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

2018 ◽  
Author(s):  
Guobao Wang ◽  
Michael T. Corwin ◽  
Kristin A. Olson ◽  
Ramsey D. Badawi ◽  
Souvik Sarkar
Keyword(s):  
Kinetic Analysis ◽  
Kinetic Modeling ◽  
Fdg Pet ◽  
Human Liver ◽  
Input Function ◽  
Population Based ◽  
Joint Estimation ◽  
Human Studies ◽  
Liver Inflammation ◽  
Time Activity

ABSTRACTThe 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.

scholarly journals Population-based input function for TSPO quantification and kinetic modeling with [11C]-DPA-713

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mercy I. Akerele ◽  
Sara A. Zein ◽  
Sneha Pandya ◽  
Anastasia Nikolopoulou ◽  
Susan A. Gauthier ◽  
...  
Keyword(s):  
Healthy Volunteers ◽  
Kinetic Analysis ◽  
Kinetic Modeling ◽  
Input Function ◽  
Brain Regions ◽  
Population Based ◽  
Patient Specific ◽  
Systematic Bias ◽  
Altman Analysis ◽  
Bland Altman Analysis

Abstract Introduction Quantitative positron emission tomography (PET) studies of neurodegenerative diseases typically require the measurement of arterial input functions (AIF), an invasive and risky procedure. This study aims to assess the reproducibility of [11C]DPA-713 PET kinetic analysis using population-based input function (PBIF). The final goal is to possibly eliminate the need for AIF. Materials and methods Eighteen subjects including six healthy volunteers (HV) and twelve Parkinson disease (PD) subjects from two [11C]-DPA-713 PET studies were included. Each subject underwent 90 min of dynamic PET imaging. Five healthy volunteers underwent a test-retest scan within the same day to assess the repeatability of the kinetic parameters. Kinetic modeling was carried out using the Logan total volume of distribution (VT) model. For each data set, kinetic analysis was performed using a patient-specific AIF (PSAIF, ground-truth standard) and then repeated using the PBIF. PBIF was generated using the leave-one-out method for each subject from the remaining 17 subjects and after normalizing the PSAIFs by 3 techniques: (a) Weightsubject×DoseInjected, (b) area under AIF curve (AUC), and (c) Weightsubject×AUC. The variability in the VT measured with PSAIF, in the test-retest study, was determined for selected brain regions (white matter, cerebellum, thalamus, caudate, putamen, pallidum, brainstem, hippocampus, and amygdala) using the Bland-Altman analysis and for each of the 3 normalization techniques. Similarly, for all subjects, the variabilities due to the use of PBIF were assessed. Results Bland-Altman analysis showed systematic bias between test and retest studies. The corresponding mean bias and 95% limits of agreement (LOA) for the studied brain regions were 30% and ± 70%. Comparing PBIF- and PSAIF-based VT estimate for all subjects and all brain regions, a significant difference between the results generated by the three normalization techniques existed for all brain structures except for the brainstem (P-value = 0.095). The mean % difference and 95% LOA is −10% and ±45% for Weightsubject×DoseInjected; +8% and ±50% for AUC; and +2% and ± 38% for Weightsubject×AUC. In all cases, normalizing by Weightsubject×AUC yielded the smallest % bias and variability (% bias = ±2%; LOA = ±38% for all brain regions). Estimating the reproducibility of PBIF-kinetics to PSAIF based on disease groups (HV/PD) and genotype (MAB/HAB), the average VT values for all regions obtained from PBIF is insignificantly higher than PSAIF (%difference = 4.53%, P-value = 0.73 for HAB; and %difference = 0.73%, P-value = 0.96 for MAB). PBIF also tends to overestimate the difference between PD and HV for HAB (% difference = 32.33% versus 13.28%) and underestimate it in MAB (%difference = 6.84% versus 20.92%). Conclusions PSAIF kinetic results are reproducible with PBIF, with variability in VT within that obtained for the test-retest studies. Therefore, VT assessed using PBIF-based kinetic modeling is clinically feasible and can be an alternative to PSAIF.

scholarly journals Population-Based Input Function for TSPO Quantification and Kinetic Modeling with [11C]-DPA-713

2021 ◽  
Author(s):  
Mercy Iyabode Akerele ◽  
Sara A. Zein ◽  
Sneha Pandya ◽  
Anastasia Nikolopoulou ◽  
Susan A. Gauthier ◽  
...  
Keyword(s):  
Healthy Volunteers ◽  
Kinetic Analysis ◽  
Kinetic Modeling ◽  
Input Function ◽  
Brain Regions ◽  
Population Based ◽  
Patient Specific ◽  
Systematic Bias ◽  
Altman Analysis ◽  
Bland Altman Analysis

Abstract Introduction: Quantitative positron emission tomography (PET) studies of neurodegenerative diseases typically require the measurement of arterial input functions (AIF), an invasive and risky procedure. This study aims to assess the reproducibility of [11C]DPA-713 PET kinetic analysis using population-based input function (PBIF). The final goal is to possibly eliminate the need for AIF. Materials and Methods: Eighteen subjects including six healthy volunteers (HV) and twelve Parkinson disease (PD) subjects from two [11C]-DPA-713 PET studies were included. Each subject underwent 90 minutes of dynamic PET imaging. Five healthy volunteers underwent a test-retest scan within the same day to assess the repeatability of the kinetic parameters. Kinetic modeling was carried out using the Logan total volume of distribution (VT) model. For each data set, kinetic analysis was performed using a patient specific AIF (PSAIF, ground-truth standard), and then repeated using the PBIF. PBIF was generated using the leave-one-out method for each subject from the remaining 17 subjects, and after normalizing the PSAIFs by 3 techniques: (a) Weightsubject×DoseInjected (b) Area Under AIF Curve (AUC), and (c) Weightsubject×AUC. The variability in the total distribution volume (VT) measured with PSAIF, in the test/retest study were determined for selected brain regions (white matter, cerebellum, thalamus, caudate, putamen, pallidum, brainstem, hippocampus and amygdala) using the Bland-Altman analysis, and for each of the 3 normalization techniques. Similarly, for all subjects, the variabilities due to the use of PBIF were assessed.Results: Bland-Altman analysis showed systematic bias between test and retest studies. The corresponding mean bias and 95% limits of agreement (LOA) for the studied brain regions were 30% and ±70%. Comparing PBIF- and PSAIF-based VT estimate for all subjects and all brain regions, a significant difference between the results generated by the three normalization techniques existed for all brain structures except for the brainstem (P-value = 0.095). The mean % difference and 95% LOA is -10% and ±45% for Weightsubject×DoseInjected; +8% and ±50% for AUC; and +2% and ±38% for Weightsubject×AUC. In all cases, normalizing by Weightsubject×AUC yielded the smallest % bias and variability (% bias = ±2%; LOA = ±38% for all brain regions. Estimating the reproducibility of PBIF-kinetics to PSAIF based on disease groups (HV/PD) and genotype (MAB/ HAB), the average VT values for all regions obtained from PBIF is insignificantly higher than PSAIF (%difference = 4.53%, P-value = 0.73 for HAB; and %difference = 0.73%, P-value = 0.96 for MAB). PBIF also tends to overestimate the difference between PD and HV for HAB (%difference = 32.33% versus 13.28%) and underestimate it in MAB (%difference = 6.84% versus 20.92%). Conclusions: PSAIF kinetic results are reproducible with PBIF, with variability in VT within that obtained for the test-retest studies. Therefore, VT assessed using PBIF-based kinetic modeling is clinically feasible, and can be an alternative to PSAIF.

scholarly journals Feasibility of Population-Based Input Function for Kinetic Modeling of [11C]-DPA-713

2020 ◽  
Author(s):  
Mercy Iyabode Akerele ◽  
Sara Zein ◽  
Sneha Pandya ◽  
Anastasia Nikolopoulou ◽  
Susan A. Gauthier ◽  
...  
Keyword(s):  
Kinetic Analysis ◽  
Kinetic Modeling ◽  
Gray Matter ◽  
Input Function ◽  
Brain Regions ◽  
Population Based ◽  
Patient Specific ◽  
Systematic Bias ◽  
Altman Analysis ◽  
Bland Altman Analysis

Abstract Introduction: Quantitative positron emission tomography (PET) studies of neurodegenerative diseases typically require the measurement of arterial input functions (AIF), an invasive and risky procedure. This study aims to assess the reproducibility of [ 11 C]DPA-713 PET kinetic analysis using population-based input function (PBIF). The final goal is to possibly eliminate the need for AIF. Materials and Methods: Eighteen subjects including six healthy controls (HC) and twelve Parkinson disease (PD) subjects from two [ 11 C]-DPA-713 PET studies were included. Each subject underwent 90 minutes of dynamic PET imaging. Five healthy subjects underwent a test-retest scan within the same day to assess the repeatability of the kinetic parameters. Kinetic modeling was carried out using the Logan total volume of distribution (VT) model. For each data set, kinetic analysis was performed using a patient specific AIF (PSAIF, ground-truth standard), and then repeated using the PBIF. PBIF was generated using the leave-one-out method for each subject from the remaining 17 subjects, and after normalizing the PSAIFs by 3 techniques: (a) Weight subject ×Dose Injected (b) Area Under AIF Curve (AUC), and (c) Weight subject ×AUC. The variability in the total distribution volume (V T ) measured with PSAIF, in the test/retest study were determined for selected brain regions (white matter, cerebellum, thalamus, caudate, putamen, pallidum, brainstem, hippocampus and amygdala) using the Bland-Altman analysis, and for each of the 3 normalization techniques. Similarly, for all subjects, the variabilities due to the use of PBIF were assessed. Results: Bland-Altman analysis showed systematic bias between test and retest studies, which was reduced after normalizing the V T estimate by the corresponding gray matter value. The corresponding mean bias and 95% limits of agreement (LOA) for the studied brain regions were 30% and 5%; ±70% and ±20% without and with gray matter normalization respectively. Comparing PBIF- and PSAIF-based VT estimate for all subjects and all brain regions, normalization by Weight subject ×AUC yielded the smallest 95% LOA among the three normalization techniques (±12%, ±13% and ±10% for Weight subject ×Dose Injected , AUC and Weight subject ×AUC respectively). Conclusions: The variability in VT is within that obtained for the test-retest studies. Therefore, VT assessed using PBIF-based kinetic modeling is clinically feasible, and can be an alternative to PSAIF.

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

2018 ◽  
Vol 63 (15) ◽  
pp. 155004 ◽  
Author(s):  
Guobao Wang ◽  
Michael T Corwin ◽  
Kristin A Olson ◽  
Ramsey D Badawi ◽  
Souvik Sarkar
Keyword(s):  
Kinetic Modeling ◽  
Human Liver ◽  
Input Function ◽  
Dynamic Pet ◽  
Liver Inflammation

scholarly journals Kinetic Analysis of the 5-HT2A Ligand [11C]MDL 100,907

2000 ◽  
Vol 20 (6) ◽  
pp. 899-909 ◽  
Author(s):  
Hiroshi Watabe ◽  
Michael A. Channing ◽  
Margaret G. Der ◽  
H. Richard Adams ◽  
Elaine Jagoda ◽  
...  
Keyword(s):  
Kinetic Analysis ◽  
Specific Binding ◽  
Compartment Model ◽  
Input Function ◽  
Brain Regions ◽  
Time Activity ◽  
Tissue Compartment ◽  
Arterial Blood ◽  
Fixed Parameter ◽  
Mdl 100,907

The goal of this study was to develop a suitable kinetic analysis method for quantification of 5-HT2A receptor parameters with [11C]MDL 100,907. Twelve control studies and four preblocking studies (400 nmol/kg unlabeled MDL 100,907) were performed in isoflurane-anesthetized rhesus monkeys. The plasma input function was determined from arterial blood samples with metabolite measurements by extraction in ethyl acetate. The preblocking studies showed that a two-tissue compartment model was necessary to fit the time activity curves of all brain regions including the cerebellum—in other words, the need for two compartments is not proof of specific binding. Therefore, a three-tissue compartment model was used to analyze the control studies, with three parameters fixed based on the preblocking data. Reliable fits of control data could be obtained only if no more than three parameters were allowed to vary. For routine use of [11C]MDL 100,907, several simplified methods were evaluated. A two-tissue (2T‘) compartment with one fixed parameter was the most reliable compartmental approach; a one-compartment model failed to fit the data adequately. The Logan graphical approach was also tested and produced comparable results to the 2T’ model. However, a simulation study showed that Logan analysis produced a larger bias at higher noise levels. Thus, the 2T' model is the best choice for analysis of [11C]MDL 100,907 studies.

scholarly journals Structural and Practical Identifiability of Dual-input Kinetic Modeling in Dynamic PET of Liver Inflammation

2018 ◽  
Author(s):  
Yang Zuo ◽  
Souvik Sarkar ◽  
Michael T. Corwin ◽  
Kristin Olson ◽  
Ramsey D. Badawi ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Kinetic Model ◽  
Portal Vein ◽  
Kinetic Modeling ◽  
Input Function ◽  
Model Parameters ◽  
Dynamic Pet ◽  
Liver Inflammation ◽  
Practical Identifiability

AbstractDynamic 18F-FDG PET with tracer kinetic modeling has the potential to noninvasively evaluate human liver inflammation using the FDG blood-to-tissue transport rate K1. Accurate kinetic modeling of dynamic liver PET data and K1 quantification requires the knowledge of dual-blood input function from the hepatic artery and portal vein. While the arterial input function can be derived from the aortic region on dynamic PET images, it is difficult to extract the portal vein input function accurately from PET. The optimization-derived dual-input kinetic modeling approach has been proposed to overcome this problem by jointly estimating the portal vein input function and FDG tracer kinetics from time activity curve fitting. In this paper, we further characterize the model properties by analyzing the structural identifiability of the model parameters using the Laplace transform and practical identifiability using Monte Carlo simulation based on fourteen patient datasets. The theoretical analysis has indicated that all the kinetic parameters of the dual-input kinetic model are structurally identifiable, though subject to local solutions. The Monte Carlo simulation results have shown that FDG K1 can be estimated reliably in the whole-liver region of interest with reasonable bias, standard deviation, and high correlation between estimated and original values, indicating of practical identifiability of K1. The result has also demonstrated the correlation between K1 and histological liver inflammation scores is reliable. FDG K1 quantification by the optimization-derived dual-input kinetic model is promising for assessing liver inflammation.

scholarly journals Validation and Reproducibility of Measurement of 5-HT1A Receptor Parameters with [carbonyl-11C]WAY-100635 in Humans: Comparison of Arterial and Reference Tissue Input Functions

2000 ◽  
Vol 20 (7) ◽  
pp. 1111-1133 ◽  
Author(s):  
Ramin V. Parsey ◽  
Mark Slifstein ◽  
Dah-Ren Hwang ◽  
Anissa Abi-Dargham ◽  
Norman Simpson ◽  
...  
Keyword(s):  
Human Brain ◽  
Kinetic Analysis ◽  
Intraclass Correlation ◽  
Input Function ◽  
Graphical Analysis ◽  
Binding Potential ◽  
Reference Tissue ◽  
Time Activity ◽  
Way 100635 ◽  
Arterial Plasma

Serotonin 5-HT1A receptors are implicated in the pathophysiology of neuropsychiatric conditions. The goal of this study was to evaluate methods to derive 5-HT1A receptor parameters in the human brain with positron emission tomography (PET) and [ carbonyl-11C]WAY 100635. Five healthy volunteer subjects were studied twice. Three methods of analysis were used to derive the binding potential (BP), and the specific to nonspecific equilibrium partition coefficient (k3/k4). Two methods, kinetic analysis based on a three compartment model and graphical analysis, used the arterial plasma time-activity curves as the input function to derive BP and k3/k4. A third method, the simplified reference tissue model (SRTM), derived the input function from uptake data of a region of reference, the cerebellum, and provided only k3/k4. All methods provided estimates of regional 5-HT1A receptor parameters that were highly correlated. Results were consistent with the known distribution of 5-HT1A receptors in the human brain. Compared with kinetic BP, graphical analysis slightly underestimated BP, and this phenomenon was mostly apparent in small size-high noise regions. Compared with kinetic k3/k4, the reference tissue method underestimated k3/k4 and the underestimation was apparent primarily in regions with high receptor density. Derivation of BP by both kinetic and graphical analysis was highly reliable, with an intraclass correlation coefficient (ICC) of 0.84 ± 0.14 (mean ± SD of 15 regions) and 0.84 ± 0.19, respectively. In contrast, the reliability of k3/k4 was lower, with ICC of 0.53 ± 0.28, 0.47 ± 0.28, and 0.55 ± 0.29 for kinetic, graphical, and reference tissue methods, respectively. In conclusion, derivation of BP by kinetic analysis using the arterial plasma input function appeared as the method of choice because of its higher test—retest reproducibility, lower vulnerability to experimental noise, and absence of bias.

scholarly journals Assessment of population-based input functions for Patlak imaging of whole body dynamic 18F-FDG PET

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Mika Naganawa ◽  
Jean-Dominique Gallezot ◽  
Vijay Shah ◽  
Tim Mulnix ◽  
Colin Young ◽  
...  
Keyword(s):  
Plasma Concentration ◽  
Gold Standard ◽  
Fdg Pet ◽  
Time Windows ◽  
Input Function ◽  
Population Based ◽  
Blood Sampling ◽  
Whole Body ◽  
Arterial Blood Sampling ◽  
Arterial Blood

Abstract Background Arterial blood sampling is the gold standard method to obtain the arterial input function (AIF) for quantification of whole body (WB) dynamic 18F-FDG PET imaging. However, this procedure is invasive and not typically available in clinical environments. As an alternative, we compared AIFs to population-based input functions (PBIFs) using two normalization methods: area under the curve (AUC) and extrapolated initial plasma concentration (CP*(0)). To scale the PBIFs, we tested two methods: (1) the AUC of the image-derived input function (IDIF) and (2) the estimated CP*(0). The aim of this study was to validate IDIF and PBIF for FDG oncological WB PET studies by comparing to the gold standard arterial blood sampling. Methods The Feng 18F-FDG plasma concentration model was applied to estimate AIF parameters (n = 23). AIF normalization used either AUC(0–60 min) or CP*(0), estimated from an exponential fit. CP*(0) is also described as the ratio of the injected dose (ID) to initial distribution volume (iDV). iDV was modeled using the subject height and weight, with coefficients that were estimated in 23 subjects. In 12 oncological patients, we computed IDIF (from the aorta) and PBIFs with scaling by the AUC of the IDIF from 4 time windows (15–45, 30–60, 45–75, 60–90 min) (PBIFAUC) and estimated CP*(0) (PBIFiDV). The IDIF and PBIFs were compared with the gold standard AIF, using AUC values and Patlak Ki values. Results The IDIF underestimated the AIF at early times and overestimated it at later times. Thus, based on the AUC and Ki comparison, 30–60 min was the most accurate time window for PBIFAUC; later time windows for scaling underestimated Ki (− 6 ± 8 to − 13 ± 9%). Correlations of AUC between AIF and IDIF, PBIFAUC(30–60), and PBIFiDV were 0.91, 0.94, and 0.90, respectively. The bias of Ki was − 9 ± 10%, − 1 ± 8%, and 3 ± 9%, respectively. Conclusions Both PBIF scaling methods provided good mean performance with moderate variation. Improved performance can be obtained by refining IDIF methods and by evaluating PBIFs with test-retest data.

scholarly journals Dynamic 18F-FDG PET imaging of liver lesions: evaluation of a two-tissue compartment model with dual blood input function

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jingnan Wang ◽  
Yunwen Shao ◽  
Bowei Liu ◽  
Xuezhu Wang ◽  
Barbara Katharina Geist ◽  
...  
Keyword(s):  
Hepatic Artery ◽  
Kinetic Modeling ◽  
Pet Imaging ◽  
Fdg Pet ◽  
Compartment Model ◽  
Input Function ◽  
Dynamic Pet ◽  
Malignant Lesions ◽  
18F Fdg ◽  
Healthy Liver

Abstract Background Dynamic PET with kinetic modeling was reported to be potentially helpful in the assessment of hepatic malignancy. In this study, a kinetic modeling analysis was performed on hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) from dynamic FDG positron emission tomography/computer tomography (PET/CT) scans. Methods A reversible two-tissue compartment model with dual blood input function, which takes into consideration the blood supply from both hepatic artery and portal vein, was used for accurate kinetic modeling of liver dynamic 18F-FDG PET imaging. The blood input functions were directly measured as the mean values over the VOIs on descending aorta and portal vein respectively. And the contribution of hepatic artery to the blood input function was optimization-derived in the process of model fitting. The kinetic model was evaluated using dynamic PET data acquired on 24 patients with identified hepatobiliary malignancy. 38 HCC or ICC identified lesions and 24 healthy liver regions were analyzed. Results Results showed significant differences in kinetic parameters $${K}_{1}-{k}_{4}$$ K 1 - k 4 , blood supplying fraction $${f}_{A}$$ f A , and metabolic rate constant $${K}_{i}$$ K i between malignant lesions and healthy liver tissue. And significant differences were also observed in $${K}_{1}$$ K 1 , $${k}_{3}$$ k 3 , $${f}_{A}$$ f A and $${K}_{i}$$ K i between HCC and ICC lesions. Further investigations of the effect of SUV measurements on the derived kinetic parameters were conducted. And results showed comparable effectiveness of the kinetic modeling using either SUVmean or SUVmax measurements. Conclusions Dynamic 18F-FDG PET imaging with optimization-derived hepatic artery blood supply fraction dual-blood input function kinetic modeling can effectively distinguish malignant lesions from healthy liver tissue, as well as HCC and ICC lesions.

Feasibility of Population-Based Input Function for Kinetic Analysis of [11C]-DPA-713

2020 ◽  
Author(s):  
Susan A. Gauthier ◽  
Claire Henchcliffe ◽  
Mercy I. Akerele ◽  
Sara A. Zein ◽  
Sneha Pandya ◽  
...  
Keyword(s):  
Kinetic Analysis ◽  
Input Function ◽  
Population Based
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