A Curve-Fitting Approach to Estimate the Arterial Plasma Input Function for the Assessment of Glucose Metabolic Rate and Response to Treatment

Vriens, Dennis; Oyen, Wim J.G.; Visser, Eric P.

doi:10.2967/jnumed.109.065243

Cited by 76 publications

(90 citation statements)

References 18 publications

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“…21 21 for the liver blood volume in rats using the Evan blue dilution technique, and a value of 0.28 6 0.02 (SD) mLÁmL 21 using a macromolecular MRI contrast agent at equilibrium (22). SUV(t) was derived from a kinetic model analysis that involved a population-based, and hence noninvasive, input function in each individual (17). Although reduced relative deviations between experimental PET data and a theoretic fitting were observed in each individual (range, 1.6-5.7; mean, 3.8% over the patient series) (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…1, 3, and 4) and the population-based input function of Vriens et al (17). Table 1, including mean SUV in normal liver at t 1 (SUV 1 : early static acquisition) and t 2 (SUV 2 : first point of the delayed dynamic acquisition).…”

Section: Statistical Methods and Derivation Of Suv(t)mentioning

confidence: 99%

“…A triexponential decay input function was arbitrarily used in each individual from recently published data by Vriens et al (17). In comparison with their results, the decay constants have been modified to take into account the 18 4 where lC p (t) is the input function of Vriens et al (17), F is the fraction of the whole free 18 F-FDG in blood and reversible compartment in the tissue volume, and lFC p (t) is the 18 F-FDG time-activity curve in blood and reversible compartment.…”

Section: F-fdg Kinetic Modelingmentioning

confidence: 99%

“…For ethical reasons, in each patient of a series, it did not seem reasonable to regularly acquire data over the liver through the whole 18 F-FDG tissue time-activity curve, which may extend far beyond 2 h after injection (15). Therefore, in this noninvasive study, a kinetic model analysis was applied in each patient that involved a noninvasive population-based input function (17) and data from only 2 PET scan acquisitions: a first static acquisition (covering a large part of the body, for diagnostic purposes) and a second late dynamic acquisition over the liver (.2 h after injection) (18). Then, mean values of the 18 F-FDG uptake and release rate constant and of the fraction of free tracer in blood and interstitial volume were assessed from the patient series, and SUV(t) was derived from these mean values.…”

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confidence: 99%

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Is Liver SUV Stable over Time in 18F-FDG PET Imaging?

Laffon¹,

Adhoute²,

Clermont³

et al. 2011

Journal of Nuclear Medicine Technology

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This work investigated whether 18 F-FDG PET standardized uptake value (SUV) is stable over time in the normal human liver. Methods: The SUV-versus-time curve, SUV(t), of 18 F-FDG in the normal human liver was derived from a kinetic model analysis. This derivation involved mean values of 18 F-FDG liver metabolism that were obtained from a patient series (n 5 11), and a noninvasive population-based input function was used in each individual. Results: Mean values (695% reliability limits) of the 18 F-FDG uptake and release rate constant and of the fraction of free tracer in blood and interstitial volume were as follows: K 5 0.0119 mLÁmin 21 ÁmL 21 (60.0012), k R 5 0.0065Ámin 21 (60.0009), and F 5 0.21 mLÁmL 21 (60.11), respectively. SUV(t) (corrected for 18 F physical decay) was derived from these mean values, showing that it smoothly peaks at 75-80 min on average after injection and that it is within 5% of the peak value between 50 and 110 min after injection. Conclusion: In the normal human liver, decay-corrected SUV(t) remains nearly constant (with a reasonable 62.5% relative measurement uncertainty) if the time delay between tracer injection and PET acquisition is in the range of 50-110 min. In current clinical practice, the findings suggest that SUV of the normal liver can be used for comparison with SUV of suspected malignant lesions, if comparison is made within this time range.

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Section: Discussionmentioning

confidence: 99%

Section: Statistical Methods and Derivation Of Suv(t)mentioning

confidence: 99%

Section: F-fdg Kinetic Modelingmentioning

confidence: 99%

mentioning

confidence: 99%

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Is Liver SUV Stable over Time in 18F-FDG PET Imaging?

Laffon¹,

Adhoute²,

Clermont³

et al. 2011

Journal of Nuclear Medicine Technology

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“…Both assumptions are crucial to make kinetic modelling results reliable. Unfortunately, the population-based input function approach has been validated almost exclusively for [ 18 F]FDG [70], whereas few attempts have been made with other tracers [71].…”

Section: Non-invasive Approachesmentioning

confidence: 99%

Deriving physiological information from PET images: from SUV to compartmental modelling

Bertoldo

Rizzo

Veronese

2014

Clin Transl Imaging

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Positron emission tomography (PET) imaging has made it possible to detect the in vivo concentration of positron-emitting compounds accurately and non-invasively. In order to relate the radioactivity concentration measured using PET to the underlying physiological or biochemical processes, the application of mathematical models to describe tracer kinetics within a particular region of interest is necessary. Image analysis can be performed both by visual interpretation and quantitative assessment and, depending on the ultimate purposes of the analysis, several alternatives are available. In clinical practice, PET quantification is routinely performed using the standard uptake value (SUV), a semiquantitative index in use since the 1980s. Its computation is very simple since it requires only the PET measure at a prefixed sample time and the injected dose normalised to some anthropometric characteristic of the subject (generally body weight or body surface area). An alternative to the SUV is the tissue-to-plasma ratio (ratio). As its name indicates, this index is computed as the ratio between the tracer activity measured in the tissue and in the plasma pool within a prefixed time window. Moving from static to more informative dynamic PET acquisition, three model classes represent the most frequently used approaches: compartmental models, the spectral analysis modelling approach, and graphical methods. These approaches differ in terms of application assumptions (e.g. reversibility of tracer uptake, model structure, etc.) and computational complexity. They also produce different information about the system under study: from a macro-description of tracer uptake to a full quantitative characterisation of the physiological processes in which the tracer is involved. The application of these approaches to clinical routine is restricted by the need for invasive blood sampling. In order to avoid arterial cannulation and blood sample management, different alternative approaches have been developed for quantification of PET kinetics, including reference tissue methods. Although these approaches are appealing, the results obtained with several tracers are questionable. This review provides a complete overview of the semi-quantitative and quantitative methods used in PET analysis. The pros and cons of each method are evaluated and discussed.

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Generation of parametric K_i images for FDG PET using two 5‐min scans

Liu

et al. 2021

Medical Physics

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Purpose The net uptake rate constant (Ki) derived from dynamic imaging is considered the gold standard quantification index for FDG PET. In this study, we investigated the feasibility and assessed the clinical usefulness of generating Ki images for FDG PET using only two 5‐min scans with population‐based input function (PBIF). Methods Using a Siemens Biograph mCT, 10 subjects with solid lung nodules underwent a single‐bed dynamic FDG PET scan and 13 subjects (five healthy and eight cancer patients) underwent a whole‐body dynamic FDG PET scan in continuous‐bed‐motion mode. For each subject, a standard Ki image was generated using the complete 0–90 min dynamic data with Patlak analysis (t* = 20 min) and individual patient's input function, while a dual‐time‐point Ki image was generated from two 5‐min scans based on the Patlak equations at early and late scans with the PBIF. Different start times for the early (ranging from 20 to 55 min with an increment of 5 min) and late (ranging from 50 to 85 min with an increment of 5 min) scans were investigated with the interval between scans being at least 30 min (36 protocols in total). The optimal dual‐time‐point protocols were then identified. Regions of interest (ROI) were drawn on nodules for the lung nodule subjects, and on tumors, cerebellum, and bone marrow for the whole‐body‐imaging subjects. Quantification accuracy was compared using the mean value of each ROI between standard Ki (gold standard) and dual‐time‐point Ki, as well as between standard Ki and relative standardized uptake value (SUV) change that is currently used in clinical practice. Correlation coefficients and least squares fits were calculated for each dual‐time‐point protocol and for each ROI. Then, the predefined criteria for identifying a reliable dual‐time‐point Ki estimation for each ROI were empirically determined as: (1) the squared correlation coefficient (R2) between standard Ki and dual‐time‐point Ki is larger than 0.9; (2) the absolute difference between the slope of the equality line (1.0) and that of the fitted line when plotting standard Ki versus dual‐time‐point Ki is smaller than 0.1; (3) the absolute value of the intercept of the fitted line when plotting standard Ki versus dual‐time‐point Ki normalized by the mean of the standard Ki across all subjects for each ROI is smaller than 10%. Using Williams’ one‐tailed t test, the correlation coefficient (R) between standard Ki and dual‐time‐point Ki was further compared with that between standard Ki and relative SUV change, for each dual‐time‐point protocol and for each ROI. Results Reliable dual‐time‐point Ki images were obtained for all the subjects using our proposed method. The percentage error introduced by the PBIF on the dual‐time‐point Ki estimation was smaller than 1% for all 36 protocols. Using the predefined criteria, reliable dual‐time‐point Ki estimation could be obtained in 25 of 36 protocols for nodules and in 34 of 36 protocols for tumors. A longer time interval between scans provided a more accurate Ki estimation i...

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A Curve-Fitting Approach to Estimate the Arterial Plasma Input Function for the Assessment of Glucose Metabolic Rate and Response to Treatment

Cited by 76 publications

References 18 publications

Is Liver SUV Stable over Time in 18F-FDG PET Imaging?

Is Liver SUV Stable over Time in 18F-FDG PET Imaging?

Deriving physiological information from PET images: from SUV to compartmental modelling

Generation of parametric K_i images for FDG PET using two 5‐min scans

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A Curve-Fitting Approach to Estimate the Arterial Plasma Input Function for the Assessment of Glucose Metabolic Rate and Response to Treatment

Cited by 76 publications

References 18 publications

Is Liver SUV Stable over Time in 18F-FDG PET Imaging?

Is Liver SUV Stable over Time in 18F-FDG PET Imaging?

Deriving physiological information from PET images: from SUV to compartmental modelling

Generation of parametric Ki images for FDG PET using two 5‐min scans

Contact Info

Product

Resources

About

Generation of parametric K_i images for FDG PET using two 5‐min scans