Data: Cohort-level Gaussian Process Modeling for Handling Missing Data in Preclinical Longitudinal PET Imaging Studies

Published: 22 September 2026| Version 1 | DOI: 10.17632/t92tdygjfy.1
Contributors:
Goodluck Okoro,
,
,
,
,

Description

Supplementary dataset containing phantom and in vivo longitudinal PET reconstruction results used to evaluate cohort-level Gaussian process regression (cGPR) against FIML, individual GPR, and linear mixed-effects modeling under simulated missing-data conditions. The workbook includes reconstruction metrics, missingness simulations, uncertainty calibration, kernel sensitivity analyses, and statistical comparisons supporting the associated manuscript.

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Positron Emission Tomography, Biomedical Imaging, Data Analysis, Gaussian Process

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