Extraction of an input function from dynamic micro-PET images using wavelet packet based sub-band decomposition independent component analysis

Abstract

Positron emission tomography (PET) can be used to quantify physiological parameters. However to perform quantification requires that an input function is measured, namely a plasma time activity curve (TAC). Image-derived input functions (IDIFs) are attractive because they are noninvasive and nearly no blood loss is involved. However, the spatial resolution and the signal to noise ratio (SNR) of PET images are low, which degrades the accuracy of IDIFs. The objective of this study was to extract accurate input functions from microPET images with zero or one plasma sample using wavelet packet based sub-band decomposition independent component analysis (WP SDICA). Two approaches were used in this study. The first was the use of simulated dynamic rat images with different spatial resolutions and SNRs, and the second was the use of dynamic images of eight Sprague-Dawley rats. We also used a population-based input function and a fuzzy c-means clustering approach and compared their results with those obtained by our method using normalized root mean square errors, area under curve errors, and correlation coefficients. Our results showed that the accuracy of the one-sample WP SDICA approach was better than the other approaches using both simulated and realistic comparisons. The errors in the metabolic rate, as estimated by one-sample WP SDICA, were also the smallest using our approach.