Estimation of the 18F-FDG Input Function in Mice by Use of Dynamic Small-Animal PET and Minimal Blood Sample Data

Abstract

Derivation of the plasma time-activity curve in murine small-animal PET studies is a challenging task when tracers that are sequestered by the myocardium are used, because plasma time-activity curve estimation usually involves drawing a region of interest within the area of the reconstructed image that corresponds to the left ventricle (LV) of the heart. The small size of the LV relative to the resolution of the small-animal PET system, coupled with spillover effects from adjacent myocardial pixels, makes this method reliable only for the earliest frames of the scan. We sought to develop a method for plasma time-activity curve estimation based on a model of tracer kinetics in blood, muscle, and liver. Methods: Sixteen C57BL/6 mice were injected with 18 F-FDG, and approximately 15 serial blood samples were taken from the femoral artery via a surgically inserted catheter during 60-min small-animal PET scans. Image data were reconstructed by use of filtered backprojection with CT-based attenuation correction. We constructed a 5-compartment model designed to predict the plasma time-activity curve of 18 F-FDG by use of data from a minimum of 2 blood samples and the dynamic smallanimal PET scan. The plasma time-activity curve (TACp) was assumed to have 4 exponential components ðTAC P 5 A 1 e l1t 1 A 2 e l2t 1 A 3 e l3t 2 ðA 1 1 A 2 1 A 3 Þe l4t Þ based on the serial blood samples. Using Bayesian constraints, we fitted 2-compartment submodels of muscle and liver to small-animal PET data for these organs and simultaneously fitted the input (forcing) function to early small-animal PET LV data and 2 blood samples (;10 min and ;1 h). Results: The area under the estimated plasma time-activity curve had an overall Spearman correlation of 0.99 when compared with the area under the gold standard plasma time-activity curve calculated from multiple blood samples. Calculated organ uptake rates (Patlak K i ) based on the predicted plasma time-activity curve had a correlation of approximately 0.99 for liver, muscle, myocardium, and brain when compared with those based on the gold standard plasma time-activity curve. The model was also able to accurately predict the plasma time-activity curve under experimental conditions that resulted in different rates of clearance of the tracer from blood. Conclusion: We have developed a robust method for accurately estimating the plasma time-activity curve of 18 F-FDG by use of dynamic small-animal PET data and 2 blood samples.

Kinetic Modeling of 3′-Deoxy-3′-18F-Fluorothymidine for Quantitative Cell Proliferation Imaging in Subcutaneous Tumor Models in Mice

“…However, because of small blood vessel diameters and total blood volumes, serial sampling of arterial blood to measure blood activity in mice is difficult (15). Several methods have been proposed to measure input functions in mice (24)(25)(26)(27). Automated blood-sampling devices dedicated to smallanimal imaging can be used (24), but these techniques are limited for multiple-tracer or repetitive studies in the same animals.…”

Repeatable Noninvasive Measurement of Mouse Myocardial Glucose Uptake with 18F-FDG: Evaluation of Tracer Kinetics in a Type 1 Diabetes Model

“…Studies in mice have evaluated the effects of diet (13), anesthetic (14), mode of injection, and blood glucose levels (15) on 18 F-FDG uptake. However, the inaccuracy of the left ventricular (LV) cavity blood image-derived input function (IDIF) (16)(17)(18) remains one of the most difficult impediments to obtaining reliable kinetic measurements in mice. The inaccuracy of the LV cavity IDIF is a particular phenomenon in mice PET imaging because of the relatively small cavity size on the order of the spatial resolution of the current small-animal PET systems (;1.2 mm, Inveon DPET; Siemens).…”

“…The inaccuracy of the LV cavity IDIF is a particular phenomenon in mice PET imaging because of the relatively small cavity size on the order of the spatial resolution of the current small-animal PET systems (;1.2 mm, Inveon DPET; Siemens). Ferl et al (17) were able to predict a blood input function using modeling by taking 2 arterial samples at approximately 10 and 60 min. Other groups have used serial blood sampling to determine the blood input function (13,16,19).…”

A Bayesian framework for the extraction of input function for 18F-FDG metabolism study for both healthy and infarcted rats' hearts

“…In this approach, only one or a few blood samples are extracted from the subject to normalize the averaged IF [11][12][13] . Other authors have proposed the extraction of the IF from images [14][15][16][17][18] . Buvat et al [19] have used the factor analysis of dynamic structures (FADS) method to decompose the dynamic sequences into component images.…”