Kinetic analysis of FDG in rat liver: effect of dietary intervention on arterial and portal vein input

Fluid dynamics analyses of the intrahepatic portal vein tributaries using 7-T MRI

BACKGROUND

Assessing portal vein (PV) hemodynamics is an essential part of liver disease management/liver surgery, yet the optimal methods of assessing intrahepatic PV flow have not yet been established. This study investigated the usefulness of 7-Tesla MRI with hemodynamic analysis for detecting small flow changes within narrow intrahepatic PV branches.

METHODS

Flow data in the main PV was obtained by two methods, two-dimensional cine phase contrast-MRI (2D cine PC-MRI) and three-dimensional non-cine phase contrast-MRI (3D PC-MRI). Hemodynamic parameters, such as flow volume rate, flow velocity, and wall shear stress in intrahepatic PV branches were calculated before and after a meal challenge using 3D PC-MRI and hemodynamic analysis.

RESULTS

The hemodynamic parameters obtained using 3D PC-MRI and 2D cine PC-MRI were similar. All intrahepatic PV branches were clearly depicted in eight planes, and significant changes in flow volume rate were seen in three planes. Average and maximum velocities, cross-sectional area, and wall shear stress were similar between before and after a meal challenge in all planes.

CONCLUSION

7-Tesla 3D PC-MRI combined with hemodynamic analysis is a promising tool for assessing intrahepatic PV flow and enables future studies in small animals to investigate PV hemodynamics associated with liver disease/postoperative liver recovery.

Dynamic PET of human liver inflammation: impact of kinetic modeling with optimization-derived dual-blood input function

The hallmark of nonalcoholic steatohepatitis is hepatocellular inflammation and injury in the setting of hepatic steatosis. Recent work has indicated that dynamic 18F-FDG PET with kinetic modeling has the potential to assess hepatic inflammation noninvasively, while static FDG-PET is less promising. Because the liver has dual blood supplies, kinetic modeling of dynamic liver PET data is challenging in human studies. This paper aims to identify the optimal dual-input kinetic modeling approach for dynamic FDG-PET of human liver inflammation. Fourteen patients with nonalcoholic fatty liver disease were included. Each patient underwent 1 h dynamic FDG-PET/CT scan and had liver biopsy within six weeks. Three models were tested for kinetic analysis: the traditional two-tissue compartmental model with an image-derived single-blood input function (SBIF), a model with population-based dual-blood input function (DBIF), and a new model with optimization-derived DBIF through a joint estimation framework. The three models were compared using Akaike information criterion (AIC), F test and histopathologic inflammation score. Results showed that the optimization-derived DBIF model improved liver time activity curve fitting and achieved lower AIC values and higher F values than the SBIF and population-based DBIF models in all patients. The optimization-derived model significantly increased FDG K1 estimates by 101% and 27% as compared with traditional SBIF and population-based DBIF. K1 by the optimization-derived model was significantly associated with histopathologic grades of liver inflammation while the other two models did not provide a statistical significance. In conclusion, modeling of DBIF is critical for dynamic liver FDG-PET kinetic analysis in human studies. The optimization-derived DBIF model is more appropriate than SBIF and population-based DBIF for dynamic FDG-PET of liver inflammation.

A new compartmental method for the analysis of liver FDG kinetics in small animal models

Background

Compartmental analysis is a standard method to quantify metabolic processes using fluorodeoxyglucose-positron emission tomography (FDG-PET). For liver studies, this analysis is complex due to the hepatocyte capability to dephosphorylate and release glucose and FDG into the blood. Moreover, a tracer is supplied to the liver by both the hepatic artery and the portal vein, which is not visible in PET images. This study developed an innovative computational approach accounting for the reversible nature of FDG in the liver and directly computing the portal vein tracer concentration by means of gut radioactivity measurements.

Methods

Twenty-one mice were subdivided into three groups: the control group ‘CTR’ (n = 7) received no treatment, the short-term starvation group ‘STS’ (n = 7) was submitted to food deprivation with free access to water within 48 h before imaging, and the metformin group ‘MTF’ (n = 7) was treated with metformin (750 mg/Kg per day) for 1 month. All mice underwent a dynamic micro-PET study for 50 min after an ¹⁸F-FDG injection. The compartmental analysis considered two FDG pools (phosphorylated and free) in both the gut and liver. A tracer was carried into the liver by the hepatic artery and the portal vein, and tracer delivery from the gut was considered as the sole input for portal vein tracer concentration. Accordingly, both the liver and gut were characterized by two compartments and two exchange coefficients. Each one of the two two-compartment models was mathematically described by a system of differential equations, and data optimization was performed by applying a Newton algorithm to the inverse problems associated to these differential systems.

Results

All rate constants were stable in each group. The tracer coefficient from the free to the metabolized compartment in the liver was increased by STS, while it was unaltered by MTF. By contrast, the tracer coefficient from the metabolized to the free compartment was reduced by MTF and increased by STS.

Conclusions

Data demonstrated that our method was able to analyze FDG kinetics under pharmacological or pathophysiological stimulation, quantifying the fraction of the tracer trapped in the liver or dephosphorylated and released into the bloodstream.

Combining MRI with PET for partial volume correction improves image-derived input functions in mice

Accurate kinetic modelling using dynamic PET requires knowledge of the tracer concentration in plasma, known as the arterial input function (AIF). AIFs are usually determined by invasive blood sampling, but this is prohibitive in murine studies due to low total blood volumes. As a result of the low spatial resolution of PET, image-derived input functions (IDIFs) must be extracted from left ventricular blood pool (LVBP) ROIs of the mouse heart. This is challenging because of partial volume and spillover effects between the LVBP and myocardium, contaminating IDIFs with tissue signal. We have applied the geometric transfer matrix (GTM) method of partial volume correction (PVC) to 12 mice injected with 18F-FDG affected by a Myocardial Infarction (MI), of which 6 were treated with a drug which reduced infarction size [1]. We utilised high resolution MRI to assist in segmenting mouse hearts into 5 classes: LVBP, infarcted myocardium, healthy myocardium, lungs/body and background. The signal contribution from these 5 classes was convolved with the point spread function (PSF) of the Cambridge split magnet PET scanner and a non-linear fit was performed on the 5 measured signal components. The corrected IDIF was taken as the fitted LVBP component. It was found that the GTM PVC method could recover an IDIF with less contamination from spillover than an IDIF extracted from PET data alone. More realistic values of Ki were achieved using GTM IDIFs, which were shown to be significantly different (p<0.05) between the treated and untreated groups.