NiftyPAD - Novel Python Package for Quantitative Analysis of Dynamic PET Data
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 dat...
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| Vydané v: | Neuroinformatics (Totowa, N.J.) Ročník 21; číslo 2; s. 457 - 468 |
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| Hlavní autori: | , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
Springer US
01.04.2023
Springer Nature B.V |
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| ISSN: | 1539-2791, 1559-0089, 1559-0089 |
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| Abstract | 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
R
2
>
0.999
correlation with PPET, with absolute difference
∼
10
-
2
for linearised Logan and MRTM2 methods, and
R
2
>
0.999999
correlation with QModeling, with absolute difference
∼
10
-
4
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 (
R
2
=
0.96
), 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. |
|---|---|
| AbstractList | 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
R
2
>
0.999
correlation with PPET, with absolute difference
∼
10
-
2
for linearised Logan and MRTM2 methods, and
R
2
>
0.999999
correlation with QModeling, with absolute difference
∼
10
-
4
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 (
R
2
=
0.96
), 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. 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 R2>0.999 correlation with PPET, with absolute difference ∼10-2 for linearised Logan and MRTM2 methods, and R2>0.999999 correlation with QModeling, with absolute difference ∼10-4 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 (R2=0.96), 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. 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 $$R^2>0.999$$ R2>0.999 correlation with PPET, with absolute difference $$\sim 10^{-2}$$ ∼10-2 for linearised Logan and MRTM2 methods, and $$R^2>0.999999$$ R2>0.999999 correlation with QModeling, with absolute difference $$\sim 10^{-4}$$ ∼10-4 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 ( $$R^2=0.96$$ R2=0.96), 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. 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 $$R^2>0.999$$ R 2 > 0.999 correlation with PPET, with absolute difference $$\sim 10^{-2}$$ ∼ 10 - 2 for linearised Logan and MRTM2 methods, and $$R^2>0.999999$$ R 2 > 0.999999 correlation with QModeling, with absolute difference $$\sim 10^{-4}$$ ∼ 10 - 4 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 ( $$R^2=0.96$$ R 2 = 0.96 ), 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. 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. 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.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. |
| Author | Heeman, Fiona Wimberley, Catriona Cardoso, M Jorge van Berckel, Bart N. M. Cash, David M. Ourselin, Sebastién Yaqub, Maqsood Gispert, Juan Domingo Lopes Alves, Isadora Lammertsma, Adriaan A. Jiao, Jieqing Markiewicz, Pawel Dixon, Rachael Barkhof, Frederik da Costa-Luis, Casper |
| Author_xml | – sequence: 1 givenname: Jieqing surname: Jiao fullname: Jiao, Jieqing email: jieqing.jiao@gmail.com organization: Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, School of Biomedical Engineering and Imaging Sciences, King’s College London – sequence: 2 givenname: Fiona surname: Heeman fullname: Heeman, Fiona organization: Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam – sequence: 3 givenname: Rachael surname: Dixon fullname: Dixon, Rachael organization: Edinburgh Imaging, Queen’s Medical Research Institute, University of Edinburgh – sequence: 4 givenname: Catriona surname: Wimberley fullname: Wimberley, Catriona organization: Edinburgh Imaging, Queen’s Medical Research Institute, University of Edinburgh – sequence: 5 givenname: Isadora surname: Lopes Alves fullname: Lopes Alves, Isadora organization: Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam – sequence: 6 givenname: Juan Domingo surname: Gispert fullname: Gispert, Juan Domingo organization: BarcelonaBeta Brain Research Centre, Pasqual Maragall Foundation – sequence: 7 givenname: Adriaan A. surname: Lammertsma fullname: Lammertsma, Adriaan A. organization: Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam – sequence: 8 givenname: Bart N. M. surname: van Berckel fullname: van Berckel, Bart N. M. organization: Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam – sequence: 9 givenname: Casper surname: da Costa-Luis fullname: da Costa-Luis, Casper organization: Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, School of Biomedical Engineering and Imaging Sciences, King’s College London – sequence: 10 givenname: Pawel surname: Markiewicz fullname: Markiewicz, Pawel organization: Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London – sequence: 11 givenname: David M. surname: Cash fullname: Cash, David M. organization: Dementia Research Centre, Queen Square Institute of Neurology, University College London – sequence: 12 givenname: M Jorge surname: Cardoso fullname: Cardoso, M Jorge organization: School of Biomedical Engineering and Imaging Sciences, King’s College London – sequence: 13 givenname: Sebastién surname: Ourselin fullname: Ourselin, Sebastién organization: School of Biomedical Engineering and Imaging Sciences, King’s College London – sequence: 14 givenname: Maqsood surname: Yaqub fullname: Yaqub, Maqsood organization: Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam – sequence: 15 givenname: Frederik surname: Barkhof fullname: Barkhof, Frederik organization: Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36622500$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/0169-2607(94)90201-1 10.2967/jnumed.117.200964 10.1212/WNL.0000000000009216 10.1006/nimg.1997.0303 10.1016/j.neuroimage.2012.01.021 10.2967/jnumed.116.173013 10.1007/s11307-019-01387-6 10.1007/s00259-009-1129-6 10.2967/jnumed.116.188029 10.1177/0271678X20915403 10.1007/s12021-018-9384-y 10.1093/geroni/igy023.3277 10.1007/s00259-017-3750-0 10.1177/0271678X18783628 10.1186/s13550-020-00714-1 10.1097/01.WCB.0000085441.37552.CA 10.1097/00004647-199609000-00008 10.1016/j.neuroimage.2013.08.031 10.1016/j.neuroimage.2006.04.053 10.3389/fninf.2020.00003 10.1177/0271678X18797343 10.1007/s00259-012-2102-3 10.3389/fninf.2018.00064 10.1088/0031-9155/51/17/007 10.2967/jnumed.112.113654 10.1016/j.nbd.2014.05.001 10.1186/s13550-019-0499-4 10.1097/01.WCB.0000033967.83623.34 10.1006/nimg.1996.0066 10.1101/755751 10.1097/RLU.0000000000002768 10.1002/alz.12069 |
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| Copyright_xml | – notice: The Author(s) 2023. corrected publication 2023 – notice: 2023. The Author(s). – notice: The Author(s) 2023. corrected publication 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s) 2023, corrected publication 2023 |
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| Keywords | NiftyPAD Reference input-based modelling Python package Pharmacokinetic analysis PET |
| Language | English |
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| SubjectTerms | Amyloid Bioinformatics Biomedical and Life Sciences Biomedicine Brain - diagnostic imaging Computational Biology/Bioinformatics Computational neuroscience Computer Appl. in Life Sciences Humans Neurology Neurosciences Pharmacokinetics Positron-Emission Tomography - methods Quantitative analysis Software Software packages Spin labeling |
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| Title | NiftyPAD - Novel Python Package for Quantitative Analysis of Dynamic PET Data |
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