Volume-Wise Application of Principal Component Analysis on Masked Dynamic PET Data in Sinogram Domain

Most of the methods used for analyzing PET data are applied in the spatial domain (image domain), in which reconstructed images contain all different types of effects and errors caused by the reconstruction algorithm such as correlation in-between pixels, correlations in-between frames, and streak-artifacts. In this paper, we have investigated a new, pixel wise, noise prenormalization method used for transformation of input data followed by volume-wise application of principal component analysis (PCA) on masked dynamic PET data in the sinogram domain. We are aiming to improve the performance of PCA and to provide images with improved quality and signal extraction. We compare the performance of PCA and the image quality obtained with the new method with previously published approaches. The results show improvement of performance of PCA with respect to image quality, signal extraction, precision, and visualization