Models for computer simulation studies of input functions for tracer kinetic modeling with positron emission tomography

In tracer kinetic modeling with Positron Emission Tomography (PET), the direct measurement (piecewise linear approximation) of plasma time-activity curve of tracer (PTAC) is often used as the input function to estimate regional physiological parameters. However, no explicit general model is available for PTAC itself, which limits the further study of the effects of PTAC, such as PTAC measurement noise or PTAC sampling schedules, on the physiological parameters estimation. A PTAC model is proposed in this paper and compared with other four possible candidates. Eight sets of [18F]-fluoro-2-deoxy-D-glucose (FDG) experimental data were used to test the models and several statistical criteria were used to validate their adequacy. An application of the model to improve the estimation of local cerebral metabolic rate of glucose (LCMRGlc) is presented. This model is also expected to be useful for generating realistic PTAC curves in computer simulation studies of other tracers and their kinetic modeling characteristics.

A computer simulation study on the effects of input function measurement noise in tracer kinetic modeling with positron emission tomography (PET)

Tracer kinetic modeling with positron emission tomography (PET) requires measurements of the time-activity curves in both plasma (PTAC) and tissue (TTAC) to estimate physiological parameters, i.e. to fit the parameters of certain compartmental models using PTAC and TTAC as the model input and output functions, respectively. However, the estimation usually ignores the measurement noise in plasma tracer activity curves. The accuracy and reliability of the physiological parameters estimated by ignoring such noise are not well understood. In this paper, effects of noise in [18F] 2-fluoro-2-deoxy-D-glucose (FDG) tracer plasma concentration measurements on estimation of local cerebral metabolic rates of glucose (LCMRGlc) with PET is investigated systematically. The PTAC modeling approach used in this paper also provides a realistic means to filter out the noise and to improve the physiological parameter accuracy, which can be potentially used in model-based non-invasive measurements of PTAC.

A study on statistically reliable and computationally efficient algorithms for generating local cerebral blood flow parametric images with positron emission tomography

With the advent of positron emission tomography (PET), a variety of techniques have been developed to measure local cerebral blood flow (LCBF) noninvasively in humans. A potential class of techniques, which includes linear least squares (LS), linear weighted least squares (WLS), linear generalized least squares (GLS), and linear generalized weighted least squares (GWLS), is proposed. The statistical characteristics of these methods are examined by computer simulation. The authors present a comparison of these four methods with two other rapid estimation techniques developed by Huang et al. (1982) and Alpert (1984), and two classical methods, the unweighted and weighted nonlinear least squares regression. The results show that these methods can take full advantage of the contribution from the fine temporal sampling data of modern tomographs, and thus provide statistically reliable estimates that are comparable to those obtained from nonlinear LS regression. These methods also have high computational efficiency, and the parameters can be estimated directly from operational equations in one single step. Therefore, they can potentially be used in image-wide estimation of local cerebral blood flow and distribution volume with PET.

Decomposition-based qualitative experiment design algorithms for a class of compartmental models

Qualitative experiment design, to determine experimental input/output configurations that provide identifiability for specific parameters of interest, can be extremely difficult if the number of unknown parameters and the number of compartments are relatively large. However, the problem can be considerably simplified if the parameters can be divided into several groups for separate identification and the model can be decomposed into smaller submodels for separate experiment design. Model decomposition-based experiment design algorithms are proposed for a practical class of large-scale compartmental models representative of biosystems characterized by multiple input sources and unidirectional interconnectivity among subsystems. The model parameters are divided into three types, each of which is identified consecutively, in three stages, using simpler submodel experiment designs. Several practical examples are presented. Necessary and sufficient conditions for identifiability using the algorithm are also discussed.

[An analysis of findings EEG and brain CT scans in patients with primary hypoparathyroidism]

10 cases of primary hypoparathyroidism showed abnormal findings on EEG. Among them 9 cases were also verified by CT scans. It was considered that the severity of abnormalities in primary hypoparathyroidism was not only influenced by the serum concentration of calcium, but also related with the severity and extent of calcification in the brain. These findings would be of significance with regard to the diagnosis, treatment and prognostic evaluation of primary hypoparathyroidism.

Cut set analysis of compartmental models with applications to experiment design

Conventional compartmental analysis typically involves equations derived from mass-rate balance considerations for each compartment (pool), with each equation associated with a single pool. However, alternative mathematical descriptions, which effectively group pools into various other configurations, facilitate model analysis in certain applications, e.g., for kinetic experiment design or analysis. Such equivalent models are usually obtained using (often) complex matrix operations. An alternative approach, cut set analysis, can be applied directly to the graph of the compartmental model to readily generate alternative mathematical descriptions in which the needed equivalence transformations are easily performed graphically. This graphical transformation is developed here for linear, time-invariant multicompartmental models in which particular parameter values are the experimental objective. The method potentially provides greater flexibility in analyzing complex compartmental models in theory and practice, and it is exemplified here by application to the design of steady-state kinetic endocrine system studies in experimental animals.

Periprosthetic chronic inflammation characterized through the measurement of superoxide anion production by synovial-derived macrophages

Periprosthetic macrophages were isolated from the synovium of primary and revision arthroplasty patients. Inflammatory activity was determined by the level of superoxide (O2-) production de novo and in response to phorbol myristate acetate (PMA) stimulation. Nonstimulated primary arthroplasty-derived macrophages produced 2.54 +/- 2.04 pmoles of O2-/minute/10(5) cells. When identical reaction tubes were stimulated with PMA, O2- levels increased to 5.76 +/- 3.77 pmol of O2-/minute/10(5) cells. Nonstimulated revision arthroplasty-derived macrophages produced 3.26 +/- 2.02 pmol of O2-/minute/10(5) cells during this ten-minute time period. When identical reaction tubes were stimulated with PMA, O2- levels increased to 3.98 +/- 2.52 pmol of O2-/minute/10(5) cells. The difference in the ratio of O2- production in response to stimulation between primary and revision groups was statistically significant. The observation of a chronic moderate level of activation and the lack of responsiveness to a potent stimulator suggests that macrophage inflammatory activity is down-regulated in periprosthetic synovium.

Estimation of local cerebral protein synthesis rates with L-[1-11C]leucine and PET: methods, model, and results in animals and humans

We have estimated the cerebral protein synthesis rates (CPSR) in a series of normal human volunteers and monkeys using L-[1-11C]leucine and positron emission tomography (PET) using a three-compartment model. The model structure, consisting of a tissue precursor, metabolite, and protein compartment, was validated with biochemical assay data obtained in rat studies. The CPSR values estimated in human hemispheres of about 0.5 nmol/min/g agree well with hemispheric estimates in monkeys. The sampling requirements (input function and scanning sequence) for accurate estimates of model parameters were investigated in a series of computer simulation studies.

Comparative aspects of the distribution, metabolism, and excretion of six iodothyronines in the rat

We have studied the kinetics of 3 iodothyronines, 3,3'-diiodothyronine (T2), 3',5'-T2, and 3'-monoiodothyronine (T1), in groups of young adult male rats maintained under normal steady state physiological conditions. We have also performed a comparative analysis of these results, combined with corresponding kinetic indices of T4, T3, and rT3, to obtain a more comprehensive understanding of normal thyroid hormone production, distribution, and metabolism. Tracer doses of 125I-labeled 3,3'-T2, 3',5'-T2, and 3'-T1 were separately injected iv, and blood samples were collected 6-12 times for each iodothyronine in optimized sequential kinetic studies designed to maximize the precision of kinetic parameters. Labeled iodothyronines were separated quantitatively from their metabolites in each plasma sample by Sephadex G-25 column chromatography. Conventional kinetic analysis of the resulting data generated distribution volume, clearance, turnover, and mean residence time indices for each iodothyronine, and concomitant compartmental analysis of the same data provided additional results useful for integration and comparative analysis of the 6 iodothyronines. Kinetic parameters for all but T4 and T3 were similar, suggesting that similar mechanisms are responsible for the transport, metabolism, and distribution of nonhormonal iodothyronines. All but T4 and T3 (and, to a much lesser extent, 3'-T1) were almost completely and irreversibly metabolized, whereas 24-30% of the hormones (and 6% of 3'-T1) were excreted as such in feces only. Three-pool models fitted individual plasma kinetic data sets best in all cases (for all 6 iodothyronines), each with a plasma, a slowly exchanging (slow), and a rapidly exchanging (fast) pool, and kinetic parameters of interest were quantified for each iodothyronine (Ti). Quantitative analysis of an integrated 18-pool model for all 6 Tis revealed several other features of physiological interest. The fractional transport rate of T3 into the fast pool (liver, at least) is about an order of magnitude larger than that for all other Tis, supporting the hypothesis that transport of T3 into fast tissues (e.g. liver cells) is selectively amplified relative to that of the 5 other iodothyronines studied. Simultaneous and direct comparison of the 6 plasma kinetic data sets also supports this result. In addition, composite slow tissue pools, which probably exclude liver and kidney, contained the largest whole body fractions of all Tis (greater than 50%), and these also appear to be major sites of whole body T4 monodeiodinations.(ABSTRACT TRUNCATED AT 400 WORDS)

Transformation of ultrastructural type of fast-twitch muscle fibres after cross-innervation by tetrodotoxin-blocked slow muscle nerve

It is shown in rats that cross-innervation of the fast-twitch muscle extensor digitorum longus (EDL) by the nerve of the slow-twitch muscle soleus (SOL) can cause a complete transformation of the Z-band width from the fast to the slow muscle fibre type within two weeks and that it can still induce such a transformation, though usually less completely, even when impulse activity is excluded by chronical tetrodotoxin (TTX) block of the nerve. This provides a piece of clear-cut evidence for the presence of some activity-independent trophic factor in the neural determination of skeletal muscle fibre type.