Automated Long Axial Field of View PET Image Processing and Kinetic Modelling with the TurBO Toolbox Article Swipe
Jouni Tuisku
,
Santeri Palonen
,
Henri Kärpijoki
,
Aino Latva‐Rasku
,
Nelli Tuomola
,
Harri Harju
,
Sergey V. Nesterov
,
Vesa Oikonen
,
Hidehiro Iida
,
Jarmo Teuho
,
Chunlei Han
,
Tomi Karjalainen
,
Anna K. Kirjavainen
,
Johan Rajander
,
Riku Klén
,
Pirjo Nuutila
,
Lauri Nummenmaa
,
Juhani Knuuti
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.06.06.658206
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.06.06.658206
Long axial field of view (LAFOV) PET imaging requires a high level of automation and standardization, as the large number of target tissues increases the manual workload significantly. We introduce an automated analysis pipeline (TurBO, Turku total-BOdy) for preprocessing and kinetic modelling of LAFOV [ 15 O]H 2 O and [ 18 F]FDG PET data, enabling efficient and reproducible analysis of tissue perfusion and metabolism at regional and voxel-levels. The approach employs automated processing including co-registration, motion correction, automated CT segmentation for region of interest (ROI) delineation, image-derived input determination, and region-specific kinetic modelling of PET data. Methods We validated the analysis pipeline using Biograph Vision Quadra (Siemens Healthineers) LAFOV PET/CT scans from 21 subjects scanned with [ 15 O]H 2 O and 16 subjects scanned with [ 18 F]FDG using six segmented CT-based ROIs (cortical brain gray matter, left iliopsoas muscle, right kidney cortex and medulla, pancreas, spleen and liver) representing different levels of blood flow and glucose metabolism. Results Model fits showed good quality with consistent parameter estimates at both regional and voxel-levels (R² > 0.83 for [ 15 O]H 2 O, R² > 0.99 for [ 18 F]FDG). Estimates from manual and automated input functions were in concordance (R² > 0.74 for [ 15 O]H 2 O, and R² > 0.78 for [ 18 F]FDG) with minimal bias (<4% for [ 15 O]H 2 O and <10% for [ 18 F]FDG). Manually and automatically (CT-based) extracted ROI level data showed strong agreement (R² > 0.82 for [ 15 O]H 2 O and R² > 0.83 for [ 18 F]FDG), while motion correction had little impact on parameter estimates (R² > 0.71 for [ 15 O]H 2 O and R² > 0.78 for [ 18 F]FDG) compared with uncorrected data. Conclusion Our automated analysis pipeline provides reliable and reproducible parameter estimates across different regions, with an approximate processing time of 1-1.5 h per subject. This pipeline completely automates LAFOV PET analysis, reducing manual effort and enabling reproducible studies of inter-organ blood flow and metabolism, including brain-body interactions.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.06.06.658206
- https://www.biorxiv.org/content/biorxiv/early/2025/06/09/2025.06.06.658206.full.pdf
- OA Status
- green
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411149704
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4411149704Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.06.06.658206Digital Object Identifier
- Title
-
Automated Long Axial Field of View PET Image Processing and Kinetic Modelling with the TurBO ToolboxWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-09Full publication date if available
- Authors
-
Jouni Tuisku, Santeri Palonen, Henri Kärpijoki, Aino Latva‐Rasku, Nelli Tuomola, Harri Harju, Sergey V. Nesterov, Vesa Oikonen, Hidehiro Iida, Jarmo Teuho, Chunlei Han, Tomi Karjalainen, Anna K. Kirjavainen, Johan Rajander, Riku Klén, Pirjo Nuutila, Lauri Nummenmaa, Juhani KnuutiList of authors in order
- Landing page
-
https://doi.org/10.1101/2025.06.06.658206Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2025/06/09/2025.06.06.658206.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2025/06/09/2025.06.06.658206.full.pdfDirect OA link when available
- Concepts
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Toolbox, Turbo, Computer science, Image processing, Image (mathematics), Computer graphics (images), Artificial intelligence, Computer vision, Engineering, Programming language, Automotive engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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18Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4388211531, https://openalex.org/W3004573595, https://openalex.org/W2137226568, https://openalex.org/W2290321704, https://openalex.org/W4379879113, https://openalex.org/W4383218413, https://openalex.org/W2092078486, https://openalex.org/W1599030510, https://openalex.org/W4400102395, https://openalex.org/W2058046532, https://openalex.org/W4367172930, https://openalex.org/W1964381068, https://openalex.org/W3185601680, https://openalex.org/W4390692805, https://openalex.org/W3041255076, https://openalex.org/W2057657643, https://openalex.org/W4386209394, https://openalex.org/W2015687079 |
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| abstract_inverted_index.O | 49, 122, 228, 254, 280 |
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