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A non compartmental method for functional quantitative imaging with Positron Emission Tomography and irreversible tracers
Full text linkIn dynamic Positron Emission Tomography (PET) studies the term "Spectral Analysis" indicates a time-invariant single input/single output model, used for the data quantification [Cunningham and Jones, 1993]. Despite the name and its common use in the engineering field, SA does not indicate an analysis in the frequency domain but, instead, it represents a method from which the radioactivity concentration measured with PET can be related to the underlying physiological processes of the investigated system. SA is so-called, because it provides a “spectrum” of the kinetic components from which it is possible to derive a large variety of physiological parameters, depending on the characteristics of the analyzed tracers.
In the last years SA has been widely used with a large number of PET tracers to study brain and non brain tissues, demonstrating to be a very flexible method. Differently from the most used PET quantification approaches, like the compartmental modelling [Godfrey, 1982] or the graphical methods [Patlak, 1983; Logan et al., 1990], SA can be applied to homogeneous as well as to heterogeneous kinetic tissues without any specific compartmental model assumptions. This characteristic makes it a high informative investigative tool especially for the analysis of novel PET tracers.
The most critical aspect of SA is related to its sensitivity to the presence of noise in the data. This characteristic makes SA not properly indicated for the application to low signal-to-noise ratio (SNR) data [Turkheimer et al., 1994]. During the past several years, several solutions have been introduced to improve the robustness of SA in the presence of noise. The most famous example is represented by rank-shaping spectral analysis (RS) [Turkheimer et al., 2003]. However, even if RS has been shown to be a precise and accurate quantification method, its applicability is limited to tracers with reversible uptake. This is a severe restriction if we consider that one of the most used PET tracer for clinical research, 18F-Fluorodeoxyglucose ([18F]FDG), is irreversible.
In this work we present SAIF, (Spectral Analysis with Iterative Filter), a SA-based method for the quantification of PET data investigated with irreversible-uptake tracers. SAIF has been designed in order to maintain the main advantages of SA but providing a superior robustness to measurement noise. The final aim was to create a reliable and flexible PET quantification tool, offering a valid alternative to standard methodologies for functional quantitative imaging with PET and irreversible tracers.
The organization of this thesis is as follows: Chapter 1 offers a brief introduction to PET technique and its quantification methods. A comparison between compartmental modelling approaches and graphical methods is also presented, in order to provide the operative context in which SA is located. Chapter 2 contains the mathematical formalization of the SA model. Standard and filtered SA versions are presented with particular attention to novelty elements introduced by SAIF. In Chapter 3 and Chapter 4, SAIF will be tested with brain and non brain PET data. Several datasets obtained by using different PET tracers are considered. As an example for brain tissue quantification, SAIF application to L-[1-11C]Leucine and [11C]SCH442416 data is presented. For non brain tissues, instead, analysis of three datasets is reported: 1) [18F]FDG PET studies applied to skeletal leg muscle, 2) [18F]FLT PET studies applied to breast cancer patients and 3) [18F]FDG PET studies applied to normal control and acute lung injury patients. For each dataset SAIF results are compared with those provided by already validated methods and used in the literature as reference for the quantification. This analysis allows to compare SAIF performances with those offered by the current state of the art. Chapter 5 investigates the conditioning of the kinetic heterogeneity to PET quantification. The relationship between this problem, the spatial resolution of the imaging technique and the noise level of the data is also considered. This aspect is a critical point for PET quantification because when it is not taken into account it can lead to heavily biased results. Particular attention is given to how SAIF addresses this issue. In Chapter 6 we present SAKE, a software application in-house developed which implements the major SA algorithms. SAKE manages the whole process of PET quantification: from data pre–processing to the result analysis. No other program or additional tool is required. Chapter 7 discusses the most relevant criticalities of the SA approach and of SAIF method in particular. Considerable attention is given to the definition of the setting algorithm as well as to the model assumptions used by SAIF to describe the data. In Chapter 8 an overall discussion is presented with a conclusive summary about strengths and weakness of SAIF method.
The appendix of the thesis is dedicated to the some additional works, not directly related to the main argument of this PhD project, but of interest for the PET field. This research concerns 1) the development of voxelwise quantification methods for [11C](R)Rolipram PET data, 2)the use of non linear mixed effects modelling for plasma metabolite correction, and 3) the evaluation of the sensitivity of PET receptor occupancy studies to the experimental design
Dopaminergic Imaging to Predict Treatment Response in Mental Illness
No full textA neuroimaging-based approach to predict treatment response in mental disorders by acquiring and analysing brain PET dopamine measures from patients. The method uses a short, simplified protocol for [18F]FDOPA brain PET imaging adapted for clinical practice (104). Individual [18F]FDOPA brain PET data are then quantified with a fully-automated analysis pipeline to extract information on the dopamine function of the subject (106). This information coupled with clinical information is run through a prediction algorithm to identify those patients whose illness will not respond to conventional antipsychotics (108)
An integrated tool for quantification of dynamic PET studies: Spectral Analysis Kinetic Estimation
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Deriving physiological information from PET images: from SUV to compartmental modelling
Full text linkPositron 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 semi-quantitative index in use since the 1980s. Its computation is very simple since it requires only the PET measure at a pre-fixed 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 pre-fixed 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
SAKE: A new quantification tool for positron emission tomography studies.
No full textIn dynamic positron emission tomography (PET) studies, spectral analysis (SA) refers to a data-driven quantification method, based on a single-input single-output model for which the transfer function is described by a sum of exponential terms. SA allows to quantify numerosities, amplitudes and eigenvalues of the transfer function allowing, in this way, to separate kinetic components of the tissue tracer activity with minimal model assumptions. The SA model can be solved with a linear estimator alone or with numerical filters, resulting in different types of SA approaches. Once estimated the number, amplitudes and eigenvalues of the transfer function, one can distinguish the presence in the system of irreversible and/or reversible components as well as derive parameters of physiological significance. These characteristics make it an appealing alternative method to compartmental models which are widely used for the quantitative analysis of dynamic studies acquired with PET. However, despite its applicability to a large number of PET tracers, its implementation is not straightforward and its utilization in the nuclear medicine community has been limited especially by the lack of an user-friendly software application. In this paper we proposed SAKE, a computer program for the quantitative analysis of PET data through the main SA methods. SAKE offers a unified pipeline of analysis usable also by people with limited computer knowledge but with high interest in SA
A non linear mixed effect modelling approach for metabolite correction of the arterial input function in pet studies
No full textQuantitative PET studies with arterial blood sampling usually require the correction of the measured total plasma activity for the presence of metabolites. In particular, if labelled metabolites are found in the plasma in significant amounts their presence has to be accounted for, because it is the concentration of the parent tracer which is required for data quantification. This is achieved by fitting a Parent Plasma fraction (PPf) model to discrete metabolite measurements. The commonly used method is based on an individual approach, i.e. for each subject the PPf model parameters are estimated from its own metabolite samples, which are, in general, sparse and noisy. This fact can compromise the quality of the reconstructed arterial input functions, and, consequently, affect the quantification of tissue kinetic parameters. In this study, we proposed a Non-Linear Mixed Effect Modelling (NLMEM) approach to describe metabolite kinetics. Since NLMEM has been developed to provide robust parameter estimates in the case of sparse and/or noisy data, it has the potential to be a reliable method for plasma metabolite correction. Three different PET datasets were considered: [11C]-(+)-PHNO (54 scans), [11C]-PIB (22 scans) and [11C]-DASB (30 scans). For each tracer both simulated and measured data were considered and NLMEM performance was compared with that provided by individual analysis. Results showed that NLMEM provided improved estimates of the plasma parent input function over the individual approach when the metabolite data were sparse or contained outliers
Spectral Analysis of Dynamic PET Studies: A Review of 20 Years of Method Developments and Applications
Full text linkIn Positron Emission Tomography (PET), spectral analysis (SA) allows the quantification of dynamic data by relating the radioactivity measured by the scanner in time to the underlying physiological processes of the system under investigation. Among the different approaches for the quantification of PET data, SA is based on the linear solution of the Laplace transform inversion whereas the measured arterial and tissue time-activity curves of a radiotracer are used to calculate the input response function of the tissue. In the recent years SA has been used with a large number of PET tracers in brain and nonbrain applications, demonstrating that it is a very flexible and robust method for PET data analysis. Differently from the most common PET quantification approaches that adopt standard nonlinear estimation of compartmental models or some linear simplifications, SA can be applied without defining any specific model configuration and has demonstrated very good sensitivity to the underlying kinetics. This characteristic makes it useful as an investigative tool especially for the analysis of novel PET tracers. The purpose of this work is to offer an overview of SA, to discuss advantages and limitations of the methodology, and to inform about its applications in the PET field
Voxelwise quantification of [11C](R)-rolipram PET data: a comparison between model-based and data-driven methods
No full textThis study compared model-based and data-driven methods to assess the best methodology for generating precise and accurate parametric maps of the parameters of interest in [11 C](R)-rolipram brain positron-emission tomography studies. Parametric images were generated using (1) a two-tissue compartmental model (2TCM) solved with the hierarchical basis function method (H-BFM) linear estimator; (2) data-driven spectral-based methods: standard spectral analysis (std SA) and rank-shaping SA (RS); and (3) the Logan graphical plot. Nonphysiologic V T estimates were eliminated and the remaining ones were compared with the reference values, i.e., those obtained with a voxelwise 2TCM solved with a nonlinear estimator. With regard to voxelwise V T estimates, H-BFM showed the best agreement with weighted nonlinear least square (WNLLS) values and the lowest percentage of mean relative difference (1±1%). All methods showed comparable variability in the relative differences. H-BFM provided the best correlation with WNLLS (y=1.034x-0.013; R 2 =0.973). Despite a slight bias, the other three methods also showed good agreement and high correlation (R 2 >0.96). H-BFM yielded the most reliable voxelwise quantification of [11 C](R)-rolipram as well as the complete description of the tracer kinetic. The Logan plot represents a valid alternative if only V T estimation is required. Its marginally higher bias was outweighed by a low computational time, ease of implementation, and robustness
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