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 neu...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811925002812 |
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| author | Mohammadtaha Parsayan Sasan Andalib Thomas Lund Andersen Habib Ganjgahi Poul Flemming Høilund-Carlsen Abass Alavi Mojtaba Zarei |
| author_facet | Mohammadtaha Parsayan Sasan Andalib Thomas Lund Andersen Habib Ganjgahi Poul Flemming Høilund-Carlsen Abass Alavi Mojtaba Zarei |
| author_sort | Mohammadtaha Parsayan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c8d26b6d3520456e840ab73e2e49139d |
| institution | DOAJ |
| issn | 1095-9572 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj-art-c8d26b6d3520456e840ab73e2e49139d2025-08-20T03:08:54ZengElsevierNeuroImage1095-95722025-07-0131412127810.1016/j.neuroimage.2025.121278Odense-Oxford PET Image Analysis (OPETIA): An FSL-based toolbox for multimodal neuroimagingMohammadtaha Parsayan0Sasan Andalib1Thomas Lund Andersen2Habib Ganjgahi3Poul Flemming Høilund-Carlsen4Abass Alavi5Mojtaba Zarei6Research Unit of Neurology, Department of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark; Department of Neurology, Odense University Hospital, Odense, DenmarkResearch Unit of Neurology, Department of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark; Department of Neurology, Odense University Hospital, Odense, DenmarkDepartment of Clinical Physiology & Nuclear Medicine, Rigshospitalet, Copenhagen, DenmarkDepartment of Statistics, University of Oxford, Oxford, UKResearch Unit of Clinical Physiology and Nuclear Medicine, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark; Department of Nuclear Medicine, Odense University Hospital, Odense, DenmarkDepartment of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USAResearch 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; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S1053811925002812OPETIAPETMRIImage analysisFSLImage processing |
| spellingShingle | Mohammadtaha Parsayan Sasan Andalib Thomas Lund Andersen Habib Ganjgahi Poul Flemming Høilund-Carlsen Abass Alavi Mojtaba Zarei Odense-Oxford PET Image Analysis (OPETIA): An FSL-based toolbox for multimodal neuroimaging NeuroImage OPETIA PET MRI Image analysis FSL Image processing |
| title | Odense-Oxford PET Image Analysis (OPETIA): An FSL-based toolbox for multimodal neuroimaging |
| title_full | Odense-Oxford PET Image Analysis (OPETIA): An FSL-based toolbox for multimodal neuroimaging |
| title_fullStr | Odense-Oxford PET Image Analysis (OPETIA): An FSL-based toolbox for multimodal neuroimaging |
| title_full_unstemmed | Odense-Oxford PET Image Analysis (OPETIA): An FSL-based toolbox for multimodal neuroimaging |
| title_short | Odense-Oxford PET Image Analysis (OPETIA): An FSL-based toolbox for multimodal neuroimaging |
| title_sort | odense oxford pet image analysis opetia an fsl based toolbox for multimodal neuroimaging |
| topic | OPETIA PET MRI Image analysis FSL Image processing |
| url | http://www.sciencedirect.com/science/article/pii/S1053811925002812 |
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