Hepatic blood flow using [15O]H2O and dynamic PET
[15O]H2O bolus model
The analysis method of hepatic blood flow (actually perfusion) is based on one-tissue compartment model, modified to account for the dual input of the liver, consisting of hepatic artery and portal vein (Taniguchi et al., 1993, 1996a, and 1999; Kudomi et al., 2008; Slimani et al., 2008; Kiss et al., 2009; Rijzewijk et al., 2010). Although appropriate radiowater model for liver requires both inputs, it does not necessarily mean that the portal perfusion (fPV) can be reliably quantified, especially in non-invasive studies where tracer concentration curve of portal vein is not measured but estimated in the model (Ziegler et al., 1996; Becker et al., 2005; Rijzewijk et al., 2010).
Radiowater concentration in the volume of interest, as measured with PET, (CPET(t)), is the volume fraction weighted sum of concentrations in the blood (CB(t)) and liver tissue, (CT(t)):
Blood concentration is a weighted sum of concentrations in the arterial and portal blood (CA and CPV), with the arterial and portal blood flows (fA and fPV) as the weighting factors:
Since radiowater is an inert tracer with reversible kinetics, the concentration curve of the portal vein blood is assumed to be simply the arterial blood delayed and dispersed in the gastrointestinal tract (GI):
Concentration of radiowater in the tissue is described with the one-tissue compartmental model with the two input functions; p is the partition coefficient of water in the tissue:
If the impact of venous blood, including portal vein, on vascular volume fraction is ignored, that is, VB is assumed to represent arterial blood volume only and marked with VA, then a simple multilinear equation for estimating the model parameters:
, where k2=(fA+fPV)/p.
In tumours of the liver, most of the blood is supplied by the portal artery (Breedis & Young, 1954; Ackerman, 1972). Therefore, perfusion in liver tumours can be analyzed using only the usual arterial input (Taniguchi et al., 1996b; Yamaguchi et al., 2000; Kunishima et al., 2002; Fukuda et al., 2004).
Arterial blood data
Blood data from on-line sampler
Arterial blood data, collected using on-line sampling system, must be calibrated and corrected for physical decay, dispersion, and time delay. It is recommended that regional tissue TAC from the liver is used in time delay correction.
Extraction of arterial blood data from PET image
Instead of using ABSS, arterial blood TAC can be extracted from the dynamic image. The procedure is very reliable if CT or MR images are available and the point spread function of PET image is known (full width at half maximum, FWHM, in mm). If no CT or MR images are available, then the program eabaort may need to be validated for the specific PET scanner. With modern PET scanners the image resolution is good enough for using the blood TAC from small ROI drawn into abdominal aorta without any correction for partial volume effect.
Analysis method in TPC
Extracting the input function simultaneously with the model fitting is performed by N Kudomi using software written by him.
If arterial blood curve is available, then the model presented above can be used, and regional liver TTACs can be fitted using fit_wliv. However, the quality of PET data may not allow accurate estimation of all model parameters. Especially, the estimates for VB have very high variability and do not affect the fitted curve, but introduce high variability to the other parameters (see an example in Figure 2). The dual-input in hepatic radiowater models is known to lead to problems with parameter identifiability (Becker et al., 2005). Therefore, VB should be constrained to a fixed value, for example to 0; although the total blood volume in the liver is high, only the arterial fraction of blood from hepatic artery is kinetically distinguishable in radiowater data. Spleen data could be used to add information to the model (Taniguchi et al., 1999).
- Dual blood supply to the liver
- Radiowater model
- ARG method
- Powerpoint presentation by Lotfi Slimani
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Updated at: 2023-03-25
Created at: 2015-02-04
Written by: Vesa Oikonen