Dynamic PET allows quantification of physiological parameters through tracer kinetic modeling. For dynamic imaging of brain or head and neck cancer on conventional PET scanners with a short axial field of view, the im...
详细信息
Dynamic PET allows quantification of physiological parameters through tracer kinetic modeling. For dynamic imaging of brain or head and neck cancer on conventional PET scanners with a short axial field of view, the image-derived inputfunction (ID-IF) from intracranial blood vessels such as the carotid artery (CA) suffers from severe partial volume effects. Alternatively, optimization-derived inputfunction (OD-IF) by the simultaneous estimation (SIME) method does not rely on an ID-IF but derives the inputfunction directly from the data. However, the optimization problem is often highly ill-posed. We proposed a new method that combines the ideas of OD-IF and ID-IF together through a kernel framework. While evaluation of such a method is challenging in human subjects, we used the uEXPLORER total-body PET system that covers major blood pools to provide a reference for validation. Methods: The conventional SIME approach estimates an inputfunction using a joint estimation together with kinetic parameters by fitting time activity curves from multiple regions of interests (ROIs). The inputfunction is commonly parameterized with a highly nonlinear model which is difficult to estimate. The proposed kernel SIME method exploits the CA ID-IF as a priori information via a kernel representation to stabilize the SIME approach. The unknown parameters are linear and thus easier to estimate. The proposed method was evaluated using 18Ffluorodeoxyglucose studies with both computer simulations and 20 human-subject scans acquired on the uEXPLORER scanner. The effect of the number of ROIs on kernel SIME was also explored. Results: The estimated OD-IF by kernel SIME showed a good match with the reference inputfunction and provided more accurate estimation of kinetic parameters for both simulation and human-subject data. The kernel SIME led to the highest correlation coefficient (R = 0.97) and the lowest mean absolute error (MAE = 10.5 %) compared to using the CA ID-IF (R = 0.86, MAE =
Background: A new model of an inputfunction for human [F-18]-2-Deoxy-2-fluoro-D-glucose fluoro (FDG) positron emission tomography (PET) brain studies with bolus injection is presented. Methods: input data for early t...
详细信息
Background: A new model of an inputfunction for human [F-18]-2-Deoxy-2-fluoro-D-glucose fluoro (FDG) positron emission tomography (PET) brain studies with bolus injection is presented. Methods: input data for early time, roughly up to 0.6 min, were obtained noninvasively from the time-activity curve (TAC) measured from a carotid artery region of interest. Representative tissue TACs were obtained by clustering the output curves to a limited number of dominant clusters. Three venous plasma samples at a later time were used to fit the functional form of the inputfunction in conjunction with obtaining kinetic rate parameters of the dominant clusters, K-1, k(2) and k(3), using the compartmental model for FDG-PET. Experiments to test the approach used data from 18 healthy subjects. Results: The model provides an effective means to recover the inputfunction in FDG-PET studies. Weighted nonlinear least squares parameter estimation using the recovered inputfunction, as contrasted with use of plasma samples, yielded highly correlated values of K= K(1)k(3)/(k(2),+k(3),) for simulated data, a correlation coefficient of 0.99780, a slope of 1.019 and an intercept of almost zero. The estimates of K for real data by graphical Patlak analysis using the recovered inputfunction were almost identical to those obtained using arterial plasma samples, with correlation coefficients greater than 0.9976, regression slopes between 0.958 and 1.091 and intercepts that are virtually zero. Conclusions: A reliable sermautomated alternative for input function estimation that uses image-derived data augmented with three plasma samples is presented and evaluated for FDG-PET human brain studies. C 2007 Elsevier Inc. All rights reserved.
It is often necessary to analyze the time response of a tracer. A common way of analyzing the tracer time response is to use a compartment model and estimate the model parameters. The model parameters are generally ph...
详细信息
ISBN:
(纸本)0819456470
It is often necessary to analyze the time response of a tracer. A common way of analyzing the tracer time response is to use a compartment model and estimate the model parameters. The model parameters are generally physiologically meaningful and called "kinetic parameters". In this paper, we simultaneously estimate both the kinetic parameters at each voxel and the model-based plasma inputfunction directly from the sinogram data. Although the plasma model parameters are not our primary interest, they are required for accurate reconstruction of kinetic parameters. The plasma model parameters are initialized with an image domain method to avoid local minima. and multiresolution optimization is used to perform the required reconstruction. Good initial guesses for the plasma parameters are required for the algorithm to converge to the correct answer. Therefore, we devised a preprocessing step involving clustering of the emission images by temporal characteristics to find a reasonable plasma curve that was consistent with the kinetics of the multiple tissue types. We compare the root mean squared error (RMSE) of the kinetic parameter estimates with the measured (true) plasma inputfunction and with the estimated plasma inputfunction. Tests using a realistic rat head phantom and a real plasma inputfunction show, that we can simultaneously estimate the kinetic parameters of the two-tissue compartment model and plasma inputfunction. The RMSE of the kinetic parameters increased for some parameters and remained the same or decreased for other parameters.
暂无评论