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. The input function was extracted from DBPIs using independent component analysis. The modified linear model with haematocrit correction was used to obtain the independent component analysis-estimated input function (Iica). For comparison, the input function derived from the last three blood samples (Iest) was used. The image-derived input functions (Iica and Iest) were compared with the input function from blood sampling (Itp). The mean percentage error of the metabolic rate of [F]-2-fluoro-2-deoxy-D-glucose (MRFDG) was calculated for both Iica and Iest against that of Itp.
Results: In simulated studies, the mean percentage errors of MRFDG between true simulated and estimated values of Iest and Iica were 8.2% and 4.2%, respectively. In clinical studies, six clinical cases were collected. The mean percentage errors and standard deviations of MRFDG with Iest and Iica were 12.6+/-7.5% and 7.7+/-3.3%, respectively.
Conclusions: We have proposed a technique for estimating image-derived input functions using independent component analysis without blood sampling. The results of our method were highly correlated with those from standard blood sampling, and more accurate than those of other methods proposed previously.