scholarly journals A Short Dynamic Scan Method of Measuring Bone Metabolic Flux Using [18F]NaF PET

Tomography ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. 623-635
Author(s):  
Tanuj Puri ◽  
Musib M. Siddique ◽  
Michelle L. Frost ◽  
Amelia E. B. Moore ◽  
Glen M. Blake
Keyword(s):  
Rate Constants ◽  
Statistical Power ◽  
Metabolic Flux ◽  
Arterial Input Function ◽  
Input Function ◽  
Ct Scans ◽  
Fixed Rate ◽  
Time Activity ◽  
Pet Ct ◽  
Dynamic Scan

[18F]NaF PET measurements of bone metabolic flux (Ki) are conventionally obtained with 60-min dynamic scans analysed using the Hawkins model. However, long scan times make this method expensive and uncomfortable for subjects. Therefore, we evaluated and compared measurements of Ki with shorter scan times analysed with fixed values of the Hawkins model rate constants. The scans were acquired in a trial in 30 postmenopausal women, half treated with teriparatide (TPT) and half untreated. Sixty-minute PET-CT scans of both hips were acquired at baseline and week 12 after injection with 180 MBq [18F]NaF. Scans were analysed using the Hawkins model by fitting bone time–activity curves at seven volumes of interest (VOIs) with a semi-population arterial input function. The model was re-run with fixed rate-constants for dynamic scan times from 0–12 min increasing in 4-min steps up to 0–60 min. Using the Hawkins model with fixed rate-constants, Ki measurements with statistical power equivalent or superior to conventionally analysed 60-min dynamic scans were obtained with scan times as short as 12 min.

scholarly journals Use of population input functions for reduced scan duration whole-body Patlak 18F-FDG PET imaging

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Joyce van Sluis ◽  
Maqsood Yaqub ◽  
Adrienne H. Brouwers ◽  
Rudi A. J. O. Dierckx ◽  
Walter Noordzij ◽  
...  
Keyword(s):  
Rate Constants ◽  
Arterial Input Function ◽  
Input Function ◽  
Dynamic Imaging ◽  
Whole Body ◽  
Time Interval ◽  
Time Activity ◽  
Dynamic Scan ◽  
Patlak Analysis ◽  
Body Dynamic

Abstract Whole-body Patlak images can be obtained from an acquisition of first 6 min of dynamic imaging over the heart to obtain the arterial input function (IF), followed by multiple whole-body sweeps up to 60 min pi. The use of a population-averaged IF (PIF) could exclude the first dynamic scan and minimize whole-body sweeps to 30–60 min pi. Here, the effects of (incorrect) PIFs on the accuracy of the proposed Patlak method were assessed. In addition, the extent of mitigating these biases through rescaling of the PIF to image-derived IF values at 30–60 min pi was evaluated. Methods Using a representative IF and rate constants from the literature, various tumour time-activity curves (TACs) were simulated. Variations included multiplication of the IF with a positive and negative gradual linear bias over 60 min of 5, 10, 15, 20, and 25% (generating TACs using an IF different from the PIF); use of rate constants (K1, k3, and both K1 and k2) multiplied by 2, 1.5, and 0.75; and addition of noise (μ = 0 and σ = 5, 10 and 15%). Subsequent Patlak analysis using the original IF (representing the PIF) was used to obtain the influx constant (Ki) for the differently simulated TACs. Next, the PIF was scaled towards the (simulated) IF value using the 30–60-min pi time interval, simulating scaling of the PIF to image-derived values. Influence of variabilities in IF and rate constants, and rescaling the PIF on bias in Ki was evaluated. Results Percentage bias in Ki observed using simulated modified IFs varied from − 16 to 16% depending on the simulated amplitude and direction of the IF modifications. Subsequent scaling of the PIF reduced these Ki biases in most cases (287 out of 290) to < 5%. Conclusions Simulations suggest that scaling of a (possibly incorrect) PIF to IF values seen in whole-body dynamic imaging from 30 to 60 min pi can provide accurate Ki estimates. Consequently, dynamic Patlak imaging protocols may be performed for 30–60 min pi making whole-body Patlak imaging clinically feasible.

scholarly journals Arterial Input Function Derived from Pairwise Correlations Between PET-image Voxels

2013 ◽  
Vol 33 (7) ◽  
pp. 1058-1065 ◽  
Author(s):  
Martin Schain ◽  
Simon Benjaminsson ◽  
Katarina Varnäs ◽  
Anton Forsberg ◽  
Christer Halldin ◽  
...  
Keyword(s):  
Arterial Input Function ◽  
Pearson Correlation ◽  
Invasive Procedure ◽  
Compartmental Analysis ◽  
Input Function ◽  
Time Activity ◽  
Pairwise Correlation ◽  
Positron Emission ◽  
Using Data ◽  
Suitable Reference

A metabolite corrected arterial input function is a prerequisite for quantification of positron emission tomography (PET) data by compartmental analysis. This quantitative approach is also necessary for radioligands without suitable reference regions in brain. The measurement is laborious and requires cannulation of a peripheral artery, a procedure that can be associated with patient discomfort and potential adverse events. A non invasive procedure for obtaining the arterial input function is thus preferable. In this study, we present a novel method to obtain image-derived input functions (IDIFs). The method is based on calculation of the Pearson correlation coefficient between the time-activity curves of voxel pairs in the PET image to localize voxels displaying blood-like behavior. The method was evaluated using data obtained in human studies with the radioligands [ 11 C]flumazenil and [ 11 C]AZ10419369, and its performance was compared with three previously published methods. The distribution volumes ( VT) obtained using IDIFs were compared with those obtained using traditional arterial measurements. Overall, the agreement in VT was good (~3% difference) for input functions obtained using the pairwise correlation approach. This approach performed similarly or even better than the other methods, and could be considered in applied clinical studies. Applications to other radioligands are needed for further verification.

Abstract 535: Mathematical Modeling of Hyperpolarized Pyruvate Metabolism in Human Heart

2020 ◽  
Vol 127 (Suppl_1) ◽  
Author(s):  
Katarina Yaros ◽  
Jae Mo Park ◽  
Craig Malloy ◽  
Jeffry Alger ◽  
Tarique Hussain ◽  
...  
Keyword(s):  
Kinetic Model ◽  
Real Time ◽  
Rate Constants ◽  
Human Heart ◽  
Metabolic Flux ◽  
Human Subjects ◽  
Input Function ◽  
Order Kinetic ◽  
Imaging Data ◽  
Pyruvate Metabolism

Background: Dissolution dynamic nuclear polarization is a novel method that increases more than 10,000-fold the 13 C signal-to-noise ratio and emerges as a useful tool for detection of molecules much less prevalent than water in the human body. Hyperpolarized [1- 13 C] pyruvate (HP) permits the noninvasive, nonradioactive study of metabolic flux via pyruvate dehydrogenase, a key enzyme in the complex cardiac adaptation to physical activity, energy sources, and in various disease states. The aim of this study is to define an analysis pipeline of multiparametric, non-steady-state, real-time human cardiac magnetic resonance spectroscopic investigations. Methods: HP was infused intravenously into five human subjects. Dynamic spectroscopic data was acquired for [1- 13 C]- pyruvate, 13 C-alanine, [1- 13 C]-lactate, and 13 C-bicarbonate at two consecutive times for each subject. Imaging data were exported into MATLAB. We employed a first-order kinetic model to fit the signal for the hyperpolarized metabolites assuming bidirectional flow between pyruvate and lactate as well as pyruvate and alanine. A gamma variate input function was used to model the initial pyruvate bolus. We also calculated non-parametric values to describe the signal for each metabolite, specifically area-under-curve (AUC) and time-to-peak (TTP). The repeatability analysis was performed using an ANOVA-based method. Results: Rate constants derived from the kinetic model: kPL 0.016 ± 0.008s -1 , kPA 0.012 ± 0.006s -1 , kPB 0.024±0.0095s -1 are of similar order of magnitude to prior studies. The reverse constant for lactate to pyruvate conversion (kLP) is relatively unchanged across the subjects (0.0099±0.0005), and reverse constant for alanine to pyruvate (kAP) conversion is almost negligible (0.0008 ± 0.0012). The model well describes the metabolites signal over time. Within subject repeatability was higher for TTP (lactate: 0.981 ± 0.018) ) than for rate constants (kPL 0.823 ± 0.153) and AUC (lactate 0.697 ± 0.244) with kPB having the lowest repeatability (0.325 ± 0.424) due to a single outlier. Conclusions: We present a real-time pipeline for analysis and modelling of pyruvate metabolism in the human heart and confirm that it is reproducible within the same subject under same conditions.

scholarly journals Estimation of Renal Perfusion Based on Measurement of Rubidium-82 Clearance by PET/CT Scanning in Healthy Subjects

2020 ◽  
Author(s):  
Stine Sundgaard Langaa ◽  
Thomas Guldager Lauridsen ◽  
Frank Holden Mose ◽  
Claire Anne Fynbo ◽  
Jørn Theil ◽  
...  
Keyword(s):  
Abdominal Aorta ◽  
Healthy Subjects ◽  
Input Function ◽  
Renal Perfusion ◽  
Left Ventricular ◽  
Kidney Volume ◽  
Time Activity ◽  
Coefficients Of Variation ◽  
Pet Ct ◽  
Rubidium 82

Abstract Background: Changes in renal blood flow (RBF) may play a pathophysiological role in hypertension and kidney disease. However, RBF determination in humans has proven difficult. We aimed to confirm the feasibility of RBF estimation based on positron emission tomography/ computed tomography (PET/CT) and rubidium-82 (82Rb) using the abdominal aorta as input function in a 1-tissue compartment model. Methods: Eighteen healthy subjects underwent two dynamic 82Rb PET/CT scans in two different fields of view (FOV). FOV-A included the left ventricular blood pool (LVBP), the abdominal aorta (AA) and the majority of the kidneys. FOV-B included AA and the kidneys in their entirety. In FOV-A, an input function was derived from LVBP and from AA; in FOV-B from AA. 1-tissue compartmental modeling was performed using tissue time activity curves generated from volumes of interest contouring the kidneys, where the renal clearance of 82Rb is represented by the K1 kinetic parameter. To investigate the correct interpretation of K1, we assumed to first estimate effective renal plasma flow (ERPF) by extrapolating clearance values (ml/min/cm3) to whole kidney values (ml/min) using the estimated total kidney volume. Thereafter, RPF was estimated from ERPF using an assumed extraction fraction (0.89). Lastly, RBF was estimated from RPF using measured haematocrit values. Intra-assay coefficients of variation and inter-observer variation were calculated.Results: For both kidneys, K1 values derived from AA did not differ significantly from values obtained from LVBP, neither were significant differences seen between AA in FOV-A and AA in FOV-B, nor between the right and left kidneys. For both kidneys, the intra-assay coefficients of variation were low (~ 5%) for both input functions. The measured K1 of 2.04 ml/min/cm3 suggests an estimated total renal perfusion normalized to body surface area of 628 ± 95 ml/min/1.73 m2 and subsequently an estimated RBF of 1091 ± 162 ml/min/1.73 m2.

Dynamic 68Ga-DOTATOC PET/CT and static image in NET patients

2016 ◽  
Vol 55 (03) ◽  
pp. 104-114 ◽  
Author(s):  
Sofie Van Binnebeek ◽  
Michel Koole ◽  
Christelle Terwinghe ◽  
Kristof Baete ◽  
Bert Vanbilloen ◽  
...  
Keyword(s):  
Neuroendocrine Tumours ◽  
Somatostatin Receptor ◽  
Input Function ◽  
Receptor Expression ◽  
The Other ◽  
Static Image ◽  
Pet Ct ◽  
Dynamic Scan ◽  
The Relationship ◽  
Good Linear Correlation

SummaryPurpose: To investigate the relationship between the dynamic parameters (Ki) and static image-derived parameters of 68Ga-DOTATOC-PET, to determine which static parameter best reflects underlying somatostatin-receptor-expression (SSR) levels on neuroendocrine tumours (NETs). Patients, methods: 20 patients with metastasized NETs underwent a dynamic and static 68Ga-DOTATOC-PET before PRRT and at 7 and 40 weeks after the first administration of 90Y-DOTATOC (in total 4 cycles were planned); 175 lesions were defined and analyzed on the dynamic as well as static scans. Quantitative analysis was performed using the software PMOD. One to five target lesions per patient were chosen and delineated manually on the baseline dynamic scan and further, on the corresponding static 68Ga-DOTATOC-PET and the dynamic and static 68Ga-DOTATOC-PET at the other time-points; SUVmax and SUVmean of the lesions was assessed on the other six scans. The input function was retrieved from the abdominal aorta on the images. Further on, Ki was calculated using the Patlak-Plot. At last, 5 reference regions for normalization of SUVtumour were delineated on the static scans resulting in 5 ratios (SUVratio). Results: SUVmax and SUVmean of the tumoural lesions on the dynamic 68Ga-DO-TATOC-PET had a very strong correlation with the corresponding parameters in the static scan (R²: 0.94 and 0.95 respectively). SUVmax, SUVmean and Ki of the lesions showed a good linear correlation; the SUVratios correlated poorly with Ki. A significantly better correlation was noticed between Ki and SUVtumour(max and mean) (p < 0.0001). Conclusions: As the dynamic para meter Ki correlates best with the absolute SUVtumour, SUVtumour best reflects underlying SSR-levels in NETs.

scholarly journals Semi-quantitative cerebral blood flow parameters derived from non-invasive [15O]H2O PET studies

2017 ◽  
Vol 39 (1) ◽  
pp. 163-172 ◽  
Author(s):  
Thomas Koopman ◽  
Maqsood Yaqub ◽  
Dennis FR Heijtel ◽  
Aart J Nederveen ◽  
Bart NM van Berckel ◽  
...  
Keyword(s):  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Arterial Input Function ◽  
Input Function ◽  
Time Activity ◽  
Non Invasive ◽  
Flow Parameters ◽  
Positron Emission ◽  
Arterial Sampling ◽  
Activity Curve

Quantification of regional cerebral blood flow (CBF) using [15O]H2O positron emission tomography (PET) requires the use of an arterial input function. Arterial sampling, however, is not always possible, for example in ill-conditioned or paediatric patients. Therefore, it is of interest to explore the use of non-invasive methods for the quantification of CBF. For validation of non-invasive methods, test–retest normal and hypercapnia data from 15 healthy volunteers were used. For each subject, the data consisted of up to five dynamic [15O]H2O brain PET studies of 10 min and including arterial sampling. A measure of CBF was estimated using several non-invasive methods earlier reported in literature. In addition, various parameters were derived from the time-activity curve (TAC). Performance of these methods was assessed by comparison with full kinetic analysis using correlation and agreement analysis. The analysis was repeated with normalization to the whole brain grey matter value, providing relative CBF distributions. A reliable, absolute quantitative estimate of CBF could not be obtained with the reported non-invasive methods. Relative (normalized) CBF was best estimated using the double integration method.

scholarly journals Influence of O-Methylated Metabolite Penetrating the Blood–Brain Barrier to Estimation of Dopamine Synthesis Capacity in Human L-[β-11C]DOPA PET

2013 ◽  
Vol 34 (2) ◽  
pp. 268-274 ◽  
Author(s):  
Keisuke Matsubara ◽  
Yoko Ikoma ◽  
Maki Okada ◽  
Masanobu Ibaraki ◽  
Tetsuya Suhara ◽  
...  
Keyword(s):  
Blood Brain Barrier ◽  
Arterial Input Function ◽  
Input Function ◽  
Brain Barrier ◽  
Dopamine Synthesis ◽  
Synthesis Rate ◽  
Time Activity ◽  
Blood Brain ◽  
The Impact ◽  
Patlak Analysis

O-methyl metabolite (L-[ β-11C]OMD) of 11C-labeled L-3,4-dihydroxyphenylalanine (L-[ β-11C]DOPA) can penetrate into brain tissue through the blood–brain barrier, and can complicate the estimation of dopamine synthesis capacity by positron emission tomography (PET) study with L-[ β-11C]DOPA. We evaluated the impact of L-[ β-11C]OMD on the estimation of the dopamine synthesis capacity in a human L-[ β-11C]DOPA PET study. The metabolite correction with mathematical modeling of L-[ β-11C]OMD kinetics in a reference region without decarboxylation and further metabolism, proposed by a previous [18F]FDOPA PET study, were implemented to estimate radioactivity of tissue L-[ β-11C]OMD in 10 normal volunteers. The component of L-[ β-11C]OMD in tissue time-activity curves (TACs) in 10 regions were subtracted by the estimated radioactivity of L-[ β-11C]OMD. To evaluate the influence of omitting blood sampling and metabolite correction, relative dopamine synthesis rate ( kref) was estimated by Gjedde–Patlak analysis with reference tissue input function, as well as the net dopamine synthesis rate ( Ki) by Gjedde–Patlak analysis with the arterial input function and TAC without and with metabolite correction. Overestimation of Ki was observed without metabolite correction. However, the kref and Ki with metabolite correction were significantly correlated. These data suggest that the influence of L-[ β-11C]OMD is minimal for the estimation of kref as dopamine synthesis capacity.

scholarly journals Comparison of [18F] NaF PET/CT dynamic analysis methods and a static analysis method including derivation of a semi-population input function for site-specific measurements of bone formation in a population with chronic kidney disease-mineral and bone disorder

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
M. H. Vrist ◽  
J. N. Bech ◽  
T. G. Lauridsen ◽  
C. A. Fynbo ◽  
J. Theil
Keyword(s):  
Regression Analysis ◽  
Nonlinear Regression ◽  
Plasma Clearance ◽  
Input Function ◽  
Nonlinear Regression Analysis ◽  
Mineral And Bone Disorder ◽  
Pet Ct ◽  
Dynamic Scan ◽  
Patlak Analysis ◽  
Population Function

Abstract Purpose The purpose of this study is to compare dynamic and static whole-body (WB) [18F]NaF PET/CT scan methods used for analysis of bone plasma clearance in patients with chronic kidney disease-mineral and bone disorder (CKD-MBD). Methods Seventeen patients with CKD-MBD underwent a 60-min dynamic scan followed by a 30-min static WB scan. Tracer kinetics in four thoracic vertebrae were analysed using nonlinear regression and Patlak analysis using image-derived arterial input functions. The static WB scan was analysed using a simplified Patlak method requiring only a single data point in combination with a fixed y-intercept value (V0), both obtained using a semi-population function. The semi-population function was constructed by combining a previously derived population input function in combination with data from venous blood samples. Static WB scan analysis data, obtained from the semi-population input functions, was compared with paired data obtained using dynamic input functions. Results Bone plasma clearance (Ki) from Patlak analyses correlated well with nonlinear regression analysis, but Ki results using Patlak analysis were lower than Ki results using nonlinear regression analysis. However, no significant difference was found between Ki obtained by static WB scans and Ki obtained by dynamic scans using nonlinear regression analysis (p = 0.29). Conclusion Bone plasma clearance measured from static WB scans correlates with clearance data measured by dynamic analysis. Static [18F]NaF PET/CT scans can be applied in future studies to measure Ki in patients with CKD-MBD, but the results should not be compared uncritically with results obtained by dynamic scan analysis.

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