Methods for analysis of data representing concentration profiles of platelet analogues in blood flow

Methods for the estimation of particle concentration profiles from numerical data are presented. The estimation techniques described, which involve the use of Fourier transforms, make more efficient use of data than do simple histogram techniques. Additionally, Fourier methods of analysis have been used to test theoretical models of experimental data

Optimization and evaluation of a neural network classifier for PET scans of memory disorder subjects

Back-propagation neural networks were used to classify PET scans as either normal or abnormal, with abnormal subjects defined as subjects who had previously been clinically diagnosed with memory disorders. Numerous neural network experiments were performed in order to achieve optimization with respect to number of hidden units and training duration. Optimizations and performance evaluations were based on ROC analysis, in which the area under the ROC curve was the figure of merit. The neural network's performance was better than that of dlscrlminant analysis, and comparable to the expert's performance, despite the low resolution image data, which consisted of one value per brain lobe, provided to the network

Evaluation of a neural-network classifier for PET scans of normal and Alzheimer's disease subjects

The value of PET as an objective diagnostic tool for dementia may depend on the degree to which abnormal metabolic patterns can be detected by quantitative classification methods. In these studies, a neural-network classifier based on coarse region of interest analyses was used to classify normal and abnormal FDG-PET scans. The performance of neural networks and of an expert reader were evaluated by cross validation testing. When the "abnormal" class was represented by subjects with clinical diagnoses of "Probable Alzheimer's," the areas under the relative-operating-characteristic (ROC) curves were 0.85 and 0.89 for the neural network and the expert reader, respectively. When testing with abnormal subjects represented by "Possible AD" cases, ROC areas for both the network and the expert were 0.81. The neural network out-performed discriminant analysis. It is concluded that PET has potential for the detection of abnormal brain function in dementing diseases, and that the combination of neural networks and PET is a useful diagnostic tool. Despite the low-resolution "view" afforded the neural network, its performance was nearly equivalent to that of an expert reader