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Statistical evaluation of test-retest studies in PET brain imaging

Baumgartner, Richard; Joshi, Aniket; Feng, Dai; Zanderigo, Francesca; Ogden, Robert T.

Background: 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.

Results: 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.

Conclusions: 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.


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EJNMMI Research

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Published Here
February 23, 2018