Optimizing PiB-PET SUVR change-over-time measurement by a large-scale analysis of longitudinal reliability, plausibility, separability, and correlation with MMSE

Christopher Schwarz, Matthew L. Senjem, Jeffrey L. Gunter, Bradley J. Kemp, Anthony J. Spychalla, Prashanthi D Vemuri, Val Lowe, Clifford R Jr. Jack, Matthew L. Senjem, Jeffrey L. Gunter, Nirubol Tosakulwong, Stephen D. Weigand, Ronald Carl Petersen

Research output: Contribution to journalArticle

35 Scopus citations


Quantitative measurements of change in β-amyloid load from Positron Emission Tomography (PET) images play a critical role in clinical trials and longitudinal observational studies of Alzheimer's disease. These measurements are strongly affected by methodological differences between implementations, including choice of reference region and use of partial volume correction, but there is a lack of consensus for an optimal method. Previous works have examined some relevant variables under varying criteria, but interactions between them prevent choosing a method via combined meta-analysis. In this work, we present a thorough comparison of methods to measure change in β-amyloid over time using Pittsburgh Compound B (PiB) PET imaging. Methods We compare 1,024 different automated software pipeline implementations with varying methodological choices according to four quality metrics calculated over three-timepoint longitudinal trajectories of 129 subjects: reliability (straightness/variance); plausibility (lack of negative slopes); ability to predict accumulator/non-accumulator status from baseline value; and correlation between change in β-amyloid and change in Mini Mental State Exam (MMSE) scores. Results and conclusion From this analysis, we show that an optimal longitudinal measure of β-amyloid from PiB should use a reference region that includes a combination of voxels in the supratentorial white matter and those in the whole cerebellum, measured using two-class partial volume correction in the voxel space of each subject's corresponding anatomical MR image.

Original languageEnglish (US)
Pages (from-to)113-127
Number of pages15
StatePublished - Jan 1 2017


ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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