Partial volume correction incorporating Rb-82 positron range for quantitative myocardial perfusion PET based on systolic-diastolic activity ratios and phantom measurements.

BACKGROUND: Quantitative myocardial PET perfusion imaging requires partial volume corrections. METHODS: Patients underwent ECG-gated, rest-dipyridamole, myocardial perfusion PET using Rb-82 decay corrected in Bq/cc for diastolic, systolic, and combined whole cycle ungated images. Diastolic partial volume correction relative to systole was determined from the systolic/diastolic activity ratio, systolic partial volume correction from phantom dimensions comparable to systolic LV wall thicknesses and whole heart cycle partial volume correction for ungated images from fractional systolic-diastolic duration for systolic and diastolic partial volume corrections. RESULTS: For 264 PET perfusion images from 159 patients (105 rest-stress image pairs, 54 individual rest or stress images), average resting diastolic partial volume correction relative to systole was 1.14 ± 0.04, independent of heart rate and within ±1.8% of stress images (1.16 ± 0.04). Diastolic partial volume corrections combined with those for phantom dimensions comparable to systolic LV wall thickness gave an average whole heart cycle partial volume correction for ungated images of 1.23 for Rb-82 compared to 1.14 if positron range were negligible as for F-18. CONCLUSION: Quantitative myocardial PET perfusion imaging requires partial volume correction, herein demonstrated clinically from systolic/diastolic absolute activity ratios combined with phantom data accounting for Rb-82 positron range.

$\beta\sb2$-adrenergic receptor desensitization, internalization and phosphorylation in response to full and partial agonists

The objective of this study is to test the hypothesis that partial agonists produce less desensitization because they generate less of the active conformation of the $\beta\sb2$-adrenergic receptor ($\beta$AR) (R*) and in turn cause less $\beta$AR phosphorylation by beta adrenergic receptor kinase ($\beta$ARK) and less $\beta$AR internalization. In the present work, rates of desensitization, internalization, and phosphorylation caused by a series of $\beta$AR agonists were correlated with a quantitative measure, defined as coupling efficiency, of agonist-dependent $\beta$AR activation of adenylyl cyclase. These studies were preformed in HEK-293 cells overexpressing the $\beta$AR with hemagglutinin (HA) and 6-histidine (6HIS) epitopes introduced into the N- and C-termini respectively. Agonists chosen provided a 95-fold range of coupling efficiencies, and, relative to epinephrine, the best agonist, (100%) were fenoterol (42%), albuterol (4.9%), dobutamine (2.5%) and ephedrine (1.1%). At concentrations of these agonists yielding $>$90% receptor occupancy, the rate and extent of the rapid phase (0-30 min) of agonist induced desensitization of adenylyl cyclase followed the same order as coupling efficiency, that is, epinephrine $\ge$ fitnoterol $>$ albuterol $>$ dobutamine $>$ ephedrine. The rate of internalization, measured by a loss of surface receptors during desensitization, with respect to these agonists also followed the same order as the desensitization and exhibited a slight lag. Like desensitization and internalization, $\beta$AR phosphorylation exhibited a dependency on agonist strength. The two strongest agonists epinephrine and fenoterol provoked 11 to 13 fold increases in the level of $\beta$AR phosphorylation after just 1 min, whereas the weakest agonists dobutamine and ephedrine caused only 3 to 4 fold increases in phosphorylation. With longer treatment times, the level of $\beta$AR phosphorylation declined with the strong agonists, but progressively increased with the weaker partial agonists. The major conclusion drawn from this study is that the occupancy-dependent rate of receptor phosphorylation increases with agonist coupling efficiencies and that this is sufficient to explain the desensitization, internalization, and phosphorylation data obtained.^ The mechanism of activation and desensitization by the partial $\beta$AR agonist salmeterol was also examined in this study. This drug is extremely hydrophobic and its study presents possibly unique problems. To determine whether salmeterol induces desensitization of the $\beta$AR its action has been studied using our system. Employing the use of reversible antagonists it was found that salmeterol, which has an estimated coupling efficiency near that of albuterol caused $\beta$AR desensitization. This desensitization was much reduced relative to epinephrine. Consistent with its coupling efficiency, it was found to be similar to albuterol in its ability to induce internalization and phosphorylation of the $\beta$AR. (Abstract shortened by UMI.) ^

A nonparametric method of comparing the partial areas under two correlated receiver operating characteristic curves

A non-parametric method was developed and tested to compare the partial areas under two correlated Receiver Operating Characteristic curves. Based on the theory of generalized U-statistics the mathematical formulas have been derived for computing ROC area, and the variance and covariance between the portions of two ROC curves. A practical SAS application also has been developed to facilitate the calculations. The accuracy of the non-parametric method was evaluated by comparing it to other methods. By applying our method to the data from a published ROC analysis of CT image, our results are very close to theirs. A hypothetical example was used to demonstrate the effects of two crossed ROC curves. The two ROC areas are the same. However each portion of the area between two ROC curves were found to be significantly different by the partial ROC curve analysis. For computation of ROC curves with large scales, such as a logistic regression model, we applied our method to the breast cancer study with Medicare claims data. It yielded the same ROC area computation as the SAS Logistic procedure. Our method also provides an alternative to the global summary of ROC area comparison by directly comparing the true-positive rates for two regression models and by determining the range of false-positive values where the models differ. ^