Odense-Oxford PET Image Analysis (OPETIA): An FSL-based toolbox for multimodal neuroimaging

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 <sup>18</sup>F-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.

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Publication Details

Journal
NeuroImage
Published
2025-05-17
DOI
https://doi.org/10.1016/j.neuroimage.2025.121278
Citations
1
Primary Topic
Medical Imaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
1.36
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article

Odense-Oxford PET Image Analysis (OPETIA): An FSL-based toolbox for multimodal neuroimaging

Poul Flemming Høilund‐Carlsen, Habib Ganjgahi, Sasan Andalib, Mojtaba Zarei et al.
1 citations
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Medical Imaging Techniques and Applications
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article

Odense-Oxford PET Image Analysis (OPETIA): An FSL-based toolbox for multimodal neuroimaging

Poul Flemming Høilund‐Carlsen, Habib Ganjgahi, Sasan Andalib, Mojtaba Zarei, Abass A. Alavi, Thomas Lund Andersen, Mohammadtaha Parsayan
article en
1 citations

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 <sup>18</sup>F-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.

NeuroImageVol. 314
University of Southern Denmark (DK), Hospital of the University of Pennsylvania (US), Odense University Hospital (DK), Rigshospitalet (DK), University of Oxford (GB)
Openalex Percentile: Top 20%
Medical Imaging Techniques and Applications
1.36
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