scholarly journals Voxel-Based Estimation of Kinetic Model Parameters of the l-[1-11C]Leucine PET Method for Determination of Regional Rates of Cerebral Protein Synthesis: Validation and Comparison with Region-of-Interest-Based Methods

2009 ◽  
Vol 29 (7) ◽  
pp. 1317-1331 ◽  
Author(s):  
Giampaolo Tomasi ◽  
Alessandra Bertoldo ◽  
Shrinivas Bishu ◽  
Aaron Unterman ◽  
Carolyn Beebe Smith ◽  
...  
Keyword(s):  
Protein Synthesis ◽  
Kinetic Model ◽  
Region Of Interest ◽  
Nonlinear Least Squares ◽  
Measured Data ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Time Activity ◽  
Positron Emission ◽  
Cerebral Protein Synthesis

We adapted and validated a basis function method (BFM) to estimate at the voxel level parameters of the kinetic model of the l-[1-11C]leucine positron emission tomography (PET) method and regional rates of cerebral protein synthesis (rCPS). In simulation at noise levels typical of voxel data, BFM yielded low-bias estimates of rCPS; in measured data, BFM and nonlinear least-squares parameter estimates were in good agreement. We also examined whether there are advantages to using voxel-level estimates averaged over regions of interest (ROIs) in place of estimates obtained by directly fitting ROI time-activity curves (TACs). In both simulated and measured data, fits of ROI TACs were poor, likely because of tissue heterogeneity not taken into account in the kinetic model. In simulation, rCPS determined from fitting ROI TACs was substantially overestimated and BFM-estimated rCPS averaged over all voxels in an ROI was slightly underestimated. In measured data, rCPS determined by regional averaging of voxel estimates was lower than rCPS determined from ROI TACs, consistent with simulation. In both simulated and measured data, intersubject variability of BFM-estimated rCPS averaged over all voxels in a ROI was low. We conclude that voxelwise estimation is preferable to fitting ROI TACs using a homogeneous tissue model.

scholarly journals Use of Spectral Analysis with Iterative Filter for Voxelwise Determination of Regional Rates of Cerebral Protein Synthesis with L-[1-11C]leucine PET

2012 ◽  
Vol 32 (6) ◽  
pp. 1073-1085 ◽  
Author(s):  
Mattia Veronese ◽  
Kathleen C Schmidt ◽  
Carolyn Beebe Smith ◽  
Alessandra Bertoldo
Keyword(s):  
Protein Synthesis ◽  
Spectral Analysis ◽  
Kinetic Parameters ◽  
Region Of Interest ◽  
Parameter Estimates ◽  
Time Activity ◽  
Positron Emission ◽  
Highly Correlated ◽  
Tissue Compartments ◽  
Cerebral Protein Synthesis

A spectral analysis approach was used to estimate kinetic parameters of the L-[1-11C]leucine positron emission tomography (PET) method and regional rates of cerebral protein synthesis ( rCPS) on a voxel-by-voxel basis. Spectral analysis applies to both heterogeneous and homogeneous tissues; it does not require prior assumptions concerning number of tissue compartments. Parameters estimated with spectral analysis can be strongly affected by noise, but numerical filters improve estimation performance. Spectral analysis with iterative filter (SAIF) was originally developed to improve estimation of leucine kinetic parameters and rCPS in region-of-interest (ROI) data analyses. In the present study, we optimized SAIF for application at the voxel level. In measured L-[1-11C]leucine PET data, voxel-level SAIF parameter estimates averaged over all voxels within a ROI (mean voxel-SAIF) generally agreed well with corresponding estimates derived by applying the originally developed SAIF to ROI time-activity curves (ROI-SAIF). Region-of-interest-SAIF and mean voxel-SAIF estimates of rCPS were highly correlated. Simulations showed that mean voxel-SAIF rCPS estimates were less biased and less variable than ROI-SAIF estimates in the whole brain and cortex; biases were similar in white matter. We conclude that estimation of rCPS with SAIF is improved when the method is applied at voxel level than in ROI analysis.

scholarly journals A Spectral Analysis Approach for Determination of Regional Rates of Cerebral Protein Synthesis with the L-[1-11C]leucine PET Method

2010 ◽  
Vol 30 (8) ◽  
pp. 1460-1476 ◽  
Author(s):  
Mattia Veronese ◽  
Alessandra Bertoldo ◽  
Shrinivas Bishu ◽  
Aaron Unterman ◽  
Giampaolo Tomasi ◽  
...  
Keyword(s):  
Protein Synthesis ◽  
Spectral Analysis ◽  
Count Rate ◽  
Volume Fraction ◽  
Analysis Approach ◽  
Model Parameters ◽  
Time Activity ◽  
Influx Rate ◽  
Precursor Pool ◽  
Cerebral Protein Synthesis

A spectral analysis approach was used to estimate kinetic model parameters of the L-[1-11C]leucine positron emission tomography (PET) method and regional rates of cerebral protein synthesis (rCPS) in predefined regions of interest (ROIs). Unlike analyses based on the assumption that tissue ROIs are kinetically homogeneous, spectral analysis allows for heterogeneity within a region. To improve estimation performance, a new approach was developed—spectral analysis with iterative filter (SAIF). In simulation SAIF produced low bias, low variance estimates of the influx rate constant for leucine ( K1), blood volume fraction ( V b), fraction of unlabeled leucine in the tissue precursor pool for protein synthesis derived from arterial plasma (λ), and rCPS. Simulation of normal count rate studies showed that SAIF applied to ROI time-activity curves (TACs) performed comparably to the basis function method (BFM) applied to voxel TACs when voxelwise estimates were averaged over all voxels in the ROI. At low count rates, however, SAIF performed better. In measured L-[1-11C]leucine PET data, there was good agreement between ROI-based SAIF estimates and average voxelwise BFM estimates of K1, V b, λ, and rCPS. We conclude that SAIF sufficiently addresses the problem of tissue heterogeneity in ROI data and provides a valid tool for estimation of rCPS, even in low count rate studies.

scholarly journals The Quantitative Analysis of D2-Dopamine Receptors in Baboon Striatum in vivo with 3-N-[2'-18F]Fluoroethylspiperone Using Positron Emission Tomography

1990 ◽  
Vol 10 (5) ◽  
pp. 720-726 ◽  
Author(s):  
S. Jovkar ◽  
K. Wienhard ◽  
G. Pawlik ◽  
H. H. Coenen
Keyword(s):  
Positron Emission Tomography ◽  
Kinetic Model ◽  
Dopamine Receptors ◽  
Specific Activity ◽  
Emission Tomography ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Positron Emission ◽  
Model Configuration

We used the ligand 3- N-[2'-18F]fluoroethylspiperone (FESP), which binds to D2-dopamine receptors in the striatum, and positron emission tomography (PET) to quantify striatal D2-dopamine densities ( Bmax) and binding kinetics in baboon brain in vivo. Sequential PET scans were obtained for 4 h post injection. Various similar models based on a nonlinear kinetic four-compartment model that takes into account the effect of ligand specific activity were used. We investigated the effect of exact model configuration on the reliability of Bmax and other kinetic transfer coefficients. We found that with the ligand FESP and dynamic PET studies, the estimated values of Bmax and other model parameters are sensitive to the choice of model configuration, ligand specific activity, and data analysis technique. The limitations of the reliability of parameter estimates in a complex kinetic model for receptor ligands were studied in simulation calculations. Results showed that the accuracy of estimated values of Bmax is affected by both the ligand binding properties and the injected dose of ligand. The estimated average value of kinetic model parameters was as follows: ligand-receptor dissociation constant k4 = 0.0080 min−1; the product of ligand-receptor association constant and fraction of ligand available to bind to specific receptors f2 ka = 0.0052 (min n M)−1; and D2-dopamine receptor density Bmax = 37.5 pmol g−1.

scholarly journals Estimation of Local Cerebral Protein Synthesis Rates with L-[1-11C]Leucine and PET: Methods, Model, and Results in Animals and Humans

1989 ◽  
Vol 9 (4) ◽  
pp. 446-460 ◽  
Author(s):  
Randall A. Hawkins ◽  
Sung-Cheng Huang ◽  
Jorge R. Barrio ◽  
Randy E. Keen ◽  
Dagan Feng ◽  
...  
Keyword(s):  
Protein Synthesis ◽  
Compartment Model ◽  
Input Function ◽  
Model Parameters ◽  
Positron Emission ◽  
Human Volunteers ◽  
Normal Human ◽  
Three Compartment Model ◽  
Computer Simulation Studies ◽  
Cerebral Protein Synthesis

We have estimated the cerebral protein synthesis rates (CPSR) in a series of normal human volunteers and monkeys using l-[1-11C]leucine and positron emission tomography (PET) using a three-compartment model. The model structure, consisting of a tissue precursor, metabolite, and protein compartment, was validated with biochemical assay data obtained in rat studies. The CPSR values estimated in human hemispheres of about 0.5 nmol/min/g agree well with hemispheric estimates in monkeys. The sampling requirements (input function and scanning sequence) for accurate estimates of model parameters were investigated in a series of computer simulation studies.

Multi-day flow forecasting using the Kalman filter

1991 ◽  
Vol 18 (2) ◽  
pp. 320-327 ◽  
Author(s):  
Murray A. Fitch ◽  
Edward A. McBean
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Linear Prediction ◽  
Measured Data ◽  
System Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Forecasting Performance ◽  
Optimal Forecasting ◽  
Linear Prediction Model

A model is developed for the prediction of river flows resulting from combined snowmelt and precipitation. The model employs a Kalman filter to reflect uncertainty both in the measured data and in the system model parameters. The forecasting algorithm is used to develop multi-day forecasts for the Sturgeon River, Ontario. The algorithm is shown to develop good 1-day and 2-day ahead forecasts, but the linear prediction model is found inadequate for longer-term forecasts. Good initial parameter estimates are shown to be essential for optimal forecasting performance. Key words: Kalman filter, streamflow forecast, multi-day, streamflow, Sturgeon River, MISP algorithm.

Integrating Parameter Estimation Solutions From the Time and Time-Scale Domains

2009 ◽  
Author(s):  
James R. McCusker ◽  
Kourosh Danai
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Time Scale ◽  
Prediction Error ◽  
Nonlinear Least Squares ◽  
Integrated Approach ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Single Model ◽  
Iterative Estimation

A method of parameter estimation was recently introduced that separately estimates each parameter of the dynamic model [1]. In this method, regions coined as parameter signatures, are identified in the time-scale domain wherein the prediction error can be attributed to the error of a single model parameter. Based on these single-parameter associations, individual model parameters can then be estimated for iterative estimation. Relative to nonlinear least squares, the proposed Parameter Signature Isolation Method (PARSIM) has two distinct attributes. One attribute of PARSIM is to leave the estimation of a parameter dormant when a parameter signature cannot be extracted for it. Another attribute is independence from the contour of the prediction error. The first attribute could cause erroneous parameter estimates, when the parameters are not adapted continually. The second attribute, on the other hand, can provide a safeguard against local minima entrapments. These attributes motivate integrating PARSIM with a method, like nonlinear least-squares, that is less prone to dormancy of parameter estimates. The paper demonstrates the merit of the proposed integrated approach in application to a difficult estimation problem.

Tracer kinetic model of regional pulmonary function using positron emission tomography

2002 ◽  
Vol 93 (3) ◽  
pp. 1104-1114 ◽  
Author(s):  
Gaetano G. Galletti ◽  
José G. Venegas
Keyword(s):  
Positron Emission Tomography ◽  
Pulmonary Function ◽  
Goodness Of Fit ◽  
Molecular Nitrogen ◽  
Region Of Interest ◽  
Emission Tomography ◽  
Gas Content ◽  
Pulmonary Vasculature ◽  
Model Parameters ◽  
Positron Emission

To determine the spatial distributions of pulmonary perfusion, shunt, and ventilation, we developed a compartmental model of regional 13N-labeled molecular nitrogen (13NN) kinetics measured from positron emission tomography (PET) images. The model features a compartment for right heart and pulmonary vasculature and two compartments for each region of interest: 1) aerated alveolar units and 2) alveolar units with no gas content (shunting). The model was tested on PET data from normal animals (dogs and sheep) and from animals with experimentally injured lungs simulating acute respiratory distress syndrome. The analysis yielded estimates of regional perfusion, shunt fraction, and specific ventilation with excellent goodness-of-fit to the data ( R 2 > 0.99). Model parameters were estimated to within 10% accuracy in the presence of exaggerated levels of experimental noise by using a Monte Carlo sensitivity analysis. Main advantages of the present model are that 1) it separates intraregional blood flow to aerated alveolar units from that shunting across nonaerated units and 2) it accounts and corrects for intraregional tracer removal by shunting blood when estimating ventilation from subsequent washout of tracer. The model was thus found to provide estimates of regional parameters of pulmonary function in sizes of lung regions that could potentially approach the intrinsic resolution for PET images of 13NN in lung (∼7.0 mm for a multiring PET camera).

scholarly journals In vivo Cerebral Protein Synthesis Rates with Leucyl-Transfer RNA Used as a Precursor Pool: Determination of Biochemical Parameters to Structure Tracer Kinetic Models for Positron Emission Tomography

1989 ◽  
Vol 9 (4) ◽  
pp. 429-445 ◽  
Author(s):  
Randy E. Keen ◽  
Jorge R. Barrio ◽  
Sung-Cheng Huang ◽  
Randall A. Hawkins ◽  
Michael E. Phelps
Keyword(s):  
Positron Emission Tomography ◽  
Protein Synthesis ◽  
Bolus Injection ◽  
Emission Tomography ◽  
Precursor Pool ◽  
Leucine Oxidation ◽  
Positron Emission ◽  
Tissue Compartments ◽  
Cerebral Protein Synthesis

Leucine oxidation and incorporation into proteins were examined in the in vivo rat brain to determine rates and compartmentation of these processes for the purpose of structuring mathematical compartmental models for the noninvasive estimation of in vivo human cerebral protein synthesis rates (CPSR) using positron emission tomography (PET). Leucine specific activity (SA) in arterial plasma and intracellular free amino acids, leucyl-tRNA, α-ketoisocaproic acid (KIC), and protein were determined in whole brain of the adult rat during the first 35 min after intravenous bolus injection of l-[1-14C]leucine. Incorporation of leucine into proteins accounted for 90% of total brain radioactivity at 35 min. The lack of [14C]KIC buildup indicates that leucine oxidation in brain is transaminase limited. Characteristic specific activities were maximal between 0 to 2 min after bolus injection with subsequent decline following the pattern: plasma leucine ≥ leucyl-tRNA ≈ KIC > intracellular leucine. The time integral of leucine SA in plasma was about four times that of tissue leucine and twice those of leucyl-tRNA and KIC, indicating the existence of free leucine, leucyl-tRNA, and KIC tissue compartments, communicating directly with plasma, and separate secondary free leucine, leucyl-tRNA, and KIC tissue compartments originating in unlabeled leucine from proteolysis. Therefore, a relatively simple model configuration based on the key assumptions that (a) protein incorporation and catabolism proceed from a precursor pool communicating with the plasma space, and (b) leucine catabolism is transaminase limited is justified for the in vivo assessment of CPSR from exogenous leucine sources using PET in humans.

Migratory Catch-Age Analysis

1990 ◽  
Vol 47 (12) ◽  
pp. 2315-2327 ◽  
Author(s):  
Terrance J. Quinn II ◽  
Richard B. Deriso ◽  
Philip R. Neal
Keyword(s):  
Auxiliary Information ◽  
Nonlinear Least Squares ◽  
Sensitivity Study ◽  
Error Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Migration Rates ◽  
Process Error ◽  
A New Technique ◽  
And Migration

We review techniques for estimating the abundance of migratory populations and develop a new technique based on catch-age data from geographic regions and our earlier technique, catch-age analysis with auxiliary information (Deriso et al. 1985, 1989). Data requirements are catch-age data over several years, some auxiliary information, and migration rates among regions. The model, containing parameters for year-class abundance, age selectivity, full-recruitment fishing mortality, and catchability, is fitted to data with a nonlinear least squares algorithm. We present a measurement error model and a process error model and favor the process error model because all model parameters can be jointly estimated. By application to data on Pacific halibut, the process error model converges readily and produces estimates with no significant bias. These estimates have relatively high precision compared to those from analyses which did not incorporate migration information. The error structure used in a model has a more significant impact on parameter estimates than migration rates. A sensitivity study of migration rates shows sensitivity of the order of the rates themselves.

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