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

Published: 24 February 2026| Version 1 | DOI: 10.17632/8p5h4ywkbc.1
Contributor:
Goodluck Okoro

Description

This dataset contains raw longitudinal PET standardized uptake value (SUV) measurements from the described phantom and tumor studies, along with Python scripts for cohort-level Gaussian Process Regression (cGPR) and missing scan prediction analysis. The data and code support probabilistic reconstruction of cohort-level PET trajectories under irregular sampling and missing observations, enabling evaluation of uncertainty-aware longitudinal modeling in preclinical PET imaging.

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Positron Emission Tomography, Biomedical Imaging, Data Analysis, Biostatistics

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