Smoothing dynamic positron emission tomography time courses using functional principal components

A functional smoothing approach to the analysis of PET time course data is presented. By borrowing information across space and accounting for this pooling through the use of a nonparametric covariate adjustment, it is possible to smooth the PET time course data thus reducing the noise. A new model for functional data analysis, the Multiplicative Nonparametric Random Effects Model, is introduced to more accurately account for the variation in the data. A locally adaptive bandwidth choice helps to determine the correct amount of smoothing at each time point. This preprocessing step to smooth the data then allows Subsequent analysis by methods Such as Spectral Analysis to be substantially improved in terms of their mean squared error

Kinetics of protein-based in vivo Imaging tracers for positron emission tomography

Within the framework of the “Sel-tag imaging project”, a novel method was used to rapidly label protein tracers and the in vivo targeting abilities of these tracers were studied in animal models of cancer using a preclinical positron emission tomography (PET) camera. To first evaluate and optimize preclinically the use of PET tracers can facilitate their translation to and implementation in human patient studies. The ultimate goal of the different projects within the Sel-tag imaging project was to find imaging biomarkers that could potentially be used for individualizing cancer treatment and thereby improve the therapeutic results. This thesis focuses on methods employed to describe the distribution of these protein-based tracers in human xenografts. Many of the techniques used had been developed for other imaging circumstances. Therefore verification for these imaging applications was an important aspect of these papers. Paper I examined the distribution in a tumour of a medium-sized AnnexinA5-based tracer that targeted phosphatidylserine externalised during cell death in tumours in two cases; first, with no pre-treatment (baseline) and, second, after pre-treatment with a chemotherapeutic agent. Small differences between tracer uptakes in the two cases required a macro parameter analysis method for quantifications. Evaluations of the influence of the enhanced permeability and retention effect by using a size-matched control were introduced. The AnnexinA5 results were compared to those of the metabolic tracer [18F]FDG and complemented with circulating serum markers to increase sensitivity. Paper II extended the analysis in paper I to incorporate more verifications that were also more thorough. The choice of input (blood or reference tissue) and the statistical significance of intergroup comparisons when using conventional uptake measurements and the more involved macro parameter analyses like in paper I were compared. We also proposed that distribution volume ratio was a more appropriate quantification parameter concept for these protein-based tracers with relatively large non-specific uptake. Paper III assessed the smaller Affibody™ tracer ZHER2:342 as an imaging biomarker for human epidermal growth factor 2 (HER2), whose overexpressions are associated with a poor prognosis for breast cancer patients. In order to demonstrate specific binding to HER2, pre-treatment of the tumour with unlabelled protein and uptake in xenografts with low HER2 expression was evaluated. Ex vivo immunohistochemistry of expression levels supported the imaging results. Paper IV examined a radiopharmaceutical that targeted the epidermal growth factor receptor (EGFR), whose overexposure in tumours is associated with a negative prognosis. Again an Affibody™ molecule, (ZEGFR:2377), was used and, as in in paper I, a size-matched control was also used to estimate the non-specific uptake. Uptakes, quantified by conventional uptake methods, varied in tumours with different EGFR expression levels. Ex vivo analyses of expression levels were also performed. Paper V addressed the non-uniform (heterogeneous) uptake of different tracers in a tumour tissue. An algorithm was written that aimed at incorporating all relevant aspects that will influence non-uniformity. Histograms were generated that visualized how the frequency and spread of deviations contributed to the heterogeneity. These aspects could not always be attended in a direct manner, but instead had to be handled in an indirect way. The effect of varying imaging parameters was examined as part of the validation procedure. The method developed is a robust, user-friendly tool for comparing heterogeneity in similar volume preclinical tumor tissues

A Unified Framework For Blood Data Modeling In Dynamic Positron Emission Tomography Studies

Quantification of dynamic PET images requires the measurement of radioligand concentrations in the arterial plasma. In general, this cannot be derived from PET images directly but it must be measured from blood samples taken from the subject’s radial artery. The aim of this thesis was to develop and validate a unified framework for the blood data modeling, which was both biologically and experimentally informed, in order to achieve a better description of the blood data

Dynamic positron emission tomography data-driven analysis using sparse Bayesian learning

A method is presented tor the analysis of dynamic positron emission tomography (PET) data using sparse Bayesian learning. Parameters are estimated in a compartmental framework using an over-complete exponential basis set and sparse Bayesian learning. The technique is applicable to analyses requiring either a plasma or reference tissue input function and produces estimates of the system's macro-parameters and model order. In addition, the Bayesian approach returns the posterior distribution which allows for some characterisation of the error component. The method is applied to the estimation of parametric images of neuroreceptor radioligand studies

Pattern recognition and machine learning for magnetic resonance images with kernel methods

The aim of this thesis is to apply a particular category of machine learning and pattern recognition algorithms, namely the kernel methods, to both functional and anatomical magnetic resonance images (MRI). This work specifically focused on supervised learning methods. Both methodological and practical aspects are described in this thesis. Kernel methods have the computational advantage for high dimensional data, therefore they are idea for imaging data. The procedures can be broadly divided into two components: the construction of the kernels and the actual kernel algorithms themselves. Pre-processed functional or anatomical images can be computed into a linear kernel or a non-linear kernel. We introduce both kernel regression and kernel classification algorithms in two main categories: probabilistic methods and non-probabilistic methods. For practical applications, kernel classification methods were applied to decode the cognitive or sensory states of the subject from the fMRI signal and were also applied to discriminate patients with neurological diseases from normal people using anatomical MRI. Kernel regression methods were used to predict the regressors in the design of fMRI experiments, and clinical ratings from the anatomical scans

A non compartmental method for functional quantitative imaging with Positron Emission Tomography and irreversible tracers

In 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