TY - JOUR
T1 - NiftyPAD - Novel Python Package for Quantitative Analysis of Dynamic PET Data
AU - Jiao, Jieqing
AU - Heeman, Fiona
AU - Dixon, Rachael
AU - Wimberley, Catriona
AU - Alves, Isadora Lopes
AU - Gispert, Juan Domingo
AU - Lammertsma, Adriaan A.
AU - van Berckel, Bart N. M.
AU - da Costa-Luis, Casper
AU - Markiewicz, Pawel
AU - Cash, David M.
AU - Cardoso, M. Jorge
AU - Ourselin, Sebastién
AU - Yaqub, Maqsood
AU - Barkhof, Frederik
N1 - Funding Information: This work is supported by EU-EFPIA Innovative Medicines Initiative (IMI) Joint Undertaking (EMIF grant 115372) and the EU-EFPIA Innovative Medicines Initiatives 2 Joint Undertaking (grant No 115952). Funding Information: We thank Ronald Boellaard for the support with the PPET software. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: this work has received support from the EU-EFPIA Innovative Medicines Initiative (IMI) Joint Undertaking (EMIF grant 115372) and the EU-EFPIA Innovative Medicines Initiatives 2 Joint Undertaking (grant No 115952). This joint undertaking receives support from the European Union’s Horizon 2020 research and innovation program and EFPIA. FB is supported by the NIHR biomedical research centre at UCLH. This communication reflects the views of the authors and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. Funding Information: We thank Ronald Boellaard for the support with the PPET software. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: this work has received support from the EU-EFPIA Innovative Medicines Initiative (IMI) Joint Undertaking (EMIF grant 115372) and the EU-EFPIA Innovative Medicines Initiatives 2 Joint Undertaking (grant No 115952). This joint undertaking receives support from the European Union’s Horizon 2020 research and innovation program and EFPIA. FB is supported by the NIHR biomedical research centre at UCLH. This communication reflects the views of the authors and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. Publisher Copyright: © 2023, The Author(s).
PY - 2023/4
Y1 - 2023/4
N2 - 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.
AB - 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.
KW - NiftyPAD
KW - PET
KW - Pharmacokinetic analysis
KW - Python package
KW - Reference input-based modelling
UR - http://www.scopus.com/inward/record.url?scp=85145913047&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/s12021-022-09616-0
DO - https://doi.org/10.1007/s12021-022-09616-0
M3 - Article
C2 - 36622500
SN - 1539-2791
VL - 21
SP - 457
EP - 468
JO - Neuroinformatics
JF - Neuroinformatics
IS - 2
ER -