial perfusion studies and showed that, when such an image was divided into 24 segments, FFNNs could detect perfusion defects without direct comparison to a normal data base. This has been extended in this investigation to assess the ability of an FFNN, trained on data in which only a single segment was hypoperfused, to detect this abnormal segment when the hypoperfusion pattern of the other segments in the image varied. The results indicated that the network could reliably determine whether a segment was normally or under perfused, with accuracies of 99% and 100%, respectively, if all other segments were normally perfused. It could also reliably detect a normally perfused segment, even if other segments were hypoperfused, with accuracies of 95% and 98%. The network was less reliable, however, in detecting a hypoperfused segment when other segments were also hypoperfused. showing accuracies of only 74% and 88%.