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 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 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.

Sleep Duration, Sleep Apnea, and Gray Matter Volume

2021 ◽  
pp. 089198872098891
Author(s):  
Regina Eun Young Kim ◽  
Robert Douglas Abbott ◽  
Soriul Kim ◽  
Robert Joseph Thomas ◽  
Chang-Ho Yun ◽  
...  
Keyword(s):  
Sleep Apnea ◽  
Sleep Duration ◽  
Gray Matter ◽  
Gray Matter Volume ◽  
Brain Structures ◽  
Brain Regions ◽  
Population Based ◽  
Older Individuals ◽  
Bootstrap Sampling ◽  
Matter Volume

This study aimed to evaluate the effect of sleep duration on brain structures in the presence versus absence of sleep apnea in middle-aged and older individuals. The study investigated a population-based sample of 2,560 individuals, aged 49-80 years. The presence of sleep apnea and self-reported sleep duration were examined in relation to gray matter volume (GMV) in total and lobar brain regions. We identified ranges of sleep duration associated with maximal GMV using quadratic regression and bootstrap sampling. A significant quadratic association between sleep duration and GMV was observed in total and lobar brain regions of men with sleep apnea. In the fully adjusted model, optimal sleep durations associated with peak GMV between brain regions ranged from 6.7 to 7.0 hours. Shorter and longer sleep durations were associated with lower GMV in total and 4 sub-regions of the brain in men with sleep apnea.

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 Quantifying lumbar vertebral perfusion by a Tofts model on DCE-MRI using segmental versus aortic arterial input function

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yi-Jui Liu ◽  
Hou-Ting Yang ◽  
Melissa Min-Szu Yao ◽  
Shao-Chieh Lin ◽  
Der-Yang Cho ◽  
...  
Keyword(s):  
Arterial Input Function ◽  
Lumbar Vertebrae ◽  
Region Of Interest ◽  
Input Function ◽  
Population Based ◽  
Patient Specific ◽  
Rank Test ◽  
Pharmacokinetic Parameters ◽  
Dce Mri ◽  
Signed Rank Test

AbstractThe purpose of this study was to investigate the influence of arterial input function (AIF) selection on the quantification of vertebral perfusion using axial dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In this study, axial DCE-MRI was performed on 2 vertebrae in each of eight healthy volunteers (mean age, 36.9 years; 5 men) using a 1.5-T scanner. The pharmacokinetic parameters Ktrans, ve, and vp, derived using a Tofts model on axial DCE-MRI of the lumbar vertebrae, were evaluated using various AIFs: the population-based aortic AIF (AIF_PA), a patient-specific aortic AIF (AIF_A) and a patient-specific segmental arterial AIF (AIF_SA). Additionally, peaks and delay times were changed to simulate the effects of various AIFs on the calculation of perfusion parameters. Nonparametric analyses including the Wilcoxon signed rank test and the Kruskal–Wallis test with a Dunn–Bonferroni post hoc analysis were performed. In simulation, Ktrans and ve increased as the peak in the AIF decreased, but vp increased when delay time in the AIF increased. In humans, the estimated Ktrans and ve were significantly smaller using AIF_A compared to AIF_SA no matter the computation style (pixel-wise or region-of-interest based). Both these perfusion parameters were significantly greater using AIF_SA compared to AIF_A.

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

scholarly journals Absolute Quantification of Cerebral Blood Flow with Magnetic Resonance, Reproducibility of the Method, and Comparison with H215O Positron Emission Tomography

2002 ◽  
Vol 22 (9) ◽  
pp. 1149-1156 ◽  
Author(s):  
Timothy J. Carroll ◽  
Vincenzo Teneggi ◽  
Mathieu Jobin ◽  
Lisa Squassante ◽  
Valerie Treyer ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
White Matter ◽  
Gray Matter ◽  
Cerebral Perfusion ◽  
Emission Tomography ◽  
Mr Perfusion ◽  
Altman Analysis ◽  
Bland Altman Analysis ◽  
Positron Emission ◽  
The Difference

While H215O positron emission tomography (PET) is still the gold standard in the quantitative assessment of cerebral perfusion (rCBF), its technical challenge, limited availability, and radiation exposure are disadvantages of the method. Recent work demonstrated the feasibility of magnetic resonance (MR) for quantitative cerebral perfusion imaging. There remain open questions, however, especially regarding reproducibility. The main purpose of this study was to assess the accuracy and reproducibility of MR-derived flow values to those derived from H215O PET. Positron emission tomography and MR perfusion imaging was performed in 20 healthy male volunteers, who were chronic smokers, on day 1 and day 3 of a 4-day hospitalization. Subjects were randomly assigned to one of two groups, each with 10 subjects. One group was allowed to smoke as usual during the hospitalization, while the other group stopped smoking from day 2. Positron emission tomography and MR images were coregistered and rCBF was determined in two regions of interest, defined over gray matter (gm) and white matter (wm), yielding rCBFPETgm, rCBFMRgm, rCBFPETwm, and rCBFMRwm. Bland-Altman analysis was used to investigate reproducibility by assessing the difference rCBFday3 - rCBFday1 in eight continual-smoker volunteers. The analysis showed a good reproducibility for PET, but not for MR. Mean ± SD of the difference rCBFday3 - rCBFday1 in gray matter was 6.35 ± 21.06 and 0.49 ± 5.27 mL · min−1 · 100 g−1 for MR and PET, respectively; the corresponding values in white matter were 2.60 ± 15.64 and −1.14 ± 4.16 mL · min−1 · 100 g−1. The Bland-Altman analysis was also used to assess MRI and PET agreement comparing rCBF measured on day 1. The analysis demonstrated a reasonably good agreement of MR and PET in white matter (rCBFPETwm - rCBFMRwm; −0.09 ± 7.23 mL · min−1 · 100 g−1), while in gray matter a reasonable agreement was only achieved after removing vascular artifacts in the MR perfusion maps (rCBFPETgm - rCBFMRgm; −11.73 ± 14.52 mL · min−1 · 100 g−1). In line with prior work, these results demonstrate that reproducibility was overall considerably better for PET than for MR. Until reproducibility is improved and vascular artifacts are efficiently removed, MR is not suitable for reliable quantitative perfusion measurements.

Reply: Bland-Altman analysis for pupillometer comparison

2010 ◽  
Vol 36 (10) ◽  
pp. 1803-1804
Author(s):  
Magdalena Scheffel ◽  
Christoph Kuehne ◽  
Thomas Kohnen
Keyword(s):  
Altman Analysis ◽  
Bland Altman Analysis

scholarly journals Gray-Matter Expansion of Social Brain Networks in Individuals High in Public Self-Consciousness

2021 ◽  
Vol 11 (3) ◽  
pp. 374
Author(s):  
Tomoyo Morita ◽  
Minoru Asada ◽  
Eiichi Naito
Keyword(s):  
Gray Matter ◽  
Structural Changes ◽  
Gray Matter Volume ◽  
Brain Networks ◽  
Japanese Version ◽  
Brain Regions ◽  
The Self ◽  
Social Brain ◽  
Default Mode ◽  
Public Self Consciousness

Self-consciousness is a personality trait associated with an individual’s concern regarding observable (public) and unobservable (private) aspects of self. Prompted by previous functional magnetic resonance imaging (MRI) studies, we examined possible gray-matter expansions in emotion-related and default mode networks in individuals with higher public or private self-consciousness. One hundred healthy young adults answered the Japanese version of the Self-Consciousness Scale (SCS) questionnaire and underwent structural MRI. A voxel-based morphometry analysis revealed that individuals scoring higher on the public SCS showed expansions of gray matter in the emotion-related regions of the cingulate and insular cortices and in the default mode network of the precuneus and medial prefrontal cortex. In addition, these gray-matter expansions were particularly related to the trait of “concern about being evaluated by others”, which was one of the subfactors constituting public self-consciousness. Conversely, no relationship was observed between gray-matter volume in any brain regions and the private SCS scores. This is the first study showing that the personal trait of concern regarding public aspects of the self may cause long-term substantial structural changes in social brain networks.

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