A novel blood-cell-two-compartment model for transferring a whole blood time activity curve to plasma in rodents

Lee, Jih-Shian; Su, Kuan-Hao; Lin, Jun-Cheng; Chuang, Ya-Ting; Chueh, Ho-Shiang; Liu, Ren-Shyan; Wang, Shyh-Jen; Chen, Jyh-Cheng

doi:10.1016/j.cmpb.2008.02.006

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…The quality of the IDIF can be improved by correction for the partial-volume and spillover effects (25). Moreover, the activity in whole blood can be corrected to the activity concentration in plasma using hematocrit (19) or modeled erythrocyte uptake (26). In addition, the noise in the short early time frames contributes to the inaccuracy of the activity concentration.…”

Section: Discussionmentioning

confidence: 99%

A Curve-Fitting Approach to Estimate the Arterial Plasma Input Function for the Assessment of Glucose Metabolic Rate and Response to Treatment

Vriens¹,

Oyen²,

Visser³

2009

J Nucl Med

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For the quantification of dynamic 18 F-FDG PET studies, the arterial plasma time-activity concentration curve (APTAC) needs to be available. This can be obtained using serial sampling of arterial blood or an image-derived input function (IDIF). Arterial sampling is invasive and often not feasible in practice; IDIFs are biased because of partial-volume effects and cannot be used when no large arterial blood pool is in the field of view. We propose a mathematic function, consisting of an initial linear rising activity concentration followed by a triexponential decay, to describe the APTAC. This function was fitted to 80 oncologic patients and verified for 40 different oncologic patients by areaunder-the-curve (AUC) comparison, Patlak glucose metabolic rate (MR glc ) estimation, and therapy response monitoring (DMR glc ). The proposed function was compared with the gold standard (serial arterial sampling) and the IDIF. Methods: To determine the free parameters of the function, plasma time-activity curves based on arterial samples in 80 patients were fitted after normalization for administered activity (AA) and initial distribution volume (iDV) of 18 F-FDG. The medians of these free parameters were used for the model. In 40 other patients (20 baseline and 20 follow-up dynamic 18 F-FDG PET scans), this model was validated. The population-based curve, individually calibrated by AA and iDV (APTAC AA/iDV ), by 1 late arterial sample (APTAC 1sample ), and by the individual IDIF (APTAC IDIF ), was compared with the gold standard of serial arterial sampling (APTAC sampled ) using the AUC. Additionally, these 3 methods of APTAC determination were evaluated with Patlak MR glc estimation and with DMR glc for therapy effects using serial sampling as the gold standard. Results: Excellent individual fits to the function were derived with significantly different decay constants (P , 0.001). Correlations between AUC from APTAC AA/iDV , APTAC 1sample , and APTAC IDIF with the gold standard (APTAC sampled ) were 0.880, 0.994, and 0.856, respectively. For MR glc , these correlations were 0.963, 0.994, and 0.966, respectively. In response monitoring, these correlations were 0.947, 0.982, and 0.949, respectively. Additional scaling by 1 late arterial sample showed a significant improvement (P , 0.001). Conclusion: The fitted input function calibrated for AA and iDV performed similarly to IDIF. Performance improved significantly using 1 late arterial sample. The proposed model can be used when an IDIF is not available or when serial arterial sampling is not feasible.

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Section: Discussionmentioning

confidence: 99%

A Curve-Fitting Approach to Estimate the Arterial Plasma Input Function for the Assessment of Glucose Metabolic Rate and Response to Treatment

Vriens¹,

Oyen²,

Visser³

2009

J Nucl Med

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“…We consider here for simplicity that plasma curve (PIF) and blood curve (BIF) are similar or one can be extracted from the other [2][3][4][5][6][7]. It is recognized that the standard way in determining IF is by blood sampling during the PET measurement.…”

Section: Introductionmentioning

confidence: 99%

Determination of the Input Function at the Entry of the Tissue of Interest and Its Impact on PET Kinetic Modeling Parameters

Bentourkia

2015

Mol Imaging Biol

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Quantitative positron emission tomography (PET) imaging is employed with several measurement protocols all relying on the a priori determination of the input function (IF). The standard technique to determine IF is by blood sampling. However, a unique IF determined in a subject for a given PET study, either defined by sampling or in the images, and commonly utilized for all analyzed tissues in that study equally at rest and during interventions, is expected to provoke biases in the rate constants and in tissue blood volume. The determination of a specific IF at the site of the tissue to be analyzed enhances PET accuracy and renders PET imaging less invasive.

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“…Aside from a high sampling frequency, the exact temporal match of IF and TAC starting times is indispensable. (2) If radioactivity is measured in whole blood, the IF needs to be corrected for the hematocrit and the uptake kinetics of FDG into red blood cells [10]. (3) Measured tissue radioactivity requires the subtraction of the radioactivity in the blood vessels of the respective tissue [11,12].…”

Section: Introductionmentioning

confidence: 99%

FDG kinetic modeling in small rodent brain PET: optimization of data acquisition and analysis

et al. 2013

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BackgroundKinetic modeling of brain glucose metabolism in small rodents from positron emission tomography (PET) data using 2-deoxy-2-[18 F]fluoro-d-glucose (FDG) has been highly inconsistent, due to different modeling parameter settings and underestimation of the impact of methodological flaws in experimentation. This article aims to contribute toward improved experimental standards. As solutions for arterial input function (IF) acquisition of satisfactory quality are becoming available for small rodents, reliable two-tissue compartment modeling and the determination of transport and phosphorylation rate constants of FDG in rodent brain are within reach.MethodsData from mouse brain FDG PET with IFs determined with a coincidence counter on an arterio-venous shunt were analyzed with the two-tissue compartment model. We assessed the influence of several factors on the modeling results: the value for the fractional blood volume in tissue, precision of timing and calibration, smoothing of data, correction for blood cell uptake of FDG, and protocol for FDG administration. Kinetic modeling with experimental and simulated data was performed under systematic variation of these parameters.ResultsBlood volume fitting was unreliable and affected the estimation of rate constants. Even small sample timing errors of a few seconds lead to significant deviations of the fit parameters. Data smoothing did not increase model fit precision. Accurate correction for the kinetics of blood cell uptake of FDG rather than constant scaling of the blood time-activity curve is mandatory for kinetic modeling. FDG infusion over 4 to 5 min instead of bolus injection revealed well-defined experimental input functions and allowed for longer blood sampling intervals at similar fit precisions in simulations.ConclusionsFDG infusion over a few minutes instead of bolus injection allows for longer blood sampling intervals in kinetic modeling with the two-tissue compartment model at a similar precision of fit parameters. The fractional blood volume in the tissue of interest should be entered as a fixed value and kinetics of blood cell uptake of FDG should be included in the model. Data smoothing does not improve the results, and timing errors should be avoided by precise temporal matching of blood and tissue time-activity curves and by replacing manual with automated blood sampling.

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A novel blood-cell-two-compartment model for transferring a whole blood time activity curve to plasma in rodents

Cited by 5 publications

References 11 publications

A Curve-Fitting Approach to Estimate the Arterial Plasma Input Function for the Assessment of Glucose Metabolic Rate and Response to Treatment

A Curve-Fitting Approach to Estimate the Arterial Plasma Input Function for the Assessment of Glucose Metabolic Rate and Response to Treatment

Determination of the Input Function at the Entry of the Tissue of Interest and Its Impact on PET Kinetic Modeling Parameters

FDG kinetic modeling in small rodent brain PET: optimization of data acquisition and analysis

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