Statistical evaluation of test-retest studies in PET brain imaging

• Published in: EJNMMI research Vol. 8; no. 1; pp. 13 - 9
• Main Authors: Baumgartner, Richard, Joshi, Aniket, Feng, Dai, Zanderigo, Francesca, Ogden, R. Todd
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 12.02.2018; Springer Nature B.V; SpringerOpen
• Subjects: Brain; Cardiac Imaging; Coefficient of variation; Correlation coefficients; Emission analysis; Imaging; In vivo methods and tests; Medical imaging; Medicine; Medicine & Public Health; Neurological diseases; Nuclear Medicine; Oncology; Original Research; Orthopedics; Positron emission; Proteins; Radiology; Schizophrenia; Tomography; Variance analysis; Molecular Imaging Technologies; Repeatability Coefficient (RC); Test-retest Experiment; Within-subject Coefficient Of Variation (WSCV); Random Effects ANOVA Model
• ISSN: 2191-219X; 2191-219X
• DOI: 10.1186/s13550-018-0366-8

BackgroundPositron 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.ResultsHere, 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.ConclusionsRandom 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.