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Lassen's equation is a good approximation of permeability-surface model: new α values for ⁹⁹mTc-HMPAO and ⁹⁹mTc-ECD.

ABSTRACT: Brain perfusion tracers like [(99m)Tc] d,l-hexamethyl-propyeneamine oxime ((99m)Tc-HMPAO) and [(99m)Tc] ethyl-cysteinate dimer ((99m)Tc-ECD) underestimate regional cerebral blood flow (rCBF) at high flow values. To improve linearity between tracer accumulation and rCBF, two different models have been proposed. One is Lassen's correction algorithm for back-diffusion of tracer, and the other is based on the permeability-surface (PS) model for correction of low first-pass extraction. Although both these models have the same goal, they have completely different forms of equation. It was demonstrated that mathematical approximation of the PS model equation leads to Lassen's equation. In this process, the relationship between PS, CBF values and Lassen's parameter was acquired, and how to correct both the back-diffusion and low first-pass extraction was also demonstrated. A computer simulation confirmed that the two models provided similar consequences when the parameter value is chosen according to the relationship found. Lassen's equation can be used to correct not only back-diffusion but also low first-pass extraction. To perform overall correction, the parameter value we have been using for decades may be too weak. I estimated that the parameter value for overall correction of HMPAO would be around 0.5, and that of ECD would be around 0.65.

SUBMITTER: Kameyama M

PROVIDER: S-EPMC4083378 | biostudies-other | 2014 Jul

REPOSITORIES: biostudies-other

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Project description:BackgroundGenomic selection is an effective tool for animal and plant breeding, allowing effective individual selection without phenotypic records through the prediction of genomic breeding value (GBV). To date, genomic selection has focused on a single trait. However, actual breeding often targets multiple correlated traits, and, therefore, joint analysis taking into consideration the correlation between traits, which might result in more accurate GBV prediction than analyzing each trait separately, is suitable for multi-trait genomic selection. This would require an extension of the prediction model for single-trait GBV to multi-trait case. As the computational burden of multi-trait analysis is even higher than that of single-trait analysis, an effective computational method for constructing a multi-trait prediction model is also needed.ResultsWe described a Bayesian regression model incorporating variable selection for jointly predicting GBVs of multiple traits and devised both an MCMC iteration and variational approximation for Bayesian estimation of parameters in this multi-trait model. The proposed Bayesian procedures with MCMC iteration and variational approximation were referred to as MCBayes and varBayes, respectively. Using simulated datasets of SNP genotypes and phenotypes for three traits with high and low heritabilities, we compared the accuracy in predicting GBVs between multi-trait and single-trait analyses as well as between MCBayes and varBayes. The results showed that, compared to single-trait analysis, multi-trait analysis enabled much more accurate GBV prediction for low-heritability traits correlated with high-heritability traits, by utilizing the correlation structure between traits, while the prediction accuracy for uncorrelated low-heritability traits was comparable or less with multi-trait analysis in comparison with single-trait analysis depending on the setting for prior probability that a SNP has zero effect. Although the prediction accuracy with varBayes was generally lower than with MCBayes, the loss in accuracy was slight. The computational time was greatly reduced with varBayes.ConclusionsIn genomic selection for multiple correlated traits, multi-trait analysis was more beneficial than single-trait analysis and varBayes was much advantageous over MCBayes in computational time, which would outweigh the loss of prediction accuracy caused by the approximation procedure, and is thus considered a practical method of choice.

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Lassen's equation is a good approximation of permeability-surface model: new α values for ⁹⁹mTc-HMPAO and ⁹⁹mTc-ECD.

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