UNLABELLED: 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 small-animal PET scan. The plasma time-activity curve (TACp) was assumed to have 4 exponential components (TAC(P)=A(1)e(lambda(1)t)+A(2)e(lambda(2)t)+A(3)e(lambda(3)t)-(A(1)+A(2)+A(3))e(lambda(4)t)) 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 (approximately 10 min and approximately 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.
UNLABELLED: 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 small-animal PET scan. The plasma time-activity curve (TACp) was assumed to have 4 exponential components (TAC(P)=A(1)e(lambda(1)t)+A(2)e(lambda(2)t)+A(3)e(lambda(3)t)-(A(1)+A(2)+A(3))e(lambda(4)t)) 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 (approximately 10 min and approximately 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.
Authors: Michael C Kreissl; Hsiao-Ming Wu; David B Stout; Waldemar Ladno; Thomas H Schindler; Xiaoli Zhang; John O Prior; Mayumi L Prins; Arion F Chatziioannou; Sung-Cheng Huang; Heinrich R Schelbert Journal: J Nucl Med Date: 2006-06 Impact factor: 10.057
Authors: Jennifer G Whisenant; Todd E Peterson; Jacob U Fluckiger; Mohammed Noor Tantawy; Gregory D Ayers; Thomas E Yankeelov Journal: Mol Imaging Biol Date: 2013-02 Impact factor: 3.488
Authors: M H Pan; S C Huang; Y P Liao; D Schaue; C C Wang; D B Stout; J R Barrio; W H McBride Journal: Mol Imaging Biol Date: 2008-08-01 Impact factor: 3.488
Authors: Monica Sala-Rabanal; Bruce A Hirayama; Chiara Ghezzi; Jie Liu; Sung-Cheng Huang; Vladimir Kepe; Hermann Koepsell; Amy Yu; David R Powell; Bernard Thorens; Ernest M Wright; Jorge R Barrio Journal: J Physiol Date: 2016-05-10 Impact factor: 5.182
Authors: F O'Sullivan; J Kirrane; M Muzi; J N O'Sullivan; A M Spence; D A Mankoff; K A Krohn Journal: IEEE Trans Med Imaging Date: 2009-08-25 Impact factor: 10.048