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

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.

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

10.1101/268748 ◽

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.

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

10.21203/rs.3.rs-152770/v1 ◽

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.

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

10.21203/rs.3.rs-110221/v1 ◽

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.

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Population-Based Input Function Modeling for [18F]FMPEP-d2, an Inverse Agonist Radioligand for Cannabinoid CB1 Receptors: Validation in Clinical Studies

PLoS ONE ◽

10.1371/journal.pone.0060231 ◽

2013 ◽

Vol 8 (4) ◽

pp. e60231 ◽

Cited By ~ 18

Author(s):

Paolo Zanotti-Fregonara ◽

Jussi Hirvonen ◽

Chul Hyoung Lyoo ◽

Sami S. Zoghbi ◽

Denise Rallis-Frutos ◽

...

Keyword(s):

Clinical Studies ◽

Input Function ◽

Population Based ◽

Inverse Agonist ◽

Cb1 Receptors

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

Journal of Cerebral Blood Flow & Metabolism ◽

10.1097/00004647-200006000-00002 ◽

2000 ◽

Vol 20 (6) ◽

pp. 899-909 ◽

Cited By ~ 64

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.

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Feasibility study of TSPO quantification with [18F]FEPPA using population-based input function

PLoS ONE ◽

10.1371/journal.pone.0177785 ◽

2017 ◽

Vol 12 (5) ◽

pp. e0177785 ◽

Cited By ~ 8

Author(s):

Rostom Mabrouk ◽

Antonio P. Strafella ◽

Dunja Knezevic ◽

Christine Ghadery ◽

Romina Mizrahi ◽

...

Keyword(s):

Feasibility Study ◽

Input Function ◽

Population Based

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Kinetic Analysis of [11C]MP4A Using a High-Radioactivity Brain Region That Represents an Integrated Input Function for Measurement of Cerebral Acetylcholinesterase Activity without Arterial Blood Sampling

Journal of Cerebral Blood Flow & Metabolism ◽

10.1097/00004647-200111000-00011 ◽

2001 ◽

Vol 21 (11) ◽

pp. 1354-1366 ◽

Cited By ~ 31

Author(s):

Shin-Ichiro Nagatsuka ◽

Kiyoshi Fukushi ◽

Hitoshi Shinotoh ◽

Hiroki Namba ◽

Masaomi Iyo ◽

...

Keyword(s):

Kinetic Analysis ◽

Least Squares ◽

Ache Activity ◽

Input Function ◽

Blood Sampling ◽

Linear Least Squares ◽

Reference Tissue ◽

Arterial Blood Sampling ◽

Arterial Blood ◽

The Brain

N-[11C]methylpiperidin-4-yl acetate ([11C]MP4A) is an acetylcholine analog. It has been used successfully for the quantitative measurement of acetylcholinesterase (AChE) activity in the human brain with positron emission tomography (PET). [11C]MP4A is specifically hydrolyzed by AChE in the brain to a hydrophilic metabolite, which is irreversibly trapped locally in the brain. The authors propose a new method of kinetic analysis of brain AChE activity by PET without arterial blood sampling, that is, reference tissue-based linear least squares (RLS) analysis. In this method, cerebellum or striatum is used as a reference tissue. These regions, because of their high AChE activity, act as a biologic integrator of plasma input function during PET scanning, when regional metabolic rates of [11C]MP4A through AChE (k3; an AChE index) are calculated by using Blomqvist's linear least squares analysis. Computer simulation studies showed that RLS analysis yielded k3 with almost the same accuracy as the standard nonlinear least squares (NLS) analysis in brain regions with low (such as neocortex and hippocampus) and moderately high (thalamus) k3 values. The authors then applied these methods to [11C]MP4A PET data in 12 healthy subjects and 26 patients with Alzheimer disease (AD) using the cerebellum as the reference region. There was a highly significant linear correlation in regional k3 estimates between RLS and NLS analyses (456 cerebral regions, [RLS k3] = 0.98 × [NLS k3], r = 0.92, P < 0.001). Significant reductions were observed in k3 estimates of frontal, temporal, parietal, occipital, and sensorimotor cerebral neocortices ( P < 0.001, single-tailed t-test), and hippocampus ( P = 0.012) in patients with AD as compared with controls when using RLS analysis. Mean reductions (19.6%) Fin these 6 regions by RLS were almost the same as those by NLS analysis (20.5%). The sensitivity of RLS analysis for detecting cortical regions with abnormally low k3 in the 26 patients with AD (138 of 312 regions, 44%) was somewhat less than NLS analysis (52%), but was greater than shape analysis (33%), another method of [11C]MP4A kinetic analysis without blood sampling. The authors conclude that RLS analysis is practical and useful for routine analysis of clinical [11C]MP4A studies.

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