TO THE EDITOR: Recently, Brix and coworkers (1) described the quantification of liver metabolism by dynamic PET in dogs. Before that, we performed similar studies in pigs (2). Both studies address the important question of which input function to use for liver modeling. There are, however, some notable differences between the studies with regard to the selection of kinetic model, which we believe require special attention for correctly quantifying liver metabolism by dynamic PET. In particular,

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... a physiologically based model is a prerequisite for obtaining physiologically meaningful parameters. Initially, Brix and coworkers (1) used a 2-tissue-compartment model including a vascular volume for analyzing their data. However, their procedure provided an estimate of the vascular volume close to zero. This is clearly incorrect compared with the more reasonable blood volume of 28% that they derived from literature. Nevertheless, data were reanalyzed using the 2-tissue-compartment model without a vascular volume, and their conclusions were based on these results. In contrast, our data analysis (2) clearly supported the use of the model including a vascular volume. Our estimates of both the vascular volume and the clearance (K 1 ) were in agreement with independent measurements of the liver blood volume and flow in the same animals. Therefore, we wish to discuss why Brix and coworkers were unable to estimate a nonzero vascular volume, and to question the physiological relevance of the liver model without a vascular volume. First, the unreasonably low vascular volume estimated by Brix and coworkers may have been due to an inaccurate input function. This raises the question of whether their blood sampling systems dispersed the arterial (and portal) blood time-activity curve (TAC) so much that their dual-input TAC did not resemble the true input to the liver. Their finding that the arterial blood sampling TACs were considerably dispersed compared with the internal aorta TACs from the PET scan supports this concern. Problems may also have arisen from their calculation of the flow-weighted dual-input function, which was based on mean values of arterial and portal flows (obtained from a different study) instead of the individual flows. After all, flows can vary markedly between animals (3) and during an experiment. If their input function does not describe the true input to the liver, this explains their unphysiologically low value for the vascular volume and gives rise to doubts concerning the validity of their comparison of different dual-input models. Second, we question whether the choice of kinetic model configuration should be based solely on statistical criteria rather than including physiological considerations. We know that model parameters such as K 1 and the vascular volume parameter are correlated (2) and that the large hepatic vascular space will contain a large amount of nonextracted FDG activity after an FDG bolus injection. Accordingly, the use of a model without a vascular volume parameter will introduce systematic errors in the estimated parameters, which precludes a clear-cut physiological interpretation of the findings. Third, Brix and coworkers suggest that arterial sampling may be used for clinical patients with liver lesions that are supplied mainly by the hepatic artery. We would like to comment that the validity of such an approach depends on the degree of arterialization and makes quantitative comparisons with the surrounding liver tissue difficult. Instead, we recommend analyzing such data by the more robust Gjedde-Patlak representation, perhaps with a correction for k 4 (4). Using this approach, we obtained similar values for the forward metabolic clearance, K, using either dual-input or arterial input function (2). In summary, the unreasonably low estimates for the vascular volume of the liver give rise to questions as to whether the blood sampling procedures used by Brix and coworkers are capable of measuring the true input to the liver. In addition, we question the interpretation and physiological relevance of a liver model without a vascular volume. REFERENCES 1. Brix G, Ziegler SI, Bellemann ME, et al. Quantification of [ 18 F]FDG uptake in the normal liver using dynamic PET: impact and modeling of the dual hepatic blood supply. J Nucl Med. 2001;42:1265-1273. 2. Munk OL, Bass L, Roelsgaard K, Bender D, Hansen SB, Keiding S. Liver kinetics of glucose analogs measured in pigs by PET: importance of dual-input blood sampling. J Nucl Med. 2001;42:795-801. 3. Ziegler SI, Haberkorn U, Byrne H, et al. Measurement of liver blood flow using oxygen-15 labelled water and dynamic positron emission tomography: limitations of model description. REPLY: We appreciate the comments of Munk et al. regarding our article (1), which give us the opportunity (a) to discuss central aspects of our investigation in more detail, and (b) to relate our results to those reported by Munk et al. (2). Please note that our manuscript had been accepted for publication about 1 month before the paper of Munk and coworkers appeared in The Journal of Nuclear Medicine. The main challenge in the quantification of 18 F-FDG uptake in the normal liver using dynamic PET is the determination of the dual hepatic blood supply. It was therefore the aim of our experimental study to assess in foxhounds the effect of different input models on the rate constants of the standard 3-compartment FDG model. Each experiment comprised a dynamic PET scan together with the continuous detection of counting rates in arterial and venous blood feeding the liver using 2 independent coincidence-based detector systems. These measurements yielded 3 different blood curves (SUV aorta PET , SUV arterial detector , and SUV venous detector ), which were used to define 5 different hepatic input functions, as described in our paper. For quantification of rate constants characterizing FDG transport and metabolization in the liver, we used the conventional 3-compartment FDG model with a "vascular volume" characterized by a volume fraction f B and a lag time to correct for the time delay of 18 F-FDG activity arrival in the liver. Although we started the nonlinear leastsquares fitting procedure with an initial value for the "vascular vol-LETTERS TO THE EDITOR 439 by on March 18, 2020. For personal use only. jnm.snmjournals.org Downloaded from