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

Mendeley Data 2026 Dataset Open access

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Authors: Goodluck Okoro
DOI: 10.17632/8p5h4ywkbc
Cited by: 0

Abstract

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.

Fields of Study & Subjects

Medical Imaging Techniques and Applications Gaussian Processes and Bayesian Inference Statistical Methods and Bayesian Inference

Publication details

Journal / source
Mendeley Data
Publication date
24 February 2026
Language
EN
Document type
Dataset