New Results

Dynamic PET of Human Liver Inflammation: Impact of Kinetic Modeling with Optimization-Derived Dual-Blood Input Function

Guobao Wang, Michael T. Corwin, Kristin A. Olson, Ramsey D. Badawi, Souvik Sarkar
doi: https://doi.org/10.1101/268748

ABSTRACT

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 did not show a promise. 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-hour dynamic FDG-PET/CT scan and had liver biopsy within six weeks. Three models were tested for kinetic analysis: traditional two-tissue compartmental model with an image-derived single-blood input function (SBIF), model with population-based dual-blood input function (DBIF), and 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.

INTRODUCTION

Nonalcoholic fatty liver disease (NAFLD) affects approximately 30% of the general population and is emerging as a leading cause of liver-related morbidity and mortality (1,2). Nonalcoholic steatohepatitis (NASH) is a more severe form of NAFLD characterized by hepatocyte inflammation and injury and can subsequently lead to cirrhosis and associated liver cancer and liver failure (3). NASH develops in 5-10% of NAFLD patients (i.e., 5-10 million people in the United States) and is associated with higher liver-related mortality than is hepatic steatosis alone (4). Differentiation of NASH from simple fatty liver is essential for future patient management in NAFLD, particularly as newer therapies become available (3,5).

The diagnostic hallmark of NASH is hepatocellular inflammation and injury in the setting of hepatic steatosis (3). Imaging methods have been developed to quantify hepatic steatosis (6), e.g. ultrasonography (7), computed tomography (CT) (8) or magnetic resonance imaging (MRI) (9–11). Other methods such as magnetic resonance elastography (MRE) (12) and ultrasound elastography (13) can measure liver stiffness and quantify advanced fibrosis of hepatic tissue (14–16). However, there is currently no effective imaging method for accurate characterization of liver inflammation in clinical practice and clinical trials (17). Liver biopsy followed by clinical histopathology remains the current standard practice (18).

18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is a widely used and effective method for imaging cell glucose metabolism. Previous studies have reported the use of FDG-PET for imaging infection and inflammation (19,20). Several studies have investigated the use of FDG-PET in the liver (21–27). However, none of these studies have reported relevant or promising results for liver inflammation and NASH assessment. It is worth noting that current clinical usage of PET is largely limited to static PET imaging which provides standardized uptake value (SUV) as a semi-quantitative measure of glucose utilization. This static way of using PET may not be able to explore the actual role of FDG-PET in NAFLD and NASH. Our recent preliminary work has indicated that while SUV by static FDG-PET did not show a promise, dynamic FDG-PET with kinetic modeling is promising for assessing liver inflammation in NASH (28). The FDG K1 parameter, which represents the transport rate of FDG from blood to hepatic tissue, can be a potential PET biomarker for characterizing liver inflammation.

Blood input function is essential for kinetic modeling in dynamic FDG-PET (29). Conventionally it is either obtained by invasive arterial blood sampling or noninvasively derived from the left ventricle or aortic regions in dynamic PET images (30–,32). In the liver, both hepatic artery and portal vein provide blood supply to the hepatic tissue (22). The portal vein input is not the same as the hepatic artery input and is difficult to obtain in human objects (22). Neglecting the influence of portal vein can result in inaccurate kinetic modeling. A common approach to accounting for the effect of dual blood supplies is to use a flow-weighted model of dualblood input function (DBIF) (21,33,34). The model parameters are often pre-determined by population means that were derived using arterial blood sampling in animal studies (22,35). Due to heterogeneity across patients and potential differences between arterial and image-derived input functions, the population-based DBIF model may become ineffective when arterial blood sampling is not available and image-derived input function is actually used in human studies.

The objective of this paper is to evaluate and identify an appropriate dual-input kinetic modeling approach for FDG kinetic quantification of liver inflammation using human patient data. Besides the population-based DBIF approach, we also examined an optimization-derived DBIF model which employs mathematical optimization to jointly estimate the parameters of DBIF and liver FDG kinetics. This model directly utilizes image-derived aortic input function, requires no arterial blood sampling, and is more adaptive to individual patients. We used both statistical information criteria and histopathologic inflammation data to compare these methods.

MATERIALS AND METHODS

Patient Characteristics

Fourteen patients with NAFLD were included in this study. These patients had a liver biopsy as a part of routine clinical care or for enrollment in clinical trials. The University of California Davis Institutional Review Board and the University of California Davis Medical Center Radiation Use Committee approved the study. All patients signed informed consent before participating in the study. Patients with history of alcohol abuse, chronic hepatitis B or C, or other chronic liver disease other than NAFLD were excluded from the study.

Liver Histopathology

Liver biopsies were performed under ultrasound guidance and scored according to the nonalcoholic steatohepatitis clinical research network (NASH-CRN) criteria (36). The NAFLD activity score (NAS, range 0-8) is comprised of severity of steatosis, inflammation, and hepatocellular ballooning. While a NAS score greater than 4 has been reported to correlate with the presence of NASH, the scores of lobular inflammation and ballooning degeneration are noted to represent hepatic inflammation and injury, and are therefore combined to create an overall “hepatic inflammation” score (range 0-5). In this study, an inflammation score >3 was considered indicative of high inflammation, and a score <3 and score =3 were deemed as low and medium inflammation, respectively.

Dynamic 18F-FDG PET/CT

Scan Protocol

Dynamic 18F-FDG PET studies were performed with the GE Discovery 690 PET/CT scanner at the UC Davis Medical Center. Patients fasted for at least 6 hours. Each patient was injected with 10 mCi 18F-FDG. List-mode time-of-flight data acquisition started right after the intravenous bolus administration and lasted for one hour. At the end of PET scan, a low-dose CT scan was performed for attenuation correction for PET. Dynamic PET data were binned into 49 time frames using the sampling schedule: 30 ⨯ 10s, 10 ⨯ 60s, and 9 ⨯ 300s. Dynamic PET images were then reconstructed using the vendor software with the standard ordered subsets expectation maximization (OSEM) algorithm with 2 iterations and 32 subsets.

Extraction of Blood and Tissue Time Activity Curves (TACs)

Eight spherical regions of interest (ROI), each with 25 mm in diameter, were placed on the eight segments of the liver avoiding any major blood vessels. These ROI placements were tuned and confirmed by an abdominal radiologist. The averaged FDG activity in all 8 ROIs was extracted from the dynamic images to form a global liver TAC of low noise. An additional ROI is placed in the descending aorta region to extract image-derived aortic input function.

FIGURE 1:
FIGURE 1:

Kinetic modeling of dynamic liver FDG-PET data with a dual-blood input function from the hepatic artery and portal vein.

Dual-Input Kinetic Modeling

Compartmental Model with Dual-blood Input Function (DBIF)

FDG kinetics commonly follow the two-tissue compartmental model (29) as shown in Figure 1(a). Glucose transporters transport 18F-FDG from blood to hepatic tissue with the rate constant K1 (mL/min/mL) and from hepatic tissue to blood with the rate k2 (1/min). FDG is phosphorylated by hexokinase in cells into FDG 6-phosphate with the rate k3 (1/min) and the dephosphorylation process occurs with the rate k4 (1/min). Cp(t), Cf(t) and Cm(t represent the FDG concentration in the plasma compartment, free-state FDG in the hepatic tissue compartment, and metabolized FDG 6-phosphate in the tissue, respectively. In traditional kinetic modeling with single-blood input function (SBIF), only blood supply from the aorta is considered and thus the input function Cp(t) is equivalent to the aortic input function CA(t). To account for the effect of dual blood supplies in the liver, a flow-weighted sum of the aortic input CA(t) and portal vein input Cv(t) can be used to model the DBIF (22): Embedded Image where fA is the fraction of hepatic artery contributing to the overall liver blood flow. As shown in Figure 1(b), portal vein can be considered as an additional compartment given FDG in the portal vein flows through the gastrointestinal system with the rate ka (1/min) before entering into the liver. Thus, the portal vein input function Cv(t) follows an analytical solution Embedded Image

This is equivalent to a dispersed version of the aortic input function CA(t) (37). As a result, the combined compartmental model is equivalently described by a set of differential equations: Embedded Image where Embedded Image

The total activity that can be measured by PET is the sum of different compartments: Embedded Image where υb is the fractional blood volume and θ = [υb,K1,k2,k3,k4,ka,fA]T. is a vector collecting all unknown parameters. Note that this DBIF model becomes the traditional SBIF model when ka = 0 and fA = 1.

Joint Estimation of Kinetic and Input Parameters

All model parameters are jointly estimated by fitting a measured liver TAC č using the model equation and following nonlinear least square estimation: Embedded Image where WRSS(θ) denotes the weighted residual sum of squares of the curve fitting and wm denotes the weighting factor for time frame m. We used the classic Levenberg-Marquardt algorithm to solve the optimization problem. In this paper, we refer this joint estimation as the optimization-derived DBIF approach for clarity. It would become equivalent to the population-based DBIF if the parameters fA and ka are assigned with fixed population means (if known). Based on our initial analysis of the patient data sets, we set the initials of kinetic parameters to υb = 0.01, k1 = 1.0, k2 = 1.0, k3 = 0.01, k4 = 0.01, ka = 1.0, fA = 0.01.

Comparison of Kinetic Models

We compared three input models: traditional model with SBIF, model with population-based DBIF and model with optimization-derived DBIF. In this study, the SBIF was derived from the descending aorta region in dynamic FDG-PET images. To utilize reported population means for the population-based DBIF, we used the following portal vein input model (22) Embedded Image which is very similar to the exponential-based model in Eq. 2 but not easily integrated into the differential equations for joint estimation. Embedded Image 0.5 and fA = 0.25 were previously reported for FDG (22).

Comparison Using Statistical Criteria

The three models were compared using two established statistical criteria for model selection for TACs: corrected Akaike information criterion (AIC) and F test (38,39). The AIC is defined by Embedded Image where n denotes the total number of unknown parameters. Note that n=5 for the SBIF and population-based DBIF models and n=7 for the optimization-derived DBIF model. Here AIC was corrected for finite sample sizes due to the ratio Embedded Image. A lower AIC value indicates a better selection of model.

The F test compares a complex model 2 with a simple model 1 using Embedded Image where the degree of freedom (number of unknown parameters) n2<n1. A larger F value indicates better fit. If the p value of F test is 0.05 or less, the model 2 is then considered significantly better than model 1.

Evaluation Using Histopathological Inflammation Data

Patients in this study were divided into three groups according to their histopathological inflammation scores: low inflammation (>3), medium inflammation (=3), and high inflammation (>3). To examine the capability of FDG kinetic parameters for differentiating different inflammation groups, a two-sample 2-sided t-test at the 0.05 level was used to test the difference of group means and the Mann-Whitney U test was used to test difference of group medians. All statistical analyses were conducted using MATLAB (Natick, MA). P-values less than 0.05 were considered as statistically significant in this study.

RESULTS

Patient Characteristics

Majority of the patients were white (75%) while 25% were Hispanics. Female patients formed 67% of the cohort with 75% of the patients between the ages 40-70 years and 25% between 18-39 years. The mean BMI was 34±6 kg/m2. The mean fasting glucose prior to PET scan was 115±33 mg/dL. The patient population had an equitable spread across NAS score (≥5 of 58%). Two thirds of patients had hepatic inflammation sore of ≥3.

Demonstration of dual-blood input function

Figure 2 demonstrates an optimization-derived DBIF from a patient data set. Only the first 5 minutes are shown in the figure and the curves after 5 minutes are very similar among others. The DBIF is a weighted combination of the image-derived SBIF and the portal vein input function derived from the optimization. In this example, the weighting factor fA was 0.19. The DBIF has a much lower peak value than the SBIF because the contribution from hepatic artery is small (19%) while the contribution from portal vein is great (81%).

FIGURE 2:
FIGURE 2:

Example of optimization-derived DBIF and its components from hepatic artery and portal vein: image-derived SBIF and portal vein input function (PVIF).

FIGURE 3:
FIGURE 3:

Fit of a liver TAC using different input models: (A) SBIF, (B) population-based DBIF, (C) optimization-derived DBIF; (D-F) corresponding residual plots of the fits in (A-C).

FIGURE 4:
FIGURE 4:

AIC values of liver TAC fitting with different input function models.

Evaluation of TAC Fit Quality

Figure 3A-3C show the fittings of the liver TAC using the image-derived SBIF, population-based DBIF and optimization-derived DBIF. The residual plots of these fits are shown in Figure 3D-3F. The fitting with SBIF could not fit the early-time peak and late time points due to lack of the portal vein component in the input function. The population-based DBIF provided an improved fit of the peak but still suffered error for late time points due to inaccuracy of the population means for a specific patient data set. A linear trend was observed in the residual plots (Figure 3D and 3E) for these two approaches, indicating a systematic bias in the fitting. The optimization-derived DBIF estimated the input parameters from the data and fit both the peak at early time and late time points closely. The linear trend was removed and the residuals became asymptotically normal.

The AIC values of the three fittings were −81, −140 and −159, respectively. AIC values of the three input models are further compared in Figure 4 for all individual patients in the cohort. The average AIC was −23±30 by SBIF, −69±34 by the population-based DBIF, and −128±34 by the optimization-derived DBIF. The optimization-derived DBIF model had the lowest AIC in all patients.

The results of F test are given in Table 1. The minimum F value among different patients was 80.5 for comparing the optimization-derived DBIF with SBIF and 13.4 for comparing the optimization-derived DBIF with population-based DBIF model, both greater than the F critical value 3.2 calculated with n1 = 5 and n2 = 7 for a p value of 0.05. The p values of F test in individual patients are all small (<0.0001), indicating the optimization-derived DBIF is more appropriate for TAC fitting than the traditional SBIF and population-based DBIF models.

Change in Kinetic Parameters

The three input models resulted in different estimates in kinetic parameters. The mean and standard deviation of kinetic parameters K1,k2,k3,k4 and Ki = K1 k3/(k2 + k3) estimated by the three approaches are listed in Table 2. It is worth noting that a reversible kinetic model (i.e., k4 > 0) was required in our study for accurately modeling FDG TACs in the liver. Neglecting k4 in the model reduced TAC fit quality with higher AIC values (results not shown). Compared with the image-derived SBIF, the optimization-derived DBIF significantly increased the mean values of K1(0.5112 vs 0.9829), k2 (0.4983 vs 1.1053), k3 (0.0008 vs 0.0141) and Ki (0.0008 vs 0.0119), all with p<0.0001. The difference in K1 in individual patient was 101% on average and up to 150%, as shown in Figure 5. Compared with the population-based DBIF, the optimization-derived DBIF significantly increased the mean values of k3 (0.0011 vs 0.0141, p<0.0001), k4 (0.0196 vs 0.0534, p=0.0034) and Ki (0.0010 vs 0.0119, p<0.0001). Although the mean values of by the optimization-derived DBIF and population-based DBIF models are similar to each other (0.9787 vs. 0.9829), K1 values by the two models were very different in each individual patient (Figure 5). The change was 27% on average and up to 44%.

View this table:
TABLE 1:

F statistics of model comparison (F critical value is 3.2 at the significance level of 0.05)

View this table:
TABLE 2:

Mean and standard deviation of FDG kinetic parameters estimated by different input function approaches

FIGURE 5:
FIGURE 5:

FDG Ki values estimated by different input function models.

FIGURE 6:
FIGURE 6:

Association of histopathological inflammation score with FDG-K1 estimated by different input models. (A) Image-derived SBIF; (B) Population-based DBIF; (C) Optimization-derived DBIF.

View this table:
TABLE 3:

P values of t test and U test for comparing in different liver inflammation groups using FDG K1 estimated by SBIF, population-based (PB) DBIF, and optimization-derived (OD) DBIF.

Association with Histopathologic Data

Figure 6 shows the associations of histopathologic grades of liver inflammation with FDG K1 by different input models. The standard boxplots reflect the median, 25% percentile and 75% percentile of K1 in each of low, medium and high inflammation groups. Overall, FDG K1 value decreased as inflammation grade increased. The results of statistical t test for comparing group means and U test for comparing group medians are summarized in Table 3. Neither the SBIF model nor the population-based DBIF model could differentiate low, medium and high inflammation groups (p>0.05). In comparison, K1 by the optimization-derived DBIF model was better associated with the inflammation grades and differentiated low versus high inflammation groups and medium versus high inflammation groups (p<0.05).

DISCUSSION

Determination of liver inflammation in nonalcoholic fatty liver disease patients is crucial in differentiating serious NASH from simple hepatic steatosis. Dynamic FDG-PET with kinetic modeling has the potential to provide a noninvasive imaging biomarker for characterizing hepatic inflammation. Accurate kinetic modeling of dynamic liver FDG-PET data requires consideration of the effect of dual-blood supplies in the liver. Although the input function from the hepatic artery can be derived from the aorta in the dynamic images, it is difficult to derive the portal vein input function from dynamic PET because the size of portal vein (10-15mm) is small compared to the spatial resolution of clinical PET scanner (4-8mm). Partial volume effects, noise and respiratory motion all can contaminate the accuracy of the portal vein input function.

The standard SBIF model simply neglects the portal vein input and therefore provides inaccurate estimation of FDG kinetic parameters. The K1 parameter, which is the major parameter of interest for liver inflammation, is generally underestimated by SBIF, as demonstrated in this study. To account for the dual-blood effect, we applied the population-based DBIF model (21,33) in this study. It improved TAC fitting with lower AIC and higher F values than SBIF but did not improve association of the FDG biomarker with inflammation grades. This can be explained by the fact that the population model is not adaptive to individual objects and thus might not provide the optimal estimation. In addition, the aortic input function was derived from PET images while the population-based model was derived using arterial sampling, resulting in further suboptimal performance.

The optimization-derived DBIF model provided the best performance according to TAC fitting quality and association with histopathological inflammation grades. Because the parameters of the input model were jointly estimated with liver tissue kinetic parameters, the estimation was more adaptive to individual patients and achieved lower AIC and higher F values. In this study, the two DBIF model parameters ka and fA were 1. 627±1.427 and 0.044±0.054. Here the fA estimates were far smaller than the population mean 0.25 that was reported based on arterial sampling. This is possibly explained by the difference between image-derived input function and arterial blood input. The resulting K1 parameter estimates were significantly different from those by SBIF and optimization-based DBIF. Although there was no ground truth of K1 values for validating the estimates, evaluation of statistical information criteria and analysis of association of K1 with liver inflammation grades provided a feasible way to prove the improved performance of the modified DBIF model.

In addition to establishing the kinetic modeling method for dynamic FDG-PET characterization of liver inflammation, this work adds a new contribution to the methodology of dual-input kinetic modeling. We extended and validated the method of DBIF modeling using a joint optimization framework. Previously, Kudomi et al estimated both aortic input function and parameters of input model from multiple regional liver TACs in dynamic FDG-PET (34). Their method, however, is more complex for practical use with multiple steps involved. When the tracer distribution is uniform in the liver, TAC data would also be less helpful for the estimation of the many parameters in their model. Our method is simpler and can be implemented voxel-by-voxel to easily derive parametric images. The proposed method shares a similar spirit with the work by Chen and Feng (40) designed for modeling a different tracer 11C-acetate. The compartmental model used in (40) was irreversible while a reversible model was required in our study for FDG modeling. The major parameter of interest was Kt in their study, while in this study we are mainly interested in FDG K1. Thus, the current work has complemented existing studies to establish the optimization-derived DBIF modeling method.

Another notable aspect of this work is the validation of dual-input kinetic modeling using histopathological reference. All existing studies of dual-input kinetic modeling were only able to validate the DBIF approaches in animal studies (e.g., pigs (33,35), foxhounds (21)) or demonstrated the improvement using statistical TAC fitting quality in human patients (40). No studies had demonstrated an impact on physiological measurements by using a histopathological ground truth. In comparison, the present work provided a direct evidence on the impact of DBIF on improving association of the FDG-PET biomarker with histopathological inflammation in human patients.

There are limitations with the present study. Histopathological inflammation can be varying across different spatial locations in the liver. The statistical analysis of PET data in the study was not specific to the location of biopsy. This is because accurate biopsy location information was difficult to obtain even with ultrasound guidance and not available in this study. We chose to analyze the PET data based on the whole liver region to achieve robustness to locations and to noise in PET data. It is noted that all the studying kinetic models did not achieve a statistical significance (p>0.05) in differentiating low versus medium inflammation levels (Table 3), though the FDG K1 values by the optimization-derived DBIF model tended to differ in the two groups (Fig. 6C). This might be limited by the number of samples (N=14) in this study and is worth further investigation in a large number of samples.

CONCLUSION

In this study, we examined three different kinetic models for analyzing dynamic FDG-PET data for characterizing liver inflammation. Statistical fit quality metrics and analysis of association with histopathology indicated that modeling of DBIF is crucial for accurate kinetic modeling of liver time activity curves. The optimization-derived DBIF model improved the association of FDG K1 with liver inflammation grades and was more appropriate than traditional single-blood input function and population-based DBIF for dynamic FDG-PET kinetic analysis in human NASH studies.

REFERENCES

  1. 1.↵
    Rinella ME. Nonalcoholic Fatty Liver Disease A Systematic Review. JAMA-Journal of the American Medical Association. 2015;313:2263–2273.
    OpenUrlCrossRefPubMed
  2. 2.↵
    Loomba R, Sanyal AJ. The global NAFLD epidemic. Nature Reviews Gastroenterology & Hepatology. 2013;10:686–690.
    OpenUrl
  3. 3.↵
    Michelotti GA, Machado MV, Diehl AM. NAFLD, NASH and liver cancer. Nature Reviews Gastroenterology & Hepatology. 2013;10:656–665.
    OpenUrlCrossRefPubMed
  4. 4.↵
    Musso G, Gambino R, Cassader M, Pagano G. Meta-analysis: Natural history of non-alcoholic fatty liver disease (NAFLD) and diagnostic accuracy of non-invasive tests for liver disease severity. Annals of Medicine. 2011;43:617–649.
    OpenUrlCrossRefPubMed
  5. 5.↵
    Wree A, Broderick L, Canbay A, Hoffman HM, Feldstein AE. From NAFLD to NASH to cirrhosis - new insights into disease mechanisms. Nature Reviews Gastroenterology & Hepatology. 2013;10:627–636.
    OpenUrlCrossRefPubMedWeb of Science
  6. 6.↵
    Bohte AE, van Werven JR, Bipat S, Stoker J. The diagnostic accuracy of US, CT, MRI and H-1-MRS for the evaluation of hepatic steatosis compared with liver biopsy: a meta-analysis. European Radiology. 2011;21:87–97.
    OpenUrlCrossRefPubMed
  7. 7.↵
    Mishra P, Younossi Z. Abdominal ultrasound for diagnosis of nonalcoholic fatty liver disease (NAFLD). American Journal of Gastroenterology. 2007;102:2716–2717.
    OpenUrlCrossRefPubMed
  8. 8.↵
    Pickhardt PJ, Park SH, Hahn L, Lee SG, Bae KT, Yu ES. Specificity of unenhanced CT for non-invasive diagnosis of hepatic steatosis: implications for the investigation of the natural history of incidental steatosis. European Radiology. 2012;22:1075–1082.
    OpenUrlCrossRefPubMed
  9. 9.↵
    Fishbein M, Castro F, Cheruku S, et al. Hepatic MRI for fat quantitation - Its relationship to fat morphology, diagnosis, and ultrasound. Journal of Clinical Gastroenterology. 2005;39:619–625.
    OpenUrlCrossRefPubMedWeb of Science
  10. 10.
    Reeder SB, Hu HCH, Sirlin CB. Proton density fat-fraction: A standardized mr-based biomarker of tissue fat concentration. Journal of Magnetic Resonance Imaging. 2012;36:1011–1014.
    OpenUrlCrossRefPubMed
  11. 11.↵
    Tang A, Tan J, Sun M, et al. Nonalcoholic Fatty Liver Disease: MR Imaging of Liver Proton Density Fat Fraction to Assess Hepatic Steatosis. Radiology. 2013;267:422–431.
    OpenUrlCrossRefPubMed
  12. 12.↵
    Mariappan YK, Glaser KJ, Ehman RL. Magnetic Resonance Elastography: A Review. Clinical Anatomy. 2010;23:497–511.
    OpenUrlCrossRefPubMedWeb of Science
  13. 13.↵
    Friedrich-Rust M, Wunder K, Kriener S, et al. Liver Fibrosis in Viral Hepatitis: Noninvasive Assessment with Acoustic Radiation Force Impulse Imaging versus Transient Elastography. Radiology. 2009;252:595–604.
    OpenUrlCrossRefPubMedWeb of Science
  14. 14.↵
    Huwart L, Peeters F, Sinkus R, et al. Liver fibrosis: non-invasive assessment with MR elastography. Nmr in Biomedicine. 2006;19:173–179.
    OpenUrlCrossRefPubMedWeb of Science
  15. 15.
    Yin M, Talwalkar JA, Glaser KJ, et al. Assessment of hepatic fibrosis with magnetic resonance elastography. Clinical Gastroenterology and Hepatology. 2007;5:1207–1213.
    OpenUrl
  16. 16.↵
    Chen J, Talwalkar JA, Yin M, Glaser KJ, Sanderson SO, Ehman RL. Early Detection of Nonalcoholic Steatohepatitis in Patients with Nonalcoholic Fatty Liver Disease by Using MR Elastography. Radiology. 2011;259:749–756.
    OpenUrlCrossRefPubMedWeb of Science
  17. 17.↵
    Lee SS, Park SH. Radiologic evaluation of nonalcoholic fatty liver disease. World Journal of Gastroenterology. 2014;20:7392–7402.
    OpenUrl
  18. 18.↵
    Milic S, Stimac D. Nonalcoholic Fatty Liver Disease/Steatohepatitis: Epidemiology, Pathogenesis, Clinical Presentation and Treatment. Digestive Diseases. 2012;30:158–162.
    OpenUrlCrossRefPubMed
  19. 19.↵
    Love C, Tomas MB, Tronco GG, Palestro CJ. FDG PET of infection and inflammation. Radiographics. 2005;25:1357–1368.
    OpenUrlCrossRefPubMedWeb of Science
  20. 20.↵
    Rudd JHF,Myers KS, Bansilal S, et al. Atherosclerosis inflammation Imaging with F-18-FDG PET: Carotid, iliac, and femoral uptake reproducibility, quantification methods, and recommendations. Journal of Nuclear Medicine. 2008;49:871–878.
    OpenUrlAbstract/FREE Full Text
  21. 21.↵
    Brix G, Ziegler SI, Bellemann ME, et al. Quantification of F-18 FDG uptake in the normal liver using dynamic PET: Impact and modeling of the dual hepatic blood supply. Journal of Nuclear Medicine. 2001;42:1265–1273.
    OpenUrlAbstract/FREE Full Text
  22. 22.↵
    Keiding S. Bringing Physiology into PET of the Liver. Journal of Nuclear Medicine. 2012;53:425–433.
    OpenUrlAbstract/FREE Full Text
  23. 23.
    Tragardh M, Moller N,Sorensen M. Methodologic Considerations for Quantitative F-18-FDG PET/CT Studies of Hepatic Glucose Metabolism in Healthy Subjects. Journal of Nuclear Medicine. 2015;56:1366–1371.
    OpenUrlAbstract/FREE Full Text
  24. 24.
    Borra R, Lautamaki R, Parkkola R, et al. Inverse association between liver fat content and hepatic glucose uptake in patients with type 2 diabetes mellitus. Metabolism-Clinical and Experimental. 2008;57:1445–1451.
    OpenUrlCrossRefPubMedWeb of Science
  25. 25.
    Iozzo P, Jarvisalo MJ, Kiss J, et al. Quantification of liver glucose metabolism by positron emission tomography: Validation study in pigs. Gastroenterology. 2007;132:531–542.
    OpenUrlCrossRefPubMed
  26. 26.
    Abikhzer G,Alabed YZ, Azoulay L, Assayag J, Rush C. Altered Hepatic Metabolic Activity in Patients With Hepatic Steatosis on FDG PET/CT. American Journal of Roentgenology. 2011;196:176–180.
    OpenUrlCrossRefPubMed
  27. 27.↵
    Lin CY, Lin WY, Lin CC, Shih CM, Jeng LB, Kao CH. The negative impact of fatty liver on maximum standard uptake value of liver on FDG PET. Clinical Imaging. 2011;35:437–441.
    OpenUrlCrossRefPubMed
  28. 28.↵
    Wang G, Corwin M, Olson K, et al. Dynamic FDG-PET study of liver inflammation in non-alcoholic fatty liver disease. Journal of Hepatology. 2017;66:S592 (abstract only).
    OpenUrl
  29. 29.↵
    Schmidt KC, Turkheimer FE. Kinetic modeling in positron emission tomography. Quarterly Journal of Nuclear Medicine. 2002;46:70–85.
    OpenUrlPubMed
  30. 30.↵
    Chen K, Bandy D, Reiman E, et al. Noninvasive quantification of the cerebral metabolic rate for glucose using positron emission tomography, F-18-fluoro-2-deoxyglucose, the Patlak method, and an image-derived input function. Journal of Cerebral Blood Flow and Metabolism. 1998;18:716–723.
    OpenUrlCrossRefPubMedWeb of Science
  31. 31.
    van der Weerdt AP, Klein LJ, Boellaard R, Visser CA, Visser FC, Lammertsma AA. Image-derived input functions for determination of MRGlu in cardiac F-18-FDG PET scans. Journal of Nuclear Medicine. 2001;42:1622–1629.
    OpenUrlAbstract/FREE Full Text
  32. 32.↵
    Zanotti-Fregonara P, Hines CS, Zoghbi SS, et al. Population-based input function and image-derived input function for C-11 (R)-rolipram PET imaging: Methodology, validation and application to the study of major depressive disorder. Neuroimage. 2012;63:1532–1541.
    OpenUrlCrossRefPubMed
  33. 33.↵
    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. Journal of Nuclear Medicine. 2001;42:795–801.
    OpenUrlAbstract/FREE Full Text
  34. 34.↵
    Kudomi N, Jarvisalo MJ, Kiss J, et al. Non-invasive estimation of hepatic glucose uptake from F-18 FDG PET images using tissue-derived input functions. European Journal of Nuclear Medicine and Molecular Imaging. 2009;36:2014–2026.
    OpenUrlCrossRefPubMed
  35. 35.↵
    Winterdahl M, Munk OL, Sorensen M, Mortensen FV, Keiding S. Hepatic Blood Perfusion Measured by 3-Minute Dynamic F-18-FDG PET in Pigs. Journal of Nuclear Medicine. 2011;52:1119–1124.
    OpenUrlAbstract/FREE Full Text
  36. 36.↵
    Kleiner DE, Brunt EM, Van Natta M, et al. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology. 2005;41:1313–1321.
    OpenUrlCrossRefPubMedWeb of Science
  37. 37.↵
    Iida H, Kanno I, Miura S, Murakami M, Takahashi K, Uemura K. Error analysis of a quantitative cerebral blood flow measurement using H2(15)O autoradiography and positron emission tomography, with respect to the dispersion of the input function. Journal of Cerebral Blood Flow and Metabolism. 1986;6:536–545.
    OpenUrlCrossRefPubMedWeb of Science
  38. 38.↵
    Glatting G, Kletting P, Reske SN, Hohl K, Ring C. Choosing the optimal fit function: Comparison of the Akaike information criterion and the F-test. Medical Physics. 2007;34:4285–4292.
    OpenUrlCrossRefPubMed
  39. 39.↵
    Richard MA, Fouquet JP, Lebel R, Lepage M. Determination of an Optimal Pharmacokinetic Model of F-18-FET for Quantitative Applications in Rat Brain Tumors. Journal of Nuclear Medicine. 2017;58:1278–1284.
    OpenUrlAbstract/FREE Full Text
  40. 40.↵
    Chen S, Feng D. Evaluation of hepatocellular carcinoma with dynamic C-11-acetate PET: A dualmodeling method. IEEE Transactions on Nuclear Science. 2008;55:999–1007.
    OpenUrl
Back to top
Posted February 20, 2018.
Download PDF
Email
Dynamic PET of Human Liver Inflammation: Impact of Kinetic Modeling with Optimization-Derived Dual-Blood Input Function
Guobao Wang, Michael T. Corwin, Kristin A. Olson, Ramsey D. Badawi, Souvik Sarkar
bioRxiv 268748; doi: https://doi.org/10.1101/268748
Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools

Subject Area