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

Jieqing Jiao1,2, Fiona Heeman3, Rachael Dixon4

  • 1Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK. jieqing.jiao@gmail.com.

Neuroinformatics
|January 9, 2023
PubMed
Summary
This summary is machine-generated.

NiftyPAD is a new open-source software for processing dynamic brain PET data, offering fast and reproducible analyses. It shows comparable results to existing tools and introduces novel methods for dual-time window scans and motion correction.

Keywords:
NiftyPADPETPharmacokinetic analysisPython packageReference input-based modelling

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Area of Science:

  • Neuroimaging
  • Radiochemistry
  • Computational Biology

Background:

  • Positron Emission Tomography (PET) datasets are growing, necessitating efficient processing.
  • Existing software may lack versatility for advanced dynamic PET analyses.

Purpose of the Study:

  • Introduce NiftyPAD, an open-source Python package for dynamic brain PET data analysis.
  • Provide versatile and reproducible processing pipelines for static and dynamic PET scans.

Main Methods:

  • Developed NiftyPAD, incorporating dual-time window scan analysis, reference input processing, and ASL-derived perfusion measures.
  • Integrated optional PET data-based motion correction.
  • Validated against established software (PPET, QModeling) using clinical data from eight subjects and four amyloid tracers.

Main Results:

  • NiftyPAD demonstrated high correlation and low absolute differences compared to PPET and QModeling for various kinetic models.
  • Accurate implementation of the SRTM ASL method was confirmed, showing high correlation and negligible bias with full scan SRTM.
  • Computational performance was validated on clinical data.

Conclusions:

  • NiftyPAD is a versatile, flexible, and lightweight software package for dynamic PET data quantification.
  • It produces results comparable to established software and facilitates integration into existing pipelines.
  • The open-source nature and modular design promote ease of use and future development.