Reference tissue modeling with parameter coupling: application to a study of SERT binding in XXXX .

@entity1 @entity303 @entity645 @entity11702 @entity25435 @entity750
When applicable, it is generally preferred to evaluate positron emission tomography (PET) studies using a reference tissue-based approach as that avoids the need for invasive arterial blood sampling. However, most reference tissue methods have been shown to have a bias that is dependent on the level of tracer binding, and the variability of parameter estimates may be substantially affected by noise level. In a study of @entity750 transporter (SERT) binding in @entity645 @entity303 , it was determined that applying parameter coupling to the simplified reference tissue model (SRTM) reduced the variability of parameter estimates and yielded the strongest between-group significant differences in SERT binding. The use of parameter coupling makes the application of SRTM more consistent with conventional blood input models and reduces the total number of fitted parameters, thus should yield more robust parameter estimates. Here, we provide a detailed evaluation of the application of parameter constraint and parameter coupling to @entity25435 PET studies. Five quantitative methods, including three methods that constrain the reference tissue clearance (k(r)(2)) to a common value across regions were applied to the clinical and simulated data to compare measurement of the tracer binding potential (BP( @entity11702 )). Compared with standard SRTM, either coupling of k(r)(2) across regions or constraining k(r)(2) to a first-pass estimate improved the sensitivity of SRTM to measuring a significant difference in BP( @entity11702 ) between @entity1 and controls. Parameter coupling was particularly effective in reducing the variance of parameter estimates, which was less than 50% of the variance obtained with standard SRTM. A linear approach was also improved when constraining k(r)(2) to a first-pass estimate, although the SRTM-based methods yielded stronger significant differences when applied to the clinical study. This work shows that parameter coupling reduces the variance of parameter estimates and may better discriminate between-group differences in specific binding.
