Partial volume correction of the microPET blood input function using ensemble learning independent component analysis

Medical images usually suffer from a partial volume effect (PVE), which may degrade the accuracy of any quantitative information extracted from the images. Our aim was to recreate accurate radioactivity concentration and time-activity curves (TACs) by microPET R4 quantification using ensemble learning independent component analysis (EL-ICA). We designed a digital cardiac phantom for this simulation and in order to evaluate the ability of EL-ICA to correct the PVE, the simulated images were convoluted using a Gaussian function (FWHM = 1-4 mm). The robustness of the proposed method towards noise was investigated by adding statistical noise (SNR = 2-16). During further evaluation, another set of cardiac phantoms were generated from the reconstructed images, and Poisson noise at different levels was added to the sinogram. In real experiments, four rat microPET images and a number of arterial blood samples were obtained; these were used to estimate the metabolic rate of FDG (MR(FDG)). Input functions estimated using the FastICA method were used for comparison. The results showed that EL-ICA could correct PVE in both the simulated and real cases. After correcting for the PVE, the errors for MR(FDG), when estimated by the EL-ICA method, were smaller than those when TACs were directly derived from the PET images and when the FastICA approach was used.