Kinetic modeling of [<sup>18</sup>F]FDG in skeletal muscle by PET: a four-compartment five-rate-constant model

Bertoldo, Alessandra; Peltoniemi, Pauliina; Oikonen, Vesa; Knuuti, Juhani; Nuutila, Pirjo; Cobelli, Claudio

doi:10.1152/ajpendo.2001.281.3.e524

Cited by 78 publications

(122 citation statements)

References 26 publications

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“…35 The total amount measured by PET, q measured (t), is the summation of all tracer amounts present in the volume of observation. The measurement mass equation is: q measured t ð Þ ¼ q nd ðtÞ þ q s ðtÞ þ q vasc ðtÞ þ q b ðtÞ ð A1:1Þ…”

Section: Appendix 1 Derivation Of the Measurement Equation Of 2tcm-1kmentioning

confidence: 99%

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Kinetic Modeling without Accounting for the Vascular Component Impairs the Quantification of [¹¹C]PBR28 Brain PET Data

Rizzo

Veronese

Tonietto

et al. 2014

J Cereb Blood Flow Metab

112

136

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The positron emission tomography radioligand [ 11 C]PBR28 targets translocator protein (18 kDa) (TSPO) and is a potential marker of neuroinflammation. [ 11 C]PBR28 binding is commonly quantified using a two-tissue compartment model and an arterial input function. Previous studies with [ 11 C]-(R)-PK11195 demonstrated a slow irreversible binding component to the TSPO proteins localized in the endothelium of brain vessels, such as venous sinuses and arteries. However, the impact of this component on the quantification of [ 11 C]PBR28 data has never been investigated. In this work we propose a novel kinetic model for [ 11 C]PBR28. This model hypothesizes the existence of an additional irreversible component from the blood to the endothelium. The model was tested on a data set of 19 healthy subjects. A simulation was also performed to quantify the error generated by the standard twotissue compartmental model when the presence of the irreversible component is not taken into account. Our results show that when the vascular component is included in the model the estimates that include the vascular component (2TCM-1K) are more than three-fold smaller, have a higher time stability and are better correlated to brain mRNA TSPO expression than those that do not include the model (2TCM).

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Section: Appendix 1 Derivation Of the Measurement Equation Of 2tcm-1kmentioning

confidence: 99%

“…To demonstrate that the proposed model 2TCM-1K is uniquely identifiable a priori we used a transfer function approach. 35 Rearranging the Laplace transforms of Equation (A1) gives…”

Section: Appendix 2 Demonstration Of a Priori Identifiability Of 2tcmmentioning

confidence: 99%

Kinetic Modeling without Accounting for the Vascular Component Impairs the Quantification of [¹¹C]PBR28 Brain PET Data

Rizzo

Veronese

Tonietto

et al. 2014

J Cereb Blood Flow Metab

112

136

View full text Add to dashboard Cite

show abstract

“…SA has been applied to preclinical (rats and rabbits) [44,45] as well as to clinical data. Most of its applications are related to the investigation of brain neuroreceptors [39,[46][47][48] or enzymes [49,50] even though SA has also been applied to PET studies involving the heart [51], skeletal leg muscle [52,53], breast cancer [54] and gastrointestinal cancer [55]. Most of these applications aimed to exploit spectral-based procedures to overcome the limits of the standard quantification methodologies.…”

Section: Applicationsmentioning

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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“…The initial ndings in lean volunteers indicate that this model can provide a clear segregation of transmembrane [18F] FDG transport and phosphorylation (42 ). These are very promising characteristics for assessing which process is rate-limiting.…”

Section: Optimizing the Modeling Of Dynamic Pet Imaging Of Skeletal Mmentioning

confidence: 87%

Assessing Skeletal Muscle Glucose Metabolism With Positron Emission Tomography

Kelley¹,

Price²,

Cobelli³

2001

IUBMB Life

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SummaryInsulin has a marked effect to stimulate the transport and metabolism of glucose in skeletal muscle in healthy individuals, whereas an impaired response, termed insulin resistance, is a major risk factor for diabetes mellitus and other metabolic diseases. Studies of the molecular physiology of insulin action in skeletal muscle indicate that a principal loci of control resides within the proximal steps of glucose transport and phosphorylation. Deoxyglucose, the metabolism of which is limited to these proximal steps, is widely used for in vitro studies of insulin action on glucose transport. The technologies of PET imaging provide a unique opportunity to carry out similar studies in vivo in human skeletal muscle. In this instance, a short-lived positron labeled tracer, [18F] FDG, can be given at suf ciently high speci c activity to image not only glucose uptake, but by dynamic PET imaging, by monitoring the time course of [18F] FDG tissue activity, data can be generated to examine the kinetics of glucose transport and phosphorylation. The experimental procedures of this approach, including an overview of the mathematical modeling, are described in this review, along with some of the key ndings of the initial applications of PET for the study of glucose metabolism in human skeletal muscle.

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Kinetic modeling of [¹⁸F]FDG in skeletal muscle by PET: a four-compartment five-rate-constant model

Cited by 78 publications

References 26 publications

Kinetic Modeling without Accounting for the Vascular Component Impairs the Quantification of [¹¹C]PBR28 Brain PET Data

Kinetic Modeling without Accounting for the Vascular Component Impairs the Quantification of [¹¹C]PBR28 Brain PET Data

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

Assessing Skeletal Muscle Glucose Metabolism With Positron Emission Tomography

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Kinetic modeling of [18F]FDG in skeletal muscle by PET: a four-compartment five-rate-constant model

Cited by 78 publications

References 26 publications

Kinetic Modeling without Accounting for the Vascular Component Impairs the Quantification of [11C]PBR28 Brain PET Data

Kinetic Modeling without Accounting for the Vascular Component Impairs the Quantification of [11C]PBR28 Brain PET Data

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

Assessing Skeletal Muscle Glucose Metabolism With Positron Emission Tomography

Contact Info

Product

Resources

About

Kinetic modeling of [¹⁸F]FDG in skeletal muscle by PET: a four-compartment five-rate-constant model

Kinetic Modeling without Accounting for the Vascular Component Impairs the Quantification of [¹¹C]PBR28 Brain PET Data

Kinetic Modeling without Accounting for the Vascular Component Impairs the Quantification of [¹¹C]PBR28 Brain PET Data