No abstract

The current article presents theory for compartmental models used in positron emission tomography (PET). Both plasma input models and reference tissue input models are considered. General theory is derived and the systems are characterized in terms of their impulse response functions. The theory shows that the macro parameters of the system may be determined simply from the coefficients of the impulse response functions. These results are discussed in the context of radioligand binding studies. It is shown that binding potential is simply related to the integral of the impulse response functions for all plasma and reference tissue input models currently used in PET. This article also introduces a general compartmental description for the behavior of the tracer in blood, which then allows for the blood volume-induced bias in reference tissue input models to be assessed.

Abstract-Spectral band selection is a fundamental problem in hyperspectral data processing. In this paper, a new bandselection method based on mutual information (MI) is proposed. MI measures the statistical dependence between two random variables and can therefore be used to evaluate the relative utility of each band to classification. A new strategy is described to estimate the mutual information using a priori knowledge of the scene, reducing reliance on a 'ground truth' reference map, by retaining bands with high associated MI values (subject to certain so-called 'complementary' conditions). Simulations of classification performance on 16 classes of vegetation from the AVIRIS 92AV3C dataset show the effectiveness of the method, which outperforms an MI-based method using the associated reference map, an entropy-based method and a correlation-based method. It is also competitive with the steepest ascent (SA) algorithm at much lower computational cost.

A kinetic modeling approach for the quantification of in vivo tracer studies with dynamic positron emission tomography (PET) is presented. The approach is based on a general compartmental description of the tracer's fate in vivo and determines a parsimonious model consistent with the measured data. The technique involves the determination of a sparse selection of kinetic basis functions from an overcomplete dictionary using the method of basis pursuit denoising. This enables the characterization of the systems impulse response function from which values of the systems macro parameters can be estimated. These parameter estimates can be obtained from a region of interest analysis or as parametric images from a voxel-based analysis. In addition, model order estimates are returned that correspond to the number of compartments in the estimated compartmental model. Validation studies evaluate the methods performance against two preexisting data led techniques, namely, graphical analysis and spectral analysis. Application of this technique to measured PET data is demonstrated using [11C]diprenorphine (opiate receptor) and [11C]WAY-100635 (5-HT1A receptor). Although the method is presented in the context of PET neuroreceptor binding studies, it has general applicability to the quantification of PET/SPECT radiotracer studies in neurology, oncology, and cardiology.

Abstract-A conventional active contour formulation suffers difficulty in appropriate choice of an initial contour and values of parameters. Recent approaches have aimed to resolve these problems but can compromise other performance aspects. To relieve the problem in initialization, we use a dual active contour, which is combined with a local shape model to improve the parameterization. One contour expands from inside the target feature, the other contracts from the outside. The two contours are interlinked to provide a balanced technique with an ability to reject "weak" local energy minima.