A study of non-invasive Patlak quantification for whole-body dynamic FDG-PET studies of mice

Zheng, Xiujuan; Wen, Lingfeng; Yu, Amy S.; Huang, Sung‐Cheng; Feng, David Dagan

doi:10.1016/j.bspc.2011.11.005

Cited by 8 publications

(8 citation statements)

References 26 publications

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“…An iterative method was used to adjust the estimated parameters to achieve the minimum least squares difference between the measured TACs and estimated TACs. The Akaike Information Criteria (AIC) was used to assess the fitting 18 , 25 . In addition, the combined parameter K 1 (= K 1 ×k 3 /(k 2 +k 3 )), was also calculated to estimate the local hepatic metabolic rate of 11 C-acetate (LHMRAct) 11 , 18 .…”

Section: Methodsmentioning

confidence: 99%

Kinetic Analysis of Dynamic ¹¹C-Acetate PET/CT Imaging as a Potential Method for Differentiation of Hepatocellular Carcinoma and Benign Liver Lesions

Huo¹,

Guo²,

Dang³

et al. 2015

Theranostics

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Objective: The kinetic analysis of 11C-acetate PET provides more information than routine one time-point static imaging. This study aims to investigate the potential of dynamic 11C-acetate hepatic PET imaging to improve the diagnosis of hepatocellular carcinoma (HCC) and benign liver lesions by using compartmental kinetic modeling and discriminant analysis.Methods: Twenty-two patients were enrolled in this study, 6 cases were with well-differentiated HCCs, 7 with poorly-differentiated HCCs and 9 with benign pathologies. Following the CT scan, all patients underwent 11C-acetate dynamic PET imaging. A three-compartment irreversible dual-input model was applied to the lesion time activity curves (TACs) to estimate the kinetic rate constants K1-k3, vascular fraction (VB) and the coefficient α representing the relative hepatic artery (HA) contribution to the hepatic blood supply on lesions and non-lesion liver tissue. The parameter Ki (=K1×k3/(k2 + k3)) was calculated to evaluate the local hepatic metabolic rate of acetate (LHMAct). The lesions were further classified by discriminant analysis with all the above parameters.Results: K1 and lesion to non-lesion standardized uptake value (SUV) ratio (T/L) were found to be the parameters best characterizing the differences among well-differentiated HCC, poorly-differentiated HCC and benign lesions in stepwise discriminant analysis. With discriminant functions consisting of these two parameters, the accuracy of lesion prediction was 87.5% for well-differentiated HCC, 50% for poorly-differentiated HCC and 66.7% for benign lesions. The classification was much better than that with SUV and T/L, where the corresponding classification accuracy of the three kinds of lesions was 57.1%, 33.3% and 44.4%.Conclusion: 11C-acetate kinetic parameter K1 could improve the identification of HCC from benign lesions in combination with T/L in discriminant analysis. The discriminant analysis using static and kinetic parameters appears to be a very helpful method for clinical liver masses diagnosis and staging.

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Section: Methodsmentioning

confidence: 99%

Kinetic Analysis of Dynamic ¹¹C-Acetate PET/CT Imaging as a Potential Method for Differentiation of Hepatocellular Carcinoma and Benign Liver Lesions

Huo¹,

Guo²,

Dang³

et al. 2015

Theranostics

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“…Meanwhile, for a reference region with irreversible uptake, the invasive GP equation is used to describe C P (t) using C R (t). Then, another linear model of a noninvasive GP plot can be obtained as follows [37,38] ,…”

Section: Noninvasive Gp Plot Methodsmentioning

confidence: 99%

“…Although the arterial blood sampling method is considered to be the gold standard of measuring C P (t) based on its accuracy [8] , it has several disadvantages such as invasiveness and technical demands [8,31,37] . Therefore, to minimize or eliminate the need for invasive and technicallydemanding blood sampling and metabolite correction, the following approaches have been proposed and applied [37,38] :…”

Section: Compartmental Modelsmentioning

confidence: 99%

Recent advances in parametric neuroreceptor mapping with dynamic PET: basic concepts and graphical analyses

Seo¹,

Kim

Lee³

et al. 2014

Neurosci. Bull.

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Tracer kinetic modeling in dynamic positron emission tomography (PET) has been widely used to investigate the characteristic distribution patterns or dysfunctions of neuroreceptors in brain diseases. Its practical goal has progressed from regional data quantification to parametric mapping that produces images of kinetic-model parameters by fully exploiting the spatiotemporal information in dynamic PET data. Graphical analysis (GA) is a major parametric mapping technique that is independent on any compartmental model configuration, robust to noise, and computationally efficient. In this paper, we provide an overview of recent advances in the parametric mapping of neuroreceptor binding based on GA methods. The associated basic concepts in tracer kinetic modeling are presented, including commonly-used compartment models and major parameters of interest. Technical details of GA approaches for reversible and irreversible radioligands are described, considering both plasma input and reference tissue input models. Their statistical properties are discussed in view of parametric imaging.

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“…The Patlak method was performed to calculate the influx rate constant for 18 F-FDG, a well-known irreversible tracer. The influx rate is related to the metabolic rate of glucose (MRglu) (26). …”

Section: Methodsmentioning

confidence: 99%

¹⁸F-Alfatide II and ¹⁸F-FDG Dual-Tracer Dynamic PET for Parametric, Early Prediction of Tumor Response to Therapy

Guo

Lang

et al. 2013

J Nucl Med

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A single dynamic PET acquisition using multiple tracers administered closely in time could provide valuable complementary information about a tumor’s status under quasi-constant conditions. This study aims to investigate the utility of dual-tracer dynamic PET imaging with 18F-Alfatide II (18F-AlF-NOTA-E[PEG4-c(RGDfk)]2) and 18F-FDG for parametric monitoring of tumor responses to therapy. Methods We administered doxorubicin to one group of athymic nude mice with U87MG tumors and Abraxane to another group of mice with MDA-MB-435 tumors. To monitor therapeutic responses, we performed dual-tracer dynamic imaging, in sessions that lasted 90 min, starting by injecting the mice via tail vein catheters with 18F-Alfatide II, followed 40 minutes later by 18F-FDG. To achieve signal separation of the two tracers, we fit a three-compartment reversible model to the time activity curve (TAC) of 18F-Alfatide II for the 40 min prior to 18F-FDG injection, and then extrapolated to 90 min. The 18F-FDG tumor TAC was isolated from the 90 min dual tracer tumor TAC by subtracting the fitted 18F-Alfatide II tumor TAC. With separated tumor TACs, the 18F-Alfatide II binding potential (Bp=k3/k4) and volume of distribution (VD), and 18F-FDG influx rate ((K1×k3)/(k2 + k3)) based on the Patlak method were calculated to validate the signal recovery in a comparison with 60-min single tracer imaging and to monitor therapeutic response. Results The transport and binding rate parameters K1-k3 of 18F-Alfatide II, calculated from the first 40 min of dual tracer dynamic scan, as well as Bp and VD, correlated well with the parameters from the 60 min single tracer scan (R2 > 0.95). Compared with the results of single tracer PET imaging, FDG tumor uptake and influx were recovered well from dual tracer imaging. Upon doxorubicin treatment, while no significant changes in static tracer uptake values of 18F-Alfatide II or 18F-FDG were observed, both 18F-Alfatide II Bp and 18F-FDG influx from kinetic analysis in tumors showed significant decreases. For Abraxane therapy of MDA-MB-435 tumors, significant decrease was only observed with 18F-Alfatide II Bp value from kinetic analysis but not 18F-FDG influx. Conclusion The parameters fitted with compartmental modeling from the dual tracer dynamic imaging are consistent with those from single tracer imaging, substantiating the feasibility of this methodology. Even though no significant differences in tumor size were found until 5 days after doxorubicin treatment started, at day 3 there were already substantial differences in 18F-Alfatide II Bp and 18F-FDG influx rate. Dual tracer imaging can measure 18F-Alfatide II Bp value and 18F-FDG influx simultaneously to evaluate tumor angiogenesis and metabolism. Such changes are known to precede anatomical changes, and thus parametric imaging may offer the promise of early prediction of therapy response.

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A study of non-invasive Patlak quantification for whole-body dynamic FDG-PET studies of mice

Cited by 8 publications

References 26 publications

Kinetic Analysis of Dynamic ¹¹C-Acetate PET/CT Imaging as a Potential Method for Differentiation of Hepatocellular Carcinoma and Benign Liver Lesions

Kinetic Analysis of Dynamic ¹¹C-Acetate PET/CT Imaging as a Potential Method for Differentiation of Hepatocellular Carcinoma and Benign Liver Lesions

Recent advances in parametric neuroreceptor mapping with dynamic PET: basic concepts and graphical analyses

¹⁸F-Alfatide II and ¹⁸F-FDG Dual-Tracer Dynamic PET for Parametric, Early Prediction of Tumor Response to Therapy

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A study of non-invasive Patlak quantification for whole-body dynamic FDG-PET studies of mice

Cited by 8 publications

References 26 publications

Kinetic Analysis of Dynamic 11C-Acetate PET/CT Imaging as a Potential Method for Differentiation of Hepatocellular Carcinoma and Benign Liver Lesions

Kinetic Analysis of Dynamic 11C-Acetate PET/CT Imaging as a Potential Method for Differentiation of Hepatocellular Carcinoma and Benign Liver Lesions

Recent advances in parametric neuroreceptor mapping with dynamic PET: basic concepts and graphical analyses

18F-Alfatide II and 18F-FDG Dual-Tracer Dynamic PET for Parametric, Early Prediction of Tumor Response to Therapy

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Kinetic Analysis of Dynamic ¹¹C-Acetate PET/CT Imaging as a Potential Method for Differentiation of Hepatocellular Carcinoma and Benign Liver Lesions

Kinetic Analysis of Dynamic ¹¹C-Acetate PET/CT Imaging as a Potential Method for Differentiation of Hepatocellular Carcinoma and Benign Liver Lesions

¹⁸F-Alfatide II and ¹⁸F-FDG Dual-Tracer Dynamic PET for Parametric, Early Prediction of Tumor Response to Therapy