Evidence map›Paper›PMID 36622500›Full record

ArticleNeuroinformatics2023

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

Jieqing Jiao, Fiona Heeman, Rachael Dixon, Catriona Wimberley, Isadora Lopes Alves, Juan Domingo Gispert, Adriaan A Lammertsma, Bart N M van Berckel, Casper da Costa-Luis, Pawel Markiewicz and 5 more

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In one paragraph

Article in Neuroinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Feasibility of shortening theQuantitative imaging in medicine and surgery · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Jieqing JiaoCentre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK. jieqing.jiao@gmail.com.
Fiona HeemanRadiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, Netherlands.
Rachael DixonEdinburgh Imaging, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK.
Catriona WimberleyEdinburgh Imaging, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK.
Isadora Lopes AlvesRadiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, Netherlands.
Juan Domingo GispertBarcelonaBeta Brain Research Centre, Pasqual Maragall Foundation, Barcelona, Spain.
Adriaan A LammertsmaRadiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, Netherlands.
Bart N M van BerckelRadiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, Netherlands.
Casper da Costa-LuisCentre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
Pawel MarkiewiczCentre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
David M CashDementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK.
M Jorge CardosoSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Sebastién OurselinSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Maqsood YaqubRadiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, Netherlands.
Frederik BarkhofCentre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 [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.

Indexed as

BrainPositron-Emission TomographyHumansNiftyPADPETPharmacokinetic analysisPython packageReference input-based modelling

Identifiers

PMID36622500
PMCPMC10085912

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.