NiftyPAD - Novel Python Package for Quantitative Analysis of Dynamic PET Data.

NiftyPAD PET Pharmacokinetic analysis Python package Reference input-based modelling

Journal

Neuroinformatics
ISSN: 1559-0089
Titre abrégé: Neuroinformatics
Pays: United States
ID NLM: 101142069

Informations de publication

Date de publication:
04 2023
Historique:
accepted: 30 11 2022
medline: 12 4 2023
pubmed: 10 1 2023
entrez: 9 1 2023
Statut: ppublish

Résumé

Current PET datasets are becoming larger, thereby increasing the demand for fast and reproducible processing pipelines. This paper presents a freely available, open source, Python-based software package called NiftyPAD, for versatile analyses of static, full or dual-time window dynamic brain PET data. The key novelties of NiftyPAD are the analyses of dual-time window scans with reference input processing, pharmacokinetic modelling with shortened PET acquisitions through the incorporation of arterial spin labelling (ASL)-derived relative perfusion measures, as well as optional PET data-based motion correction. Results obtained with NiftyPAD were compared with the well-established software packages PPET and QModeling for a range of kinetic models. Clinical data from eight subjects scanned with four different amyloid tracers were used to validate the computational performance. NiftyPAD achieved [Formula: see text] correlation with PPET, with absolute difference [Formula: see text] for linearised Logan and MRTM2 methods, and [Formula: see text] correlation with QModeling, with absolute difference [Formula: see text] for basis function based SRTM and SRTM2 models. For the recently published SRTM ASL method, which is unavailable in existing software packages, high correlations with negligible bias were observed with the full scan SRTM in terms of non-displaceable binding potential ([Formula: see text]), indicating reliable model implementation in NiftyPAD. Together, these findings illustrate that NiftyPAD is versatile, flexible, and produces comparable results with established software packages for quantification of dynamic PET data. It is freely available ( https://github.com/AMYPAD/NiftyPAD ), and allows for multi-platform usage. The modular setup makes adding new functionalities easy, and the package is lightweight with minimal dependencies, making it easy to use and integrate into existing processing pipelines.

Identifiants

pubmed: 36622500
doi: 10.1007/s12021-022-09616-0
pii: 10.1007/s12021-022-09616-0
pmc: PMC10085912
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

457-468

Informations de copyright

© 2023. The Author(s).

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Auteurs

Jieqing Jiao (J)

Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK. jieqing.jiao@gmail.com.
School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK. jieqing.jiao@gmail.com.

Fiona Heeman (F)

Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, Netherlands.

Rachael Dixon (R)

Edinburgh Imaging, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK.

Catriona Wimberley (C)

Edinburgh Imaging, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK.

Isadora Lopes Alves (I)

Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, Netherlands.

Juan Domingo Gispert (JD)

BarcelonaBeta Brain Research Centre, Pasqual Maragall Foundation, Barcelona, Spain.

Adriaan A Lammertsma (AA)

Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, Netherlands.

Bart N M van Berckel (BNM)

Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, Netherlands.

Casper da Costa-Luis (C)

Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.

Pawel Markiewicz (P)

Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.

David M Cash (DM)

Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK.

M Jorge Cardoso (MJ)

School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.

Sebastién Ourselin (S)

School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.

Maqsood Yaqub (M)

Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, Netherlands.

Frederik Barkhof (F)

Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, Netherlands.

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Classifications MeSH