학술논문
Estimation of the glucose metabolism from dynamic PET-scans using neural networks
Document Type
Conference
Author
Source
Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing Neural networks for signal processing Neural Networks for Signal Processing [1995] V. Proceedings of the 1995 IEEE Workshop. :439-448 1995
Subject
Language
Abstract
A method for fast pixel by pixel estimation of the glucose metabolism in the brain using the tracer [/sup 18/F]fluorodeoxy-glucose in dynamic positron emission tomography (PET)-scan data is described. A neural network is trained to estimate the glucose metabolism on data generated by direct fitting of the rate constants in Sokoloff's model. The generalisation ability of the neural network is tested on data from subjects not included in the training set. This method can be used to estimate changes of the metabolism in different brain regions for subjects with serious brain disorders. By using the neural estimation procedure the processing time for a brain scan volume is reduced from 48 hours to 4 minutes.