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Bayesian and population approaches for pixel-wise quantification of positron emission Tomography images: ridge regression and Global-Two-Stage
No full textPET (Positron Emission Tomography) is a technique in which a radioactive tracer which decays by positron emission is injected into the subject's body. Through a complex instrumentation and sophisticated reconstruction algorithms, it is then possible to compute the distribution of the tracer over time in the area of interest, which is the desired outcome of the measurement.
After reconstruction the image is ready for quantitative analysis, necessary to derive the so-called kinetic parameters, which are relevant in that they have a physiological meaning. This analysis may be performed either at ROI level (Region-Of-Interest, an anatomically homogeneous region such as cerebellum or thalamus) or at pixel level. In the latter scenario kinetic parameters are computed separately for each of the hundreds of thousand of pixels of the image, and the so-called parametric images are generated. Pixel-by-pixel analysis has the intrinsic problem due to the high noise level of pixel TACs (Time Activity Curve, i.e. the value of radioactive concentration as a function of time)
as this may give rise to unreliable estimates for the kinetic parameters or to non-convergence of the algorithms used for estimation. Parametric maps, however, are of paramount importance as they are characterized by a high spatial resolution: phenomena such as a lesion in a cerebral structure or the presence of a small tumoral mass may be invisible with ROI analysis but detectable even at simple visual inspection through pixel analysis.
The aim of this thesis was to develop fast methods for the generation of more reliable parametrci maps. A method already developed in literature, known as ridge regression (RR), was comprehensively studied and developed; in addition, a technique completely new to the field of PET , Global-Two-Stages(GTS), belonging to the field of population approaches , was proposed and tested. The basic ideas of these methodologies which make them part of the family of Bayesian approaches is, loosely speaking, to employ, in the parameter estimation for a given pixel, not only the TAC of that pixel but to incorporate also the information driving from the other pixels in order to obtain a global regularizing effect, penalizing, for instance the noisiest TACs..The analysis was carried out first on simulated data because, in order to be able to compute indices which quantify the goodness of final estimates such BIAS and Root Mean Square Error (RMSE), the knowledge of "true" parameters is necessary, and data are necessarily to be simulated. The performances of the proposed Bayesian algorithms were compared to those of the appropriate "gold standard" , the most used estimation method for the tracer under examination. Interest was then addressed to a real rich dataset of the tracer [11C]PK11195, very used for the study of pathologies such as Alzheimer and Huntington, in that it is linked to the overall level of neuroinflammation.
The analysis of simulated data revealed that RR and GTS gave always rise to decrease of RMSE, leving BIAS substantially unchanged.The improvements are clearly dependent on the tracer, nose level, and specific kinetic parameter considered.The study of the [11C]PK11195 dataset showed how RR and GTS much more regular parametric maps with respect to SRTM, the "gold standard" used for comparison. The proposed approaches (RR and GTS) also yielded excellent results in terms of the ability to differentiate between healthy and ill subjects on the basis of the maps of the kinetic parameter BP (Binding Potential): this fact has clearly a significant diagnostic impact as more reliable methods (i.e. with higher sensitivity and specificity) are needed for the daily application in clinical practise.
In conclusion, Ridge Regression and Global-Two-Stage are precious instruments for the improvement of parametric maps: both methodologies can be applied with virtually any tracer and model, provided that initial estimates can be computed through standard weighted least squares, and have therefore a wide range of applicability
PET parametric imaging improved by global-two-stage method.
No full textThe analysis of positron emission tomography (PET) images at the pixel level may yield unreliable parameter estimates due to the low signal-to-noise ratio of pixel time activity curves (TAC). To address this issue it can be helpful to use techniques developed in the pharmacokinetic/pharmacodynamic area and referred to as 'population approaches.' In this paper, we describe a new estimation algorithm, the Global-Two-Stage (GTS), and assess its performances through Monte Carlo simulations. GTS was compared to the basis function method on synthetic [11C](R)-PK11195 data, and to weighted nonlinear least squares on synthetic [11C]WAY100,635 data. In both cases, GTS produced parameter estimates with lower root mean square error and lower bias than the well-established estimation methods used for comparison, with a negligible increase of computational time. GTS was applied first to all the pixels of the simulated slices. Then, after a preliminary segmentation of pixels into more homogeneous populations, GTS was applied to each subpopulation separately: this last approach provided the best results. In conclusion, GTS is a powerful and fast technique that can be applied to improve parametric maps, as long as preliminary estimates of parameters and of their covariance are available
Global-two-stage approach for [11C]DASB parametric imaging: evaluation on simulated data
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Voxel-based quantification of L-[1-11C]leucine kinetics for determination of regional rates of cerebral protein synthesis
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Voxel-based estimation of kinetic model parameters of the L-[1-(11)C]leucine PET method for determination of regional rates of cerebral protein synthesis: validation and comparison with region-of-interest-based methods
No full textWe adapted and validated a basis function method (BFM) to estimate at the voxel level parameters of the kinetic model of the L-[1-(11)C]leucine positron emission tomography (PET) method and regional rates of cerebral protein synthesis (rCPS). In simulation at noise levels typical of voxel data, BFM yielded low-bias estimates of rCPS; in measured data, BFM and nonlinear least-squares parameter estimates were in good agreement. We also examined whether there are advantages to using voxel-level estimates averaged over regions of interest (ROIs) in place of estimates obtained by directly fitting ROI time-activity curves (TACs). In both simulated and measured data, fits of ROI TACs were poor, likely because of tissue heterogeneity not taken into account in the kinetic model. In simulation, rCPS determined from fitting ROI TACs was substantially overestimated and BFM-estimated rCPS averaged over all voxels in an ROI was slightly underestimated. In measured data, rCPS determined by regional averaging of voxel estimates was lower than rCPS determined from ROI TACs, consistent with simulation. In both simulated and measured data, intersubject variability of BFM-estimated rCPS averaged over all voxels in a ROI was low. We conclude that voxelwise estimation is preferable to fitting ROI TACs using a homogeneous tissue model
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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