Related Experiment Video
Updated: May 22, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Odense-Oxford PET Image Analysis (OPETIA): An FSL-based toolbox for multimodal neuroimaging
Mohammadtaha Parsayan1, Sasan Andalib1, Thomas Lund Andersen2
1Research Unit of Neurology, Department of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark; Department of Neurology, Odense University Hospital, Odense, Denmark.
Abstract:
Advanced analysis of MRI and PET images provides quantitative and accurate information about the brain structure and function, allowing differential diagnosis, prognosis, and personalized treatment. Most clinical software lack accurate quantification. Here we developed a user-friendly multimodal neuroimage analysis toolbox, named Odense-Oxford PET Image Analysis (OPETIA), based on Functional Magnetic Resonance Imaging of the Brain Software Library (FSL) and Python programming language. FSL is a strong toolbox library for MRI analysis but has not been widely used for PET image analysis. OPETIA includes a graphical user interface that facilitates automatic multimodal neuroimage analysis. OPETIA can automatically pre-process magnetic resonance, and PET images and calculates maximum, mean, and standard deviation of Standardized Uptake Value (SUV) and Standardized Uptake Value Ratio (SUVR) in the volumes of interest (VOI). To assess the efficacy of OPETIA, we analysed a set of static 18F-fluorodeoxyglucose (FDG) PET and MRIs of healthy subjects and patients with Alzheimer's disease (AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset using OPETIA and compared the SUVR measurements with those obtained from Statistical Parametric Mapping, version 12 (SPM12). The result of this comparison showed a close association between OPETIA and SPM12 results (p-value 〈 0.01, r 〉 0.8). OPETIA measurements were significantly (p-value < 0.01) larger than those of SPM12 in all brain regions (according to the Harvard-Oxford brain atlas), indicating a systematic difference between these tools. The Cronbach's Alpha values for both tools were > 0.9, indicating a high reproducibility. We compared the group difference (control vs Alzheimer's disease) obtained from each toolbox using two-sample t-test and found significantly (p-value < 0.01) larger Cohen's d values for SUVRs from OPETIA (d = 0.22) than SPM12 (d = 0.04). We suggest that OPETIA is a user-friendly and robust tool for quantitative analysis of multimodal neuroimaging such as cerebral PET and MR images.

