Abstract
A neural network approach has been developed to predict diagnostic image information to assist in the assessment of coronary artery disease. The predicted information represents the redistribution (or reversibility) of perfusion in the myocardium. A multilayer, backpropagation neural network is trained to predict the redistribution information from two other types of images: stress perfusion and myocardial thickening using SPECT imaging. The significance of this approach is two-fold: (i) the predicted reversibility information obviates the additional acquisition of delayed images (with the patient at rest), and (ii) the neural network approach represents a novel way with which to analyze and predict images from other images. This paper presents the methods that underlie the approach, and discusses the most recent experimental results that demonstrate its viability.
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© 1996 Springer-Verlag Berlin Heidelberg
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de Braal, L., Ezquerra, N., Schwartz, E., Cooke, C.D., Garcia, E. (1996). Analyzing and predicting images through a neural network approach. In: Höhne, K.H., Kikinis, R. (eds) Visualization in Biomedical Computing. VBC 1996. Lecture Notes in Computer Science, vol 1131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0046962
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DOI: https://doi.org/10.1007/BFb0046962
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