Performance Evaluation of PET Image Reconstruction Using Radial Basis Function Networks
In this paper, for the reconstruction of the positron emission tomography (PET) images, Artificial Neural Network (ANN) method and Artificial Neural Network-Radial Basis Function (ANN-RBF) method are pursued. ANN is a dominant tool for demonstrating, exclusively when the essential data relationship is unfamiliar. ANN imitates the learning process of the human brain and can process problems involving nonlinear and complex data even if the data are imprecise and noisy. But, ANN calls for high processing time and its architecture needs to be emulated. So, ANN-RBF method is implemented which is a two-layer feed-forward network in which the hidden nodes implement a set of radial basis functions. Thus, the learning process is very fast. By the image quality parameter of peak signal-to-noise ratio (PSNR) value, the ANN method and the ANN-RBF method are compared and it was clinched that better results are obtained from ANN with RBF method.
KeywordsANN ANN-RBF PSNR value Positron emission tomography Radial basis function
We thank Anderson Diagnostics and Lab, Chennai for providing PET images for our research and Department of Instrumentation and Control Engineering of Kalasalingam University (Kalasalingam Academy of Research and Education), Tamil Nadu, India for permitting to use the computational facilities available in biomedical Laboratory which was setup with the support of the Department of Science and Technology (DST), New Delhi under FIST Program.
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