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).

Quantification method in [18F]fluorodeoxyglucose brain positron emission tomography using independent component analysis.

Objective: To extract accurate image-derived input functions from dynamic brain positron emission tomography images (DBPIs) using independent component analysis (ICA).

Methods: A modified linear model with haematocrit correction was used to improve the accuracy of input functions estimated by independent component analysis and to reduce the error of quantitative analysis. Two types of material were examined: (1) a simulated dynamic phantom with a three-compartment, four-parameter model; (2) clinical 2-h DBPIs with a standard plasma sampling procedure.