Abstract
Positron emission tomography (PET) is a molecular imaging technology that enables in vivo quantification of metabolic activity or receptor density, among other applications. Examples of applications of PET imaging in neuroscience include studies of neuroreceptor/neurotransmitter levels in neuropsychiatric diseases (e.g., measuring receptor expression in schizophrenia) and of misfolded protein levels in neurodegenerative diseases (e.g., beta amyloid and tau deposits in Alzheimer’s disease). Assessment of a PET tracer’s test-retest properties is an important component of tracer validation, and it is usually carried out using data from a small number of subjects. Here, we investigate advantages and limitations of test-retest metrics that are commonly used for PET brain imaging, including percent test-retest difference and intraclass correlation coefficient (ICC). In addition, we show how random effects analysis of variance, which forms the basis for ICC, can be used to derive additional test-retest metrics, which are generally not reported in the PET brain imaging test-retest literature, such as within-subject coefficient of variation and repeatability coefficient. We reevaluate data from five published clinical PET imaging test-retest studies to illustrate the relative merits and utility of the various test-retest metrics. We provide recommendations on evaluation of test-retest in brain PET imaging and show how the random effects ANOVA based metrics can be used to supplement the commonly used metrics such as percent test-retest. Random effects ANOVA is a useful model for PET brain imaging test-retest studies. The metrics that ensue from this model are recommended to be reported along with the percent test-retest metric as they capture various sources of variability in the PET test-retest experiments in a succinct way.
Highlights
Positron emission tomography (PET) is a molecular imaging technology that enables in vivo quantification of metabolic activity or receptor density, among other applications
To demonstrate how various ROIs are performing across different test-retest metrics, they are plotted in the same color across datasets and fitting methods
The random effects ANOVA model underpins the rationale for most metrics and we found it to be a useful model for brain PET imaging, as it describes and quantifies the test-retest PET experiments in a succinct way, while at the same time capturing various random variations present in the data
Summary
Positron emission tomography (PET) is a molecular imaging technology that enables in vivo quantification of metabolic activity or receptor density, among other applications. Assessment of a PET tracer’s test-retest properties is an important component of tracer validation, and it is usually carried out using data from a small number of subjects. Positron emission tomography (PET) is a molecular imaging technology used for in vivo measurement of metabolism and neurochemistry, including measurement of cerebral blood flow, glucose metabolism, oxygen utilization, and density of neuroreceptors or other molecular targets [1, 2]. As we will summarize here, most of the indices used to summarize the results of test-retest experiments measure quantities that are important for such experiments Note, that these indices by themselves do not provide all the useful information when considering other types of PET studies, i.e., a cross-sectional study of two groups of subjects
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